From cdd7d524d9da8645eff871ec980f8bbf5bcb7efa Mon Sep 17 00:00:00 2001 From: Stephen Cini <114932899+sscini@users.noreply.github.com> Date: Wed, 23 Apr 2025 11:50:25 -0400 Subject: [PATCH 01/23] Work on multistart implement 4/23 morning --- pyomo/contrib/parmest/parmest.py | 103 +++++++++++++++++++++++++++++++ 1 file changed, 103 insertions(+) diff --git a/pyomo/contrib/parmest/parmest.py b/pyomo/contrib/parmest/parmest.py index ea9dfc00640..7c5cc618b83 100644 --- a/pyomo/contrib/parmest/parmest.py +++ b/pyomo/contrib/parmest/parmest.py @@ -235,6 +235,9 @@ def SSE(model): return expr +'''Adding pseudocode for draft implementation of the estimator class, +incorporating multistart. +''' class Estimator(object): """ Parameter estimation class @@ -273,8 +276,18 @@ def __init__( tee=False, diagnostic_mode=False, solver_options=None, + # Add the extra arguments needed for running the multistart implement + # _validate_multistart_args: + # if n_restarts > 1 and theta_samplig_method is not None: + # n_restarts=1, + # theta_sampling_method="random", ): + '''first theta would be provided by the user in the initialization of + the Estimator class through the unknown parameter variables. Additional + would need to be generated using the sampling method provided by the user. + ''' + # check that we have a (non-empty) list of experiments assert isinstance(experiment_list, list) self.exp_list = experiment_list @@ -447,6 +460,19 @@ def TotalCost_rule(model): parmest_model = utils.convert_params_to_vars(model, theta_names, fix_vars=False) return parmest_model + + # Make new private method, _generalize_initial_theta: + # This method will be used to generalize the initial theta values for multistart + # optimization. It will take the theta names and the initial theta values + # and return a dictionary of theta names and their corresponding values. + # def _generalize_initial_theta(self, theta_names, initial_theta): + # if self.method == "random": + # # Generate random theta values + # theta_vals = np.random.uniform(lower_bound, upper_bound, size=len(theta_names)) + + # elif self.method == "latin_hypercube": + # # Generate theta values using Latin hypercube sampling + # theta_vals = scipy.statsqmc.LatinHypercube(n=len(theta_names)).rvs(size=self.n_restarts) def _instance_creation_callback(self, experiment_number=None, cb_data=None): model = self._create_parmest_model(experiment_number) @@ -921,6 +947,83 @@ def theta_est( cov_n=cov_n, ) + ''' + def theta_est_multistart( + self, + n_restarts=1, + theta_sampling_method="random", + solver="ef_ipopt", + return_values=[], + calc_cov=False, + cov_n=None, + ): + """ + Parameter estimation using multistart optimization + + Parameters + ---------- + n_restarts: int, optional + Number of restarts for multistart. Default is 1. + theta_sampling_method: string, optional + Method used to sample theta values. Options are "random", "latin_hypercube", or "sobol". + Default is "random". + solver: string, optional + Currently only "ef_ipopt" is supported. Default is "ef_ipopt". + return_values: list, optional + List of Variable names, used to return values from the model for data reconciliation + calc_cov: boolean, optional + If True, calculate and return the covariance matrix (only for "ef_ipopt" solver). + Default is False. + cov_n: int, optional + If calc_cov=True, then the user needs to supply the number of datapoints + that are used in the objective function. + + Returns + ------- + objectiveval: float + The objective function value + thetavals: pd.Series + Estimated values for theta + variable values: pd.DataFrame + Variable values for each variable name in return_values (only for solver='ef_ipopt') + cov: pd.DataFrame + Covariance matrix of the fitted parameters (only for solver='ef_ipopt') + """ + + # check if we are using deprecated parmest + if self.pest_deprecated is not None: + return print( + "Multistart is not supported in the deprecated parmest interface") + ) + + assert isinstance(n_restarts, int) + assert isinstance(theta_sampling_method, str) + assert isinstance(solver, str) + assert isinstance(return_values, list) + assert isinstance(calc_cov, bool) + if calc_cov: + num_unknowns = max( + [ + len(experiment.get_labeled_model().unknown_parameters) + for experiment in self.exp_list + ] + ) + assert isinstance(cov_n, int), ( + "The number of datapoints that are used in the objective function is " + "required to calculate the covariance matrix" + ) + assert ( + cov_n > num_unknowns + ), "The number of datapoints must be greater than the number of parameters to estimate" + return_values = self._Q_opt( + solver=solver, + return_values=return_values, + bootlist=None, + calc_cov=calc_cov, + cov_n=cov_n, + ) + + ''' def theta_est_bootstrap( self, bootstrap_samples, From eca0ba802fe283ac18b451724c16cf9fe1bec927 Mon Sep 17 00:00:00 2001 From: Stephen Cini <114932899+sscini@users.noreply.github.com> Date: Wed, 23 Apr 2025 16:22:48 -0400 Subject: [PATCH 02/23] Finished first draft of pseudocode for multistart --- pyomo/contrib/parmest/parmest.py | 68 ++++++++++++++++++++++++++++---- 1 file changed, 60 insertions(+), 8 deletions(-) diff --git a/pyomo/contrib/parmest/parmest.py b/pyomo/contrib/parmest/parmest.py index 7c5cc618b83..88bc02c7c65 100644 --- a/pyomo/contrib/parmest/parmest.py +++ b/pyomo/contrib/parmest/parmest.py @@ -466,13 +466,30 @@ def TotalCost_rule(model): # optimization. It will take the theta names and the initial theta values # and return a dictionary of theta names and their corresponding values. # def _generalize_initial_theta(self, theta_names, initial_theta): + # if n_restarts == 1: + # # If only one restart, return an empty list + # return [] + + # return {theta_names[i]: initial_theta[i] for i in range(len(theta_names))} # if self.method == "random": # # Generate random theta values - # theta_vals = np.random.uniform(lower_bound, upper_bound, size=len(theta_names)) + # theta_vals = np.random.uniform(lower_bound, upper_bound, size=len(theta_names) + # else: + # # Generate theta values using Latin hypercube sampling or Sobol sampling + # samples # elif self.method == "latin_hypercube": # # Generate theta values using Latin hypercube sampling - # theta_vals = scipy.statsqmc.LatinHypercube(n=len(theta_names)).rvs(size=self.n_restarts) + # sampler = scipy.stats.qmc.LatinHypercube(d=len(theta_names)) + # samples = sampler.random(n=self.n_restarts) + # theta_vals = np.array([lower_bound + (upper_bound - lower_bound) * theta for theta in samples]) + + # elif self.method == "sobol": + # sampler = scipy.stats.qmc.Sobol(d=len(theta_names)) + # samples = sampler.random(n=self.n_restarts) + # theta_vals = np.array([lower_bound + (upper_bound - lower_bound) * theta for theta in samples]) + + # return theta_vals_multistart def _instance_creation_callback(self, experiment_number=None, cb_data=None): model = self._create_parmest_model(experiment_number) @@ -951,6 +968,7 @@ def theta_est( def theta_est_multistart( self, n_restarts=1, + theta_vals=None, theta_sampling_method="random", solver="ef_ipopt", return_values=[], @@ -1015,12 +1033,46 @@ def theta_est_multistart( assert ( cov_n > num_unknowns ), "The number of datapoints must be greater than the number of parameters to estimate" - return_values = self._Q_opt( - solver=solver, - return_values=return_values, - bootlist=None, - calc_cov=calc_cov, - cov_n=cov_n, + if n_restarts > 1 and theta_sampling_method is not None: + call self._generalize_initial_theta( + self.estimator_theta_names, self.initial_theta + ) + # make empty list to store results + + for i in range(n_restarts): + # for number of restarts, call the self._Q_opt method + # with the theta values generated using the _generalize_initial_theta method + + # Call the _Q_opt method with the generated theta values + objectiveval, thetavals, variable_values, cov = self._Q_opt( + ThetaVals=theta_vals, + solver=solver, + return_values=return_values, + calc_cov=calc_cov, + cov_n=cov_n, + ) + # Store the results in a list or DataFrame + # depending on the number of restarts + if n_restarts > 1 and cov is not None: + results.append( + { + "objectiveval": objectiveval, + "thetavals": thetavals, + "variable_values": variable_values, + "cov": cov, + } + elif n_restarts > 1 and cov is None: + results.append( + { objectiveval: objectiveval, + "thetavals": thetavals, + "variable_values": variable_values, + } + ) + return pd.DataFrame(results) + else: + return objectiveval, thetavals, variable_values, cov + + ) ''' From 2160aece3f9c91f87b5c2c321d32fd9e5eca1127 Mon Sep 17 00:00:00 2001 From: Stephen Cini <114932899+sscini@users.noreply.github.com> Date: Wed, 23 Apr 2025 16:38:59 -0400 Subject: [PATCH 03/23] Fixed logical errors in pseudocode --- pyomo/contrib/parmest/parmest.py | 11 +++++++++++ 1 file changed, 11 insertions(+) diff --git a/pyomo/contrib/parmest/parmest.py b/pyomo/contrib/parmest/parmest.py index 88bc02c7c65..8fc58831bbf 100644 --- a/pyomo/contrib/parmest/parmest.py +++ b/pyomo/contrib/parmest/parmest.py @@ -313,6 +313,10 @@ def __init__( self.diagnostic_mode = diagnostic_mode self.solver_options = solver_options + # add the extra multistart arguments to the Estimator class + # self.n_restarts = n_restarts + # self.theta_sampling_method = theta_sampling_method + # TODO: delete this when the deprecated interface is removed self.pest_deprecated = None @@ -1038,7 +1042,14 @@ def theta_est_multistart( self.estimator_theta_names, self.initial_theta ) # make empty list to store results + + + theta_vals = self._generalize_initial_theta( + self.estimator_theta_names, self.initial_theta, self.n_restarts, theta_sampling_method + ) + + results = [] for i in range(n_restarts): # for number of restarts, call the self._Q_opt method # with the theta values generated using the _generalize_initial_theta method From 266beea52a91488c29b8cd687a3b4c99209c73ca Mon Sep 17 00:00:00 2001 From: Stephen Cini <114932899+sscini@users.noreply.github.com> Date: Wed, 30 Apr 2025 11:07:20 -0400 Subject: [PATCH 04/23] Started implementing review comments 4/30 --- pyomo/contrib/parmest/parmest.py | 163 +++++++++++++------------------ 1 file changed, 70 insertions(+), 93 deletions(-) diff --git a/pyomo/contrib/parmest/parmest.py b/pyomo/contrib/parmest/parmest.py index 8fc58831bbf..5428f49c00b 100644 --- a/pyomo/contrib/parmest/parmest.py +++ b/pyomo/contrib/parmest/parmest.py @@ -279,8 +279,8 @@ def __init__( # Add the extra arguments needed for running the multistart implement # _validate_multistart_args: # if n_restarts > 1 and theta_samplig_method is not None: - # n_restarts=1, - # theta_sampling_method="random", + n_restarts=20, + multistart_sampling_method="random", ): '''first theta would be provided by the user in the initialization of @@ -314,8 +314,8 @@ def __init__( self.solver_options = solver_options # add the extra multistart arguments to the Estimator class - # self.n_restarts = n_restarts - # self.theta_sampling_method = theta_sampling_method + self.n_restarts = n_restarts + self.multistart_sampling_method = multistart_sampling_method # TODO: delete this when the deprecated interface is removed self.pest_deprecated = None @@ -469,31 +469,51 @@ def TotalCost_rule(model): # This method will be used to generalize the initial theta values for multistart # optimization. It will take the theta names and the initial theta values # and return a dictionary of theta names and their corresponding values. - # def _generalize_initial_theta(self, theta_names, initial_theta): - # if n_restarts == 1: - # # If only one restart, return an empty list - # return [] - - # return {theta_names[i]: initial_theta[i] for i in range(len(theta_names))} - # if self.method == "random": - # # Generate random theta values - # theta_vals = np.random.uniform(lower_bound, upper_bound, size=len(theta_names) - # else: - # # Generate theta values using Latin hypercube sampling or Sobol sampling - # samples - - # elif self.method == "latin_hypercube": - # # Generate theta values using Latin hypercube sampling - # sampler = scipy.stats.qmc.LatinHypercube(d=len(theta_names)) - # samples = sampler.random(n=self.n_restarts) - # theta_vals = np.array([lower_bound + (upper_bound - lower_bound) * theta for theta in samples]) - - # elif self.method == "sobol": - # sampler = scipy.stats.qmc.Sobol(d=len(theta_names)) - # samples = sampler.random(n=self.n_restarts) - # theta_vals = np.array([lower_bound + (upper_bound - lower_bound) * theta for theta in samples]) - - # return theta_vals_multistart + def _generate_initial_theta(self, parmest_model, seed=None): + if self.n_restarts == 1: + # If only one restart, return an empty list + return print("No multistart optimization needed. Please use normal theta_est()") + + # Get the theta names and initial theta values + theta_names = self._return_theta_names() + initial_theta = [parmest_model.find_component(name)() for name in theta_names] + + # Get the lower and upper bounds for the theta values + lower_bound = np.array([parmest_model.find_component(name).lb for name in theta_names]) + upper_bound = np.array([parmest_model.find_component(name).ub for name in theta_names]) + # Check if the lower and upper bounds are defined + if np.any(np.isnan(lower_bound)) or np.any(np.isnan(upper_bound)): + raise ValueError( + "The lower and upper bounds for the theta values must be defined." + ) + + # Check the length of theta_names and initial_theta, and make sure bounds are defined + if len(theta_names) != len(initial_theta): + raise ValueError( + "The length of theta_names and initial_theta must be the same." + ) + + if self.method == "random": + np.random.seed(seed) + # Generate random theta values + theta_vals_multistart = np.random.uniform(lower_bound, upper_bound, size=len(theta_names)) + + # Generate theta values using Latin hypercube sampling or Sobol sampling + return theta_vals_multistart + + elif self.method == "latin_hypercube": + # Generate theta values using Latin hypercube sampling + sampler = scipy.stats.qmc.LatinHypercube(d=len(theta_names), seed=seed) + samples = sampler.random(n=self.n_restarts) + theta_vals_multistart = np.array([lower_bound + (upper_bound - lower_bound) * theta for theta in samples]) + + + elif self.method == "sobol": + sampler = scipy.stats.qmc.Sobol(d=len(theta_names), seed=seed) + samples = sampler.random(n=self.n_restarts) + theta_vals_multistart = np.array([lower_bound + (upper_bound - lower_bound) * theta for theta in samples]) + + return theta_vals_multistart def _instance_creation_callback(self, experiment_number=None, cb_data=None): model = self._create_parmest_model(experiment_number) @@ -968,16 +988,11 @@ def theta_est( cov_n=cov_n, ) - ''' def theta_est_multistart( self, - n_restarts=1, theta_vals=None, - theta_sampling_method="random", solver="ef_ipopt", return_values=[], - calc_cov=False, - cov_n=None, ): """ Parameter estimation using multistart optimization @@ -993,12 +1008,7 @@ def theta_est_multistart( Currently only "ef_ipopt" is supported. Default is "ef_ipopt". return_values: list, optional List of Variable names, used to return values from the model for data reconciliation - calc_cov: boolean, optional - If True, calculate and return the covariance matrix (only for "ef_ipopt" solver). - Default is False. - cov_n: int, optional - If calc_cov=True, then the user needs to supply the number of datapoints - that are used in the objective function. + Returns ------- @@ -1008,49 +1018,29 @@ def theta_est_multistart( Estimated values for theta variable values: pd.DataFrame Variable values for each variable name in return_values (only for solver='ef_ipopt') - cov: pd.DataFrame - Covariance matrix of the fitted parameters (only for solver='ef_ipopt') + """ # check if we are using deprecated parmest if self.pest_deprecated is not None: return print( - "Multistart is not supported in the deprecated parmest interface") + "Multistart is not supported in the deprecated parmest interface" ) - assert isinstance(n_restarts, int) - assert isinstance(theta_sampling_method, str) + assert isinstance(self.n_restarts, int) + assert isinstance(self.multistart_sampling_method, str) assert isinstance(solver, str) assert isinstance(return_values, list) - assert isinstance(calc_cov, bool) - if calc_cov: - num_unknowns = max( - [ - len(experiment.get_labeled_model().unknown_parameters) - for experiment in self.exp_list - ] - ) - assert isinstance(cov_n, int), ( - "The number of datapoints that are used in the objective function is " - "required to calculate the covariance matrix" - ) - assert ( - cov_n > num_unknowns - ), "The number of datapoints must be greater than the number of parameters to estimate" - if n_restarts > 1 and theta_sampling_method is not None: - call self._generalize_initial_theta( - self.estimator_theta_names, self.initial_theta - ) - # make empty list to store results - - + + if self.n_restarts > 1 and self.multistart_sampling_method is not None: + # Generate theta values using the sampling method theta_vals = self._generalize_initial_theta( - self.estimator_theta_names, self.initial_theta, self.n_restarts, theta_sampling_method + self.estimator_theta_names, self.initial_theta, self.n_restarts, self.multistart_sampling_method ) - + # make empty list to store results results = [] - for i in range(n_restarts): + for i in range(self.n_restarts): # for number of restarts, call the self._Q_opt method # with the theta values generated using the _generalize_initial_theta method @@ -1059,34 +1049,21 @@ def theta_est_multistart( ThetaVals=theta_vals, solver=solver, return_values=return_values, - calc_cov=calc_cov, - cov_n=cov_n, ) # Store the results in a list or DataFrame # depending on the number of restarts - if n_restarts > 1 and cov is not None: - results.append( - { - "objectiveval": objectiveval, - "thetavals": thetavals, - "variable_values": variable_values, - "cov": cov, - } - elif n_restarts > 1 and cov is None: - results.append( - { objectiveval: objectiveval, - "thetavals": thetavals, - "variable_values": variable_values, - } - ) + + if self.n_restarts > 1: + results.append( + { objectiveval: objectiveval, + "thetavals": thetavals, + "variable_values": variable_values, + } + ) return pd.DataFrame(results) - else: - return objectiveval, thetavals, variable_values, cov - - - ) - ''' + + def theta_est_bootstrap( self, bootstrap_samples, From 9f1ffe5ec226d177cc6125be8febe7775e405fe1 Mon Sep 17 00:00:00 2001 From: Stephen Cini <114932899+sscini@users.noreply.github.com> Date: Thu, 1 May 2025 15:51:16 -0400 Subject: [PATCH 05/23] Work on edits, 5/1/25 --- pyomo/contrib/parmest/parmest.py | 98 ++++++++++++++++++++++++++------ 1 file changed, 82 insertions(+), 16 deletions(-) diff --git a/pyomo/contrib/parmest/parmest.py b/pyomo/contrib/parmest/parmest.py index 5428f49c00b..c5e418ac540 100644 --- a/pyomo/contrib/parmest/parmest.py +++ b/pyomo/contrib/parmest/parmest.py @@ -465,8 +465,8 @@ def TotalCost_rule(model): return parmest_model - # Make new private method, _generalize_initial_theta: - # This method will be used to generalize the initial theta values for multistart + # Make new private method, _generate_initial_theta: + # This method will be used to generate the initial theta values for multistart # optimization. It will take the theta names and the initial theta values # and return a dictionary of theta names and their corresponding values. def _generate_initial_theta(self, parmest_model, seed=None): @@ -504,16 +504,48 @@ def _generate_initial_theta(self, parmest_model, seed=None): elif self.method == "latin_hypercube": # Generate theta values using Latin hypercube sampling sampler = scipy.stats.qmc.LatinHypercube(d=len(theta_names), seed=seed) - samples = sampler.random(n=self.n_restarts) + samples = sampler.random(n=self.n_restarts+1)[1:] # Skip the first sample theta_vals_multistart = np.array([lower_bound + (upper_bound - lower_bound) * theta for theta in samples]) elif self.method == "sobol": sampler = scipy.stats.qmc.Sobol(d=len(theta_names), seed=seed) - samples = sampler.random(n=self.n_restarts) + samples = sampler.random(n=self.n_restarts+1)[1:] theta_vals_multistart = np.array([lower_bound + (upper_bound - lower_bound) * theta for theta in samples]) - return theta_vals_multistart + # elif self.method == "prior": + # # Still working on this + # theta_vals_multistart = np.array([lower_bound + (upper_bound - lower_bound) * theta for theta in initial_theta]) + + else: + raise ValueError( + "Invalid sampling method. Choose 'random', 'latin_hypercube', 'sobol'." # or 'prior'." + ) + + # Make an output dataframe with the theta names and their corresponding values for each restart, + # and nan for the output info values + df_multistart = pd.DataFrame( + theta_vals_multistart, columns=theta_names + ) + df_multistart["initial objective"] = np.nan + df_multistart["final objective"] = np.nan + df_multistart["solver termination"] = np.nan + df_multistart["solve_time"] = np.nan + + # Add the initial theta values to the first row of the dataframe + for i in self.n_restarts: + df_multistart.iloc[i, :] = theta_vals_multistart[i, :] + df_multistart.iloc[0, :] = initial_theta + # # Add the initial objective value to the first row of the dataframe + # df_multistart.iloc[0, -1] = self._Q_at_theta(initial_theta, initialize_parmest_model=True)[0] + # # Add the final objective value to the first row of the dataframe + # df_multistart.iloc[0, -2] = self._Q_at_theta(initial_theta, initialize_parmest_model=True)[0] + # # Add the solver termination value to the first row of the dataframe + # df_multistart.iloc[0, -3] = self._Q_at_theta(initial_theta, initialize_parmest_model=True)[2] + # # Add the solve time to the first row of the dataframe + # df_multistart.iloc[0, -4] = self._Q_at_theta(initial_theta, initialize_parmest_model=True)[3] + + return theta_vals_multistart, df_multistart def _instance_creation_callback(self, experiment_number=None, cb_data=None): model = self._create_parmest_model(experiment_number) @@ -990,6 +1022,8 @@ def theta_est( def theta_est_multistart( self, + buffer=10, + save_results=False, theta_vals=None, solver="ef_ipopt", return_values=[], @@ -1034,33 +1068,65 @@ def theta_est_multistart( if self.n_restarts > 1 and self.multistart_sampling_method is not None: # Generate theta values using the sampling method - theta_vals = self._generalize_initial_theta( + theta_vals, results_df = self._generate_initial_theta( self.estimator_theta_names, self.initial_theta, self.n_restarts, self.multistart_sampling_method ) # make empty list to store results - results = [] for i in range(self.n_restarts): # for number of restarts, call the self._Q_opt method # with the theta values generated using the _generalize_initial_theta method # Call the _Q_opt method with the generated theta values - objectiveval, thetavals, variable_values, cov = self._Q_opt( + objectiveval, thetavals[i], variable_values = self._Q_opt( ThetaVals=theta_vals, solver=solver, return_values=return_values, ) + + # Check if the solver terminated successfully + if variable_values.solver.termination_condition != pyo.TerminationCondition.optimal: + # If not, set the objective value to NaN + solver_termination = variable_values.solver.termination_condition + solve_time = variable_values.solver.time + thetavals = np.nan + + else: + + # If the solver terminated successfully, set the objective value + init_objectiveval = objectiveval + final_objectiveval = variable_values.solver.objective() + solver_termination = variable_values.solver.termination_condition + solve_time = variable_values.solver.time + + # Check if the objective value is better than the best objective value + if final_objectiveval < best_objectiveval: + best_objectiveval = objectiveval + best_theta = thetavals + # Store the results in a list or DataFrame # depending on the number of restarts + results_df.iloc[i, :-4] = theta_vals + results_df.iloc[i, -4] = init_objectiveval + results_df.iloc[i, -3] = objectiveval + results_df.iloc[i, -2] = variable_values.solver.termination_condition + results_df.iloc[i, -1] = variable_values.solver.time + + # Add buffer to save the dataframe dynamically, if save_results is True + if save_results and (i + 1) % buffer == 0: + mode = 'w' if i + 1 == buffer else 'a' + header = i + 1 == buffer + results_df.to_csv( + f"multistart_results.csv", mode=mode, header=header, index=False + ) + print(f"Intermediate results saved after {i + 1} iterations.") - if self.n_restarts > 1: - results.append( - { objectiveval: objectiveval, - "thetavals": thetavals, - "variable_values": variable_values, - } - ) - return pd.DataFrame(results) + # Final save after all iterations + if save_results: + results_df.to_csv("multistart_results.csv", mode='a', header=False, index=False) + print("Final results saved.") + + return results_df, best_theta, best_objectiveval From ea067c80ebac177024f3007a5cb0a217e49d47ba Mon Sep 17 00:00:00 2001 From: Stephen Cini <114932899+sscini@users.noreply.github.com> Date: Fri, 2 May 2025 06:50:36 -0400 Subject: [PATCH 06/23] Made edits, still debugging --- pyomo/contrib/parmest/parmest.py | 153 +++++++++++++++++++++---------- 1 file changed, 103 insertions(+), 50 deletions(-) diff --git a/pyomo/contrib/parmest/parmest.py b/pyomo/contrib/parmest/parmest.py index c5e418ac540..3352aa80fe2 100644 --- a/pyomo/contrib/parmest/parmest.py +++ b/pyomo/contrib/parmest/parmest.py @@ -276,11 +276,6 @@ def __init__( tee=False, diagnostic_mode=False, solver_options=None, - # Add the extra arguments needed for running the multistart implement - # _validate_multistart_args: - # if n_restarts > 1 and theta_samplig_method is not None: - n_restarts=20, - multistart_sampling_method="random", ): '''first theta would be provided by the user in the initialization of @@ -313,10 +308,6 @@ def __init__( self.diagnostic_mode = diagnostic_mode self.solver_options = solver_options - # add the extra multistart arguments to the Estimator class - self.n_restarts = n_restarts - self.multistart_sampling_method = multistart_sampling_method - # TODO: delete this when the deprecated interface is removed self.pest_deprecated = None @@ -469,8 +460,8 @@ def TotalCost_rule(model): # This method will be used to generate the initial theta values for multistart # optimization. It will take the theta names and the initial theta values # and return a dictionary of theta names and their corresponding values. - def _generate_initial_theta(self, parmest_model, seed=None): - if self.n_restarts == 1: + def _generate_initial_theta(self, parmest_model, seed=None, n_restarts=None, multistart_sampling_method=None, user_provided=None): + if n_restarts == 1: # If only one restart, return an empty list return print("No multistart optimization needed. Please use normal theta_est()") @@ -482,7 +473,7 @@ def _generate_initial_theta(self, parmest_model, seed=None): lower_bound = np.array([parmest_model.find_component(name).lb for name in theta_names]) upper_bound = np.array([parmest_model.find_component(name).ub for name in theta_names]) # Check if the lower and upper bounds are defined - if np.any(np.isnan(lower_bound)) or np.any(np.isnan(upper_bound)): + if any(bound is None for bound in lower_bound) and any(bound is None for bound in upper_bound): raise ValueError( "The lower and upper bounds for the theta values must be defined." ) @@ -493,33 +484,77 @@ def _generate_initial_theta(self, parmest_model, seed=None): "The length of theta_names and initial_theta must be the same." ) - if self.method == "random": + if multistart_sampling_method == "random": np.random.seed(seed) # Generate random theta values theta_vals_multistart = np.random.uniform(lower_bound, upper_bound, size=len(theta_names)) # Generate theta values using Latin hypercube sampling or Sobol sampling - return theta_vals_multistart - elif self.method == "latin_hypercube": + elif multistart_sampling_method == "latin_hypercube": # Generate theta values using Latin hypercube sampling sampler = scipy.stats.qmc.LatinHypercube(d=len(theta_names), seed=seed) - samples = sampler.random(n=self.n_restarts+1)[1:] # Skip the first sample + samples = sampler.random(n=n_restarts) theta_vals_multistart = np.array([lower_bound + (upper_bound - lower_bound) * theta for theta in samples]) - elif self.method == "sobol": + elif multistart_sampling_method == "sobol": sampler = scipy.stats.qmc.Sobol(d=len(theta_names), seed=seed) - samples = sampler.random(n=self.n_restarts+1)[1:] + # Generate theta values using Sobol sampling + # The first value of the Sobol sequence is 0, so we skip it + samples = sampler.random(n=n_restarts+1)[1:] theta_vals_multistart = np.array([lower_bound + (upper_bound - lower_bound) * theta for theta in samples]) - # elif self.method == "prior": - # # Still working on this - # theta_vals_multistart = np.array([lower_bound + (upper_bound - lower_bound) * theta for theta in initial_theta]) + elif multistart_sampling_method == "user_provided": + # Add user provided dataframe option + if user_provided is not None: + + if isinstance(user_provided, np.ndarray): + # Check if the user provided numpy array has the same number of rows as the number of restarts + if user_provided.shape[0] != n_restarts: + raise ValueError( + "The user provided numpy array must have the same number of rows as the number of restarts." + ) + # Check if the user provided numpy array has the same number of columns as the number of theta names + if user_provided.shape[1] != len(theta_names): + raise ValueError( + "The user provided numpy array must have the same number of columns as the number of theta names." + ) + # Check if the user provided numpy array has the same theta names as the model + # if not, raise an error + # if not all(theta in theta_names for theta in user_provided.columns): + raise ValueError( + "The user provided numpy array must have the same theta names as the model." + ) + # If all checks pass, return the user provided numpy array + theta_vals_multistart = user_provided + elif isinstance(user_provided, pd.DataFrame): + # Check if the user provided dataframe has the same number of rows as the number of restarts + if user_provided.shape[0] != n_restarts: + raise ValueError( + "The user provided dataframe must have the same number of rows as the number of restarts." + ) + # Check if the user provided dataframe has the same number of columns as the number of theta names + if user_provided.shape[1] != len(theta_names): + raise ValueError( + "The user provided dataframe must have the same number of columns as the number of theta names." + ) + # Check if the user provided dataframe has the same theta names as the model + # if not, raise an error + # if not all(theta in theta_names for theta in user_provided.columns): + raise ValueError( + "The user provided dataframe must have the same theta names as the model." + ) + # If all checks pass, return the user provided dataframe + theta_vals_multistart = user_provided.iloc[0: len(initial_theta)].values + else: + raise ValueError( + "The user must provide a numpy array or pandas dataframe from a previous attempt to use the 'user_provided' method." + ) else: raise ValueError( - "Invalid sampling method. Choose 'random', 'latin_hypercube', 'sobol'." # or 'prior'." + "Invalid sampling method. Choose 'random', 'latin_hypercube', 'sobol' or 'user_provided'." ) # Make an output dataframe with the theta names and their corresponding values for each restart, @@ -527,25 +562,23 @@ def _generate_initial_theta(self, parmest_model, seed=None): df_multistart = pd.DataFrame( theta_vals_multistart, columns=theta_names ) - df_multistart["initial objective"] = np.nan - df_multistart["final objective"] = np.nan - df_multistart["solver termination"] = np.nan - df_multistart["solve_time"] = np.nan + # Add the initial theta values to the first row of the dataframe - for i in self.n_restarts: + for i in range(1, n_restarts): df_multistart.iloc[i, :] = theta_vals_multistart[i, :] df_multistart.iloc[0, :] = initial_theta - # # Add the initial objective value to the first row of the dataframe - # df_multistart.iloc[0, -1] = self._Q_at_theta(initial_theta, initialize_parmest_model=True)[0] - # # Add the final objective value to the first row of the dataframe - # df_multistart.iloc[0, -2] = self._Q_at_theta(initial_theta, initialize_parmest_model=True)[0] - # # Add the solver termination value to the first row of the dataframe - # df_multistart.iloc[0, -3] = self._Q_at_theta(initial_theta, initialize_parmest_model=True)[2] - # # Add the solve time to the first row of the dataframe - # df_multistart.iloc[0, -4] = self._Q_at_theta(initial_theta, initialize_parmest_model=True)[3] - return theta_vals_multistart, df_multistart + + # Add the output info values to the dataframe, starting values as nan + for i in range(len(theta_names)): + df_multistart[f'converged_{theta_names[i]}'] = np.nan + df_multistart["initial objective"] = np.nan + df_multistart["final objective"] = np.nan + df_multistart["solver termination"] = np.nan + df_multistart["solve_time"] = np.nan + + return df_multistart def _instance_creation_callback(self, experiment_number=None, cb_data=None): model = self._create_parmest_model(experiment_number) @@ -1022,10 +1055,15 @@ def theta_est( def theta_est_multistart( self, + n_restarts=20, buffer=10, + multistart_sampling_method="random", + user_provided=None, + seed=None, save_results=False, theta_vals=None, solver="ef_ipopt", + file_name = "multistart_results.csv", return_values=[], ): """ @@ -1061,25 +1099,39 @@ def theta_est_multistart( "Multistart is not supported in the deprecated parmest interface" ) - assert isinstance(self.n_restarts, int) - assert isinstance(self.multistart_sampling_method, str) + assert isinstance(n_restarts, int) + assert isinstance(multistart_sampling_method, str) assert isinstance(solver, str) assert isinstance(return_values, list) - if self.n_restarts > 1 and self.multistart_sampling_method is not None: + if n_restarts > 1 and multistart_sampling_method is not None: + + # Find the initialized values of theta from the labeled parmest model + # and the theta names from the estimator object + parmest_model = self._create_parmest_model(experiment_number=0) + theta_names = self._return_theta_names() + initial_theta = [parmest_model.find_component(name)() for name in theta_names] + # Generate theta values using the sampling method - theta_vals, results_df = self._generate_initial_theta( - self.estimator_theta_names, self.initial_theta, self.n_restarts, self.multistart_sampling_method - ) + results_df = self._generate_initial_theta(parmest_model, seed=seed, n_restarts=n_restarts, + multistart_sampling_method=multistart_sampling_method, user_provided=user_provided) + results_df = pd.DataFrame(results_df) + # Extract theta_vals from the dataframe + theta_vals = results_df.iloc[:, :len(theta_names)] + converged_theta_vals = np.zeros((n_restarts, len(theta_names))) # make empty list to store results - for i in range(self.n_restarts): - # for number of restarts, call the self._Q_opt method - # with the theta values generated using the _generalize_initial_theta method + for i in range(n_restarts): + # for number of restarts, call the self._Q_opt method + # with the theta values generated using the _generalize_initial_theta method + + # set the theta values in the model + theta_vals_current = theta_vals.iloc[i, :] + # Call the _Q_opt method with the generated theta values - objectiveval, thetavals[i], variable_values = self._Q_opt( - ThetaVals=theta_vals, + objectiveval, converged_theta, variable_values = self._Q_opt( + ThetaVals=theta_vals_current, solver=solver, return_values=return_values, ) @@ -1094,6 +1146,7 @@ def theta_est_multistart( else: # If the solver terminated successfully, set the objective value + converged_theta_vals[i, :] = converged_theta.values() init_objectiveval = objectiveval final_objectiveval = variable_values.solver.objective() solver_termination = variable_values.solver.termination_condition @@ -1106,7 +1159,7 @@ def theta_est_multistart( # Store the results in a list or DataFrame # depending on the number of restarts - results_df.iloc[i, :-4] = theta_vals + results_df.iloc[i, len(theta_names):len(theta_names) + len(theta_names)] = converged_theta_vals[i, :] results_df.iloc[i, -4] = init_objectiveval results_df.iloc[i, -3] = objectiveval results_df.iloc[i, -2] = variable_values.solver.termination_condition @@ -1117,13 +1170,13 @@ def theta_est_multistart( mode = 'w' if i + 1 == buffer else 'a' header = i + 1 == buffer results_df.to_csv( - f"multistart_results.csv", mode=mode, header=header, index=False + file_name, mode=mode, header=header, index=False ) print(f"Intermediate results saved after {i + 1} iterations.") # Final save after all iterations if save_results: - results_df.to_csv("multistart_results.csv", mode='a', header=False, index=False) + results_df.to_csv(file_name, mode='a', header=False, index=False) print("Final results saved.") return results_df, best_theta, best_objectiveval From 3b839ef8c204c33941969ee49fd54f6b107980a0 Mon Sep 17 00:00:00 2001 From: Stephen Cini <114932899+sscini@users.noreply.github.com> Date: Wed, 14 May 2025 08:18:00 -0600 Subject: [PATCH 07/23] Addressed some comments in code. Still working through example to debug --- pyomo/contrib/parmest/parmest.py | 38 +++++++++++++++++++++++++------- 1 file changed, 30 insertions(+), 8 deletions(-) diff --git a/pyomo/contrib/parmest/parmest.py b/pyomo/contrib/parmest/parmest.py index 3352aa80fe2..e122afb7560 100644 --- a/pyomo/contrib/parmest/parmest.py +++ b/pyomo/contrib/parmest/parmest.py @@ -460,7 +460,10 @@ def TotalCost_rule(model): # This method will be used to generate the initial theta values for multistart # optimization. It will take the theta names and the initial theta values # and return a dictionary of theta names and their corresponding values. - def _generate_initial_theta(self, parmest_model, seed=None, n_restarts=None, multistart_sampling_method=None, user_provided=None): + def _generate_initial_theta(self, parmest_model=None, seed=None, n_restarts=None, multistart_sampling_method=None, user_provided=None): + """ + Generate initial theta values for multistart optimization using selected sampling method. + """ if n_restarts == 1: # If only one restart, return an empty list return print("No multistart optimization needed. Please use normal theta_est()") @@ -484,25 +487,31 @@ def _generate_initial_theta(self, parmest_model, seed=None, n_restarts=None, mul "The length of theta_names and initial_theta must be the same." ) - if multistart_sampling_method == "random": + if multistart_sampling_method == "uniform": + # Generate random theta values using uniform distribution, with set seed for reproducibility np.random.seed(seed) # Generate random theta values theta_vals_multistart = np.random.uniform(lower_bound, upper_bound, size=len(theta_names)) - # Generate theta values using Latin hypercube sampling or Sobol sampling + elif multistart_sampling_method == "latin_hypercube": + # Generate theta values using Latin hypercube sampling or Sobol sampling # Generate theta values using Latin hypercube sampling + # Create a Latin Hypercube sampler that uses the dimensions of the theta names sampler = scipy.stats.qmc.LatinHypercube(d=len(theta_names), seed=seed) + # Generate random samples in the range of [0, 1] for number of restarts samples = sampler.random(n=n_restarts) - theta_vals_multistart = np.array([lower_bound + (upper_bound - lower_bound) * theta for theta in samples]) - + # Resulting samples should be size (n_restarts, len(theta_names)) elif multistart_sampling_method == "sobol": sampler = scipy.stats.qmc.Sobol(d=len(theta_names), seed=seed) # Generate theta values using Sobol sampling # The first value of the Sobol sequence is 0, so we skip it samples = sampler.random(n=n_restarts+1)[1:] + + if multistart_sampling_method == "sobol" or multistart_sampling_method == "latin_hypercube": + # Scale the samples to the range of the lower and upper bounds for each theta in theta_names theta_vals_multistart = np.array([lower_bound + (upper_bound - lower_bound) * theta for theta in samples]) elif multistart_sampling_method == "user_provided": @@ -1073,9 +1082,13 @@ def theta_est_multistart( ---------- n_restarts: int, optional Number of restarts for multistart. Default is 1. - theta_sampling_method: string, optional + th_sampling_method: string, optional Method used to sample theta values. Options are "random", "latin_hypercube", or "sobol". Default is "random". + buffer: int, optional + Number of iterations to save results dynamically. Default is 10. + user_provided: pd.DataFrame, optional + User provided dataframe of theta values for multistart optimization. solver: string, optional Currently only "ef_ipopt" is supported. Default is "ef_ipopt". return_values: list, optional @@ -1157,8 +1170,17 @@ def theta_est_multistart( best_objectiveval = objectiveval best_theta = thetavals - # Store the results in a list or DataFrame - # depending on the number of restarts + # Store the results in a list or DataFrame depending on the number of restarts + ''' General structure for dataframe: + theta_names = ['theta1', 'theta2', ...] + results_df = pd.DataFrame(columns=theta_names + ['converged_theta1', 'converged_theta2', ..., + 'initial objective', 'final objective', + 'solver termination', 'solve_time']) + Each row of the dataframe corresponds to a restart, and the columns + correspond to the theta names, the converged theta values, the initial and final objective values, + the solver termination condition, and the solve time. + ''' + results_df.iloc[i, len(theta_names):len(theta_names) + len(theta_names)] = converged_theta_vals[i, :] results_df.iloc[i, -4] = init_objectiveval results_df.iloc[i, -3] = objectiveval From 50c36bc7e9ba47f191231544633e037dd90d2ecf Mon Sep 17 00:00:00 2001 From: Stephen Cini <114932899+sscini@users.noreply.github.com> Date: Wed, 21 May 2025 09:06:33 -0400 Subject: [PATCH 08/23] Got dataframe formatted, still working on executing Q_opt --- pyomo/contrib/parmest/parmest.py | 121 ++++++++++++++++--------------- 1 file changed, 63 insertions(+), 58 deletions(-) diff --git a/pyomo/contrib/parmest/parmest.py b/pyomo/contrib/parmest/parmest.py index e122afb7560..9f647a5d02b 100644 --- a/pyomo/contrib/parmest/parmest.py +++ b/pyomo/contrib/parmest/parmest.py @@ -490,10 +490,10 @@ def _generate_initial_theta(self, parmest_model=None, seed=None, n_restarts=None if multistart_sampling_method == "uniform": # Generate random theta values using uniform distribution, with set seed for reproducibility np.random.seed(seed) - # Generate random theta values - theta_vals_multistart = np.random.uniform(lower_bound, upper_bound, size=len(theta_names)) - - + # Generate random theta values for each restart (n_restarts x len(theta_names)) + theta_vals_multistart = np.random.uniform( + low=lower_bound, high=upper_bound, size=(n_restarts, len(theta_names)) + ) elif multistart_sampling_method == "latin_hypercube": # Generate theta values using Latin hypercube sampling or Sobol sampling @@ -510,10 +510,6 @@ def _generate_initial_theta(self, parmest_model=None, seed=None, n_restarts=None # The first value of the Sobol sequence is 0, so we skip it samples = sampler.random(n=n_restarts+1)[1:] - if multistart_sampling_method == "sobol" or multistart_sampling_method == "latin_hypercube": - # Scale the samples to the range of the lower and upper bounds for each theta in theta_names - theta_vals_multistart = np.array([lower_bound + (upper_bound - lower_bound) * theta for theta in samples]) - elif multistart_sampling_method == "user_provided": # Add user provided dataframe option if user_provided is not None: @@ -563,30 +559,39 @@ def _generate_initial_theta(self, parmest_model=None, seed=None, n_restarts=None else: raise ValueError( - "Invalid sampling method. Choose 'random', 'latin_hypercube', 'sobol' or 'user_provided'." + "Invalid sampling method. Choose 'uniform', 'latin_hypercube', 'sobol' or 'user_provided'." ) - # Make an output dataframe with the theta names and their corresponding values for each restart, - # and nan for the output info values - df_multistart = pd.DataFrame( - theta_vals_multistart, columns=theta_names - ) - - - # Add the initial theta values to the first row of the dataframe - for i in range(1, n_restarts): - df_multistart.iloc[i, :] = theta_vals_multistart[i, :] - df_multistart.iloc[0, :] = initial_theta - - - # Add the output info values to the dataframe, starting values as nan - for i in range(len(theta_names)): - df_multistart[f'converged_{theta_names[i]}'] = np.nan + if multistart_sampling_method == "sobol" or multistart_sampling_method == "latin_hypercube": + # Scale the samples to the range of the lower and upper bounds for each theta in theta_names + theta_vals_multistart = np.array([lower_bound + (upper_bound - lower_bound) * theta for theta in samples]) + + # Create a DataFrame where each row is an initial theta vector for a restart, + # columns are theta_names, and values are the initial theta values for each restart + if multistart_sampling_method == "user_provided": + # If user_provided is a DataFrame, use its columns and values directly + if isinstance(user_provided, pd.DataFrame): + df_multistart = user_provided.copy() + df_multistart.columns = theta_names + else: + df_multistart = pd.DataFrame(theta_vals_multistart, columns=theta_names) + else: + # Ensure theta_vals_multistart is 2D (n_restarts, len(theta_names)) + arr = np.atleast_2d(theta_vals_multistart) + if arr.shape[0] == 1 and n_restarts > 1: + arr = np.tile(arr, (n_restarts, 1)) + df_multistart = pd.DataFrame(arr, columns=theta_names) + + # Add columns for output info, initialized as nan + for name in theta_names: + df_multistart[f'converged_{name}'] = np.nan df_multistart["initial objective"] = np.nan df_multistart["final objective"] = np.nan df_multistart["solver termination"] = np.nan df_multistart["solve_time"] = np.nan + print(df_multistart) + return df_multistart def _instance_creation_callback(self, experiment_number=None, cb_data=None): @@ -1066,7 +1071,7 @@ def theta_est_multistart( self, n_restarts=20, buffer=10, - multistart_sampling_method="random", + multistart_sampling_method="uniform", user_provided=None, seed=None, save_results=False, @@ -1083,8 +1088,8 @@ def theta_est_multistart( n_restarts: int, optional Number of restarts for multistart. Default is 1. th_sampling_method: string, optional - Method used to sample theta values. Options are "random", "latin_hypercube", or "sobol". - Default is "random". + Method used to sample theta values. Options are "uniform", "latin_hypercube", or "sobol". + Default is "uniform". buffer: int, optional Number of iterations to save results dynamically. Default is 10. user_provided: pd.DataFrame, optional @@ -1139,53 +1144,53 @@ def theta_est_multistart( # with the theta values generated using the _generalize_initial_theta method # set the theta values in the model - theta_vals_current = theta_vals.iloc[i, :] + theta_vals_current = theta_vals.iloc[i, :].to_dict() # Call the _Q_opt method with the generated theta values - objectiveval, converged_theta, variable_values = self._Q_opt( + qopt_result = self._Q_opt( ThetaVals=theta_vals_current, solver=solver, return_values=return_values, ) - # Check if the solver terminated successfully - if variable_values.solver.termination_condition != pyo.TerminationCondition.optimal: - # If not, set the objective value to NaN - solver_termination = variable_values.solver.termination_condition - solve_time = variable_values.solver.time + # Unpack results depending on return_values + if len(return_values) > 0: + objectiveval, converged_theta, variable_values = qopt_result + else: + objectiveval, converged_theta = qopt_result + variable_values = None + + # Since _Q_opt does not return the solver result object, we cannot check + # solver termination condition directly here. Instead, we can assume + # that if converged_theta contains NaN, the solve failed. + if converged_theta.isnull().any(): + solver_termination = "not optimal" + solve_time = np.nan thetavals = np.nan - + final_objectiveval = np.nan + init_objectiveval = np.nan else: - - # If the solver terminated successfully, set the objective value - converged_theta_vals[i, :] = converged_theta.values() + converged_theta_vals[i, :] = converged_theta.values init_objectiveval = objectiveval - final_objectiveval = variable_values.solver.objective() - solver_termination = variable_values.solver.termination_condition - solve_time = variable_values.solver.time + final_objectiveval = objectiveval + solver_termination = "optimal" + solve_time = np.nan # Check if the objective value is better than the best objective value if final_objectiveval < best_objectiveval: best_objectiveval = objectiveval best_theta = thetavals - # Store the results in a list or DataFrame depending on the number of restarts - ''' General structure for dataframe: - theta_names = ['theta1', 'theta2', ...] - results_df = pd.DataFrame(columns=theta_names + ['converged_theta1', 'converged_theta2', ..., - 'initial objective', 'final objective', - 'solver termination', 'solve_time']) - Each row of the dataframe corresponds to a restart, and the columns - correspond to the theta names, the converged theta values, the initial and final objective values, - the solver termination condition, and the solve time. - ''' - - results_df.iloc[i, len(theta_names):len(theta_names) + len(theta_names)] = converged_theta_vals[i, :] - results_df.iloc[i, -4] = init_objectiveval - results_df.iloc[i, -3] = objectiveval - results_df.iloc[i, -2] = variable_values.solver.termination_condition - results_df.iloc[i, -1] = variable_values.solver.time + # Store the results in the DataFrame for this restart + # Fill converged theta values + for j, name in enumerate(theta_names): + results_df.at[i, f'converged_{name}'] = converged_theta[j] if not np.isnan(converged_theta_vals[i, j]) else np.nan + # Fill initial and final objective values, solver termination, and solve time + results_df.at[i, "initial objective"] = init_objectiveval if 'init_objectiveval' in locals() else np.nan + results_df.at[i, "final objective"] = objectiveval if 'objectiveval' in locals() else np.nan + results_df.at[i, "solver termination"] = solver_termination if 'solver_termination' in locals() else np.nan + results_df.at[i, "solve_time"] = solve_time if 'solve_time' in locals() else np.nan # Add buffer to save the dataframe dynamically, if save_results is True if save_results and (i + 1) % buffer == 0: From f4c7018736e437eb03cf37e2300f6742e56f31ff Mon Sep 17 00:00:00 2001 From: Stephen Cini <114932899+sscini@users.noreply.github.com> Date: Mon, 2 Jun 2025 14:15:04 -0400 Subject: [PATCH 09/23] Working code, adding features 6/2/25 --- .../reactor_design/multistart_example.py | 53 +++++++++++++++++++ pyomo/contrib/parmest/parmest.py | 42 +++++++++------ 2 files changed, 80 insertions(+), 15 deletions(-) create mode 100644 pyomo/contrib/parmest/examples/reactor_design/multistart_example.py diff --git a/pyomo/contrib/parmest/examples/reactor_design/multistart_example.py b/pyomo/contrib/parmest/examples/reactor_design/multistart_example.py new file mode 100644 index 00000000000..90c6791c188 --- /dev/null +++ b/pyomo/contrib/parmest/examples/reactor_design/multistart_example.py @@ -0,0 +1,53 @@ +# ___________________________________________________________________________ +# +# Pyomo: Python Optimization Modeling Objects +# Copyright (c) 2008-2025 +# National Technology and Engineering Solutions of Sandia, LLC +# Under the terms of Contract DE-NA0003525 with National Technology and +# Engineering Solutions of Sandia, LLC, the U.S. Government retains certain +# rights in this software. +# This software is distributed under the 3-clause BSD License. +# ___________________________________________________________________________ + +from pyomo.common.dependencies import pandas as pd +from os.path import join, abspath, dirname +import pyomo.contrib.parmest.parmest as parmest +from pyomo.contrib.parmest.examples.reactor_design.reactor_design import ( + ReactorDesignExperiment, +) + + +def main(): + + # Read in data + file_dirname = dirname(abspath(str(__file__))) + file_name = abspath(join(file_dirname, "reactor_data.csv")) + data = pd.read_csv(file_name) + + # Create an experiment list + exp_list = [] + for i in range(data.shape[0]): + exp_list.append(ReactorDesignExperiment(data, i)) + + # View one model + # exp0_model = exp_list[0].get_labeled_model() + # exp0_model.pprint() + + pest = parmest.Estimator(exp_list, obj_function='SSE') + + # Parameter estimation + obj, theta = pest.theta_est() + + # Parameter estimation with multistart to avoid local minima + obj, theta = pest.theta_est_multistart( + num_starts=10, + start_method='random', + random_seed=42, + max_iter=1000, + tol=1e-6, + ) + + + +if __name__ == "__main__": + main() diff --git a/pyomo/contrib/parmest/parmest.py b/pyomo/contrib/parmest/parmest.py index 9f647a5d02b..b02803608b9 100644 --- a/pyomo/contrib/parmest/parmest.py +++ b/pyomo/contrib/parmest/parmest.py @@ -1128,7 +1128,7 @@ def theta_est_multistart( # and the theta names from the estimator object parmest_model = self._create_parmest_model(experiment_number=0) theta_names = self._return_theta_names() - initial_theta = [parmest_model.find_component(name)() for name in theta_names] + # initial_theta = [parmest_model.find_component(name)() for name in theta_names] # Generate theta values using the sampling method results_df = self._generate_initial_theta(parmest_model, seed=seed, n_restarts=n_restarts, @@ -1146,20 +1146,24 @@ def theta_est_multistart( # set the theta values in the model theta_vals_current = theta_vals.iloc[i, :].to_dict() + # Set current theta values in the model + for name, value in theta_vals_current.items(): + parmest_model.find_component(name).set_value(value) + + # Print the current theta values being set + print(f"Setting {name} to {value}") + # Call the _Q_opt method with the generated theta values qopt_result = self._Q_opt( - ThetaVals=theta_vals_current, + bootlist=None, solver=solver, return_values=return_values, ) - # Unpack results depending on return_values - if len(return_values) > 0: - objectiveval, converged_theta, variable_values = qopt_result - else: - objectiveval, converged_theta = qopt_result - variable_values = None + # Unpack results + objectiveval, converged_theta = qopt_result + # Since _Q_opt does not return the solver result object, we cannot check # solver termination condition directly here. Instead, we can assume @@ -1175,13 +1179,18 @@ def theta_est_multistart( init_objectiveval = objectiveval final_objectiveval = objectiveval solver_termination = "optimal" - solve_time = np.nan - - # Check if the objective value is better than the best objective value - if final_objectiveval < best_objectiveval: - best_objectiveval = objectiveval - best_theta = thetavals + solve_time = converged_theta.get('solve_time', np.nan) + + # # Check if the objective value is better than the best objective value + # # Set a very high initial best objective value + # best_objectiveval = np.inf + # best_theta = np.inf + # if final_objectiveval < best_objectiveval: + # best_objectiveval = objectiveval + # best_theta = thetavals + print(f"Restart {i+1}/{n_restarts}: Objective Value = {final_objectiveval}, Theta = {converged_theta}") + # Store the results in the DataFrame for this restart # Fill converged theta values for j, name in enumerate(theta_names): @@ -1192,6 +1201,9 @@ def theta_est_multistart( results_df.at[i, "solver termination"] = solver_termination if 'solver_termination' in locals() else np.nan results_df.at[i, "solve_time"] = solve_time if 'solve_time' in locals() else np.nan + # Diagnostic: print the table after each restart + print(results_df) + # Add buffer to save the dataframe dynamically, if save_results is True if save_results and (i + 1) % buffer == 0: mode = 'w' if i + 1 == buffer else 'a' @@ -1206,7 +1218,7 @@ def theta_est_multistart( results_df.to_csv(file_name, mode='a', header=False, index=False) print("Final results saved.") - return results_df, best_theta, best_objectiveval + return results_df # just this for now, then best_theta, best_objectiveval From e7880005b003ca37570444e772939abaee6d652a Mon Sep 17 00:00:00 2001 From: Stephen Cini <114932899+sscini@users.noreply.github.com> Date: Tue, 3 Jun 2025 11:21:40 -0400 Subject: [PATCH 10/23] Added questions for next round of reviews --- pyomo/contrib/parmest/parmest.py | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/pyomo/contrib/parmest/parmest.py b/pyomo/contrib/parmest/parmest.py index b02803608b9..b6f37571503 100644 --- a/pyomo/contrib/parmest/parmest.py +++ b/pyomo/contrib/parmest/parmest.py @@ -1126,6 +1126,8 @@ def theta_est_multistart( # Find the initialized values of theta from the labeled parmest model # and the theta names from the estimator object + + # @Reviewers, pyomo team: Use this or use instance creation callback? parmest_model = self._create_parmest_model(experiment_number=0) theta_names = self._return_theta_names() # initial_theta = [parmest_model.find_component(name)() for name in theta_names] @@ -1139,6 +1141,8 @@ def theta_est_multistart( converged_theta_vals = np.zeros((n_restarts, len(theta_names))) # make empty list to store results + # @ Pyomo team, each of these instances are independent and thus embarassingly parallelizable, + # Do you have recommendations on how to parallelize this for loop on a multicore machine for i in range(n_restarts): # for number of restarts, call the self._Q_opt method # with the theta values generated using the _generalize_initial_theta method @@ -1179,7 +1183,10 @@ def theta_est_multistart( init_objectiveval = objectiveval final_objectiveval = objectiveval solver_termination = "optimal" - solve_time = converged_theta.get('solve_time', np.nan) + + # plan to add solve time if available, @Reviewers, recommendations on how from current pyomo examples would + # be appreciated + solve_time = converged_theta.solve_time if hasattr(converged_theta, 'solve_time') else np.nan # # Check if the objective value is better than the best objective value # # Set a very high initial best objective value From f071718bc76063f86be016f63b163709aef0c23a Mon Sep 17 00:00:00 2001 From: Stephen Cini <114932899+sscini@users.noreply.github.com> Date: Tue, 3 Jun 2025 11:37:11 -0400 Subject: [PATCH 11/23] Removed diagnostic tables to simplify output --- pyomo/contrib/parmest/parmest.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/pyomo/contrib/parmest/parmest.py b/pyomo/contrib/parmest/parmest.py index b6f37571503..9ff6ab8a135 100644 --- a/pyomo/contrib/parmest/parmest.py +++ b/pyomo/contrib/parmest/parmest.py @@ -590,7 +590,8 @@ def _generate_initial_theta(self, parmest_model=None, seed=None, n_restarts=None df_multistart["solver termination"] = np.nan df_multistart["solve_time"] = np.nan - print(df_multistart) + # Debugging output + # print(df_multistart) return df_multistart @@ -1209,7 +1210,7 @@ def theta_est_multistart( results_df.at[i, "solve_time"] = solve_time if 'solve_time' in locals() else np.nan # Diagnostic: print the table after each restart - print(results_df) + # print(results_df) # Add buffer to save the dataframe dynamically, if save_results is True if save_results and (i + 1) % buffer == 0: From 9b1545da4940542aeac0e8d7a977ce2acce5968b Mon Sep 17 00:00:00 2001 From: Stephen Cini <114932899+sscini@users.noreply.github.com> Date: Wed, 4 Jun 2025 15:57:10 -0400 Subject: [PATCH 12/23] Work from Wednesday of Sprint week --- pyomo/contrib/parmest/parmest.py | 41 +++++++++++++++++--------------- 1 file changed, 22 insertions(+), 19 deletions(-) diff --git a/pyomo/contrib/parmest/parmest.py b/pyomo/contrib/parmest/parmest.py index 9ff6ab8a135..4aa5eb81016 100644 --- a/pyomo/contrib/parmest/parmest.py +++ b/pyomo/contrib/parmest/parmest.py @@ -1128,37 +1128,38 @@ def theta_est_multistart( # Find the initialized values of theta from the labeled parmest model # and the theta names from the estimator object + # print statement to indicate multistart optimization is starting + print(f"Starting multistart optimization with {n_restarts} restarts using {multistart_sampling_method} sampling method.") + # @Reviewers, pyomo team: Use this or use instance creation callback? - parmest_model = self._create_parmest_model(experiment_number=0) theta_names = self._return_theta_names() - # initial_theta = [parmest_model.find_component(name)() for name in theta_names] - # Generate theta values using the sampling method - results_df = self._generate_initial_theta(parmest_model, seed=seed, n_restarts=n_restarts, - multistart_sampling_method=multistart_sampling_method, user_provided=user_provided) + parmest_model_for_bounds = self._create_parmest_model(experiment_number=0) + results_df = self._generate_initial_theta( + parmest_model_for_bounds, seed=seed, n_restarts=n_restarts, + multistart_sampling_method=multistart_sampling_method, user_provided=user_provided + ) results_df = pd.DataFrame(results_df) # Extract theta_vals from the dataframe theta_vals = results_df.iloc[:, :len(theta_names)] converged_theta_vals = np.zeros((n_restarts, len(theta_names))) - # make empty list to store results - # @ Pyomo team, each of these instances are independent and thus embarassingly parallelizable, - # Do you have recommendations on how to parallelize this for loop on a multicore machine + # Each restart uses a fresh model instance for i in range(n_restarts): - # for number of restarts, call the self._Q_opt method - # with the theta values generated using the _generalize_initial_theta method - - # set the theta values in the model + # Create a fresh model for each restart + parmest_model = self._create_parmest_model(experiment_number=0) theta_vals_current = theta_vals.iloc[i, :].to_dict() # Set current theta values in the model for name, value in theta_vals_current.items(): parmest_model.find_component(name).set_value(value) - # Print the current theta values being set - print(f"Setting {name} to {value}") - - + # Optional: Print the current theta values being set + print(f"Setting {name} to {value}") + for name in theta_names: + current_value = parmest_model.find_component(name)() + print(f"Current value of {name} is {current_value}") + # Call the _Q_opt method with the generated theta values qopt_result = self._Q_opt( bootlist=None, @@ -1174,16 +1175,18 @@ def theta_est_multistart( # solver termination condition directly here. Instead, we can assume # that if converged_theta contains NaN, the solve failed. if converged_theta.isnull().any(): - solver_termination = "not optimal" + solver_termination = "not successful" solve_time = np.nan thetavals = np.nan final_objectiveval = np.nan init_objectiveval = np.nan else: converged_theta_vals[i, :] = converged_theta.values - init_objectiveval = objectiveval + # Calculate the initial objective value using the current theta values + # Use the _Q_at_theta method to evaluate the objective at these theta values + init_objectiveval, _, _ = self._Q_at_theta(theta_vals_current) final_objectiveval = objectiveval - solver_termination = "optimal" + solver_termination = "successful" # plan to add solve time if available, @Reviewers, recommendations on how from current pyomo examples would # be appreciated From 80079cb90a0cedf15eb286de04d3f2e48a8ac503 Mon Sep 17 00:00:00 2001 From: Stephen Cini <114932899+sscini@users.noreply.github.com> Date: Wed, 4 Jun 2025 16:01:40 -0400 Subject: [PATCH 13/23] Create Simple_Multimodal_Multistart.ipynb --- .../Simple_Multimodal_Multistart.ipynb | 8231 +++++++++++++++++ 1 file changed, 8231 insertions(+) create mode 100644 pyomo/contrib/parmest/examples/Simple_Multimodal_Multistart.ipynb diff --git a/pyomo/contrib/parmest/examples/Simple_Multimodal_Multistart.ipynb b/pyomo/contrib/parmest/examples/Simple_Multimodal_Multistart.ipynb new file mode 100644 index 00000000000..0c2a2eb2062 --- /dev/null +++ b/pyomo/contrib/parmest/examples/Simple_Multimodal_Multistart.ipynb @@ -0,0 +1,8231 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "d3d4744d-5ea5-45d6-8e8b-aefc5d8b8118", + "metadata": {}, + "source": [ + "This example is created by Stephen Cini, inspired by example in Montana's manuscript and advice from Dr. Alex Dowling. Purpose is to test multistart function's ability to find local minima.\n", + "\n", + "**Equation:** \n", + " $$f(x, \\theta) = (\\theta_1 x^3 - \\theta_2 x^2 + 2x - 1)^2 + (\\theta_1 - \\theta_2)^2 + (x^2 - 1)^2$$ \n", + "$\\qquad f(x, \\theta)\\rightarrow$ (response / output variable) \n", + "$\\qquad \\theta_1, \\theta_2 \\rightarrow$ parameters \n", + "$\\qquad x\\rightarrow$ (control / decision variable) \n", + "\n", + "**Data:** \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
SubstrateVelocity
Concentration(counts/min2)
(ppm)(a) Treated(b) Untreated
0.0276
47		67
51
0.0697
107		84
86
0.11123
139		98
115
0.22159
152		131
124
0.56191
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\n", + "\n", + "[Find data or remove table]" + ] + }, + { + "cell_type": "markdown", + "id": "7f241a55-464a-41f6-9bc2-28f7467c2234", + "metadata": {}, + "source": [ + "# Importing packages" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "2a458e95", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pyomo version after reload:\n", + "6.9.3.dev0\n", + "pyomo.__file__: /Users/scini/Documents/GitHub/pyomo/pyomo/__init__.py\n" + ] + } + ], + "source": [ + "# Get version of pyomo\n", + "\n", + "# Force reload of pyomo module to ensure we get the latest version\n", + "import importlib\n", + "import pyomo # your .py file without the .py extension\n", + "importlib.reload(pyomo)\n", + "print(\"Pyomo version after reload:\")\n", + "print(pyomo.__version__)\n", + "print(\"pyomo.__file__:\", pyomo.__file__)\n", + "\n", + "import pyomo.environ as pyo\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "f6c3dd5b-0200-4262-9315-de03690bec0b", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import scipy\n", + "import seaborn as sns # This package is only needed for pairplot\n", + "import matplotlib.pyplot as plt\n", + "import pyomo.environ as pyo\n", + "import pyomo.contrib.parmest.parmest as parmest # import parmest\n", + "import pyomo.contrib.parmest.experiment as experiment\n", + "import pyomo.contrib.doe as doe\n", + "import pandas as pd\n", + "from itertools import product\n", + "import idaes" + ] + }, + { + "cell_type": "markdown", + "id": "5915d030-b939-4162-a85c-9f90c28a17c9", + "metadata": {}, + "source": [ + "# Data\n", + "Data for this model in Montana's manuscript was generated by simulating the model with true parameter values and adding 1% Gaussian error. So this is replicated here." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "231649a2-75e9-43af-938f-00b71ff136e8", + "metadata": {}, + "outputs": [], + "source": [ + "# True param values\n", + "true_params = {\n", + " 'theta1': -1.5,\n", + " 'theta2': 0.5,\n", + "}\n", + "\n", + "# $$f(x, \\theta) = (\\theta_1 x^3 - \\theta_2 x^2 + 2x - 1)^2 + (\\theta_1 - \\theta_2)^2 + (x^2 - 1)^2$$ \n", + "\n", + "def model(x, theta1, theta2):\n", + " return ((theta1 * x**3 - theta2 * x**2 + 2 * x - 1)**2 +\n", + " (theta1 - theta2)**2 +\n", + " (x**2 - 1)**2)\n", + "\n", + "def generate_data(num_samples=1000):\n", + " x_values = np.linspace(-4, 4, num_samples)\n", + " y_values = np.array([model(x, true_params['theta1'], true_params['theta2']) for x in x_values])\n", + "\n", + " # Add 1% Gaussian noise to the y values\n", + " noise = np.random.normal(0, 0.01 * np.abs(y_values), size=y_values.shape)\n", + " y_values += noise\n", + " # Create a DataFrame with the x and y values\n", + " # and return it\n", + " return pd.DataFrame({'x': x_values, 'y': y_values})\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "ae697125", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data generated with 1000 samples.\n", + " x y\n", + "0 -4.000000 6515.412188\n", + "1 -3.991992 6367.720590\n", + "2 -3.983984 6257.737250\n", + "3 -3.975976 6182.891231\n", + "4 -3.967968 6013.270490\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Make some data\n", + "data_df = generate_data()\n", + "print(\"Data generated with {} samples.\".format(len(data_df)))\n", + "\n", + "print(data_df.head())\n", + "\n", + "# Plot the data\n", + "plt.figure(figsize=(10, 6))\n", + "sns.scatterplot(data=data_df, x='x', y='y', color='blue', label='Data')\n", + "plt.title('Generated Data')\n", + "plt.xlabel('x')\n", + "plt.ylabel('y')\n", + "plt.legend()\n", + "plt.grid()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "580a2f7a", + "metadata": {}, + "outputs": [], + "source": [ + "conc = data_df['x'].values\n", + "vel = data_df['y'].values" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "7172aa46", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Analyze the objective space by evaluating the sum of squared errors (SSE)\n", + "# over a grid of theta_1 and theta_2 values\n", + "theta1_range = np.linspace(-2, 2, 200)\n", + "theta2_range = np.linspace(-2, 2, 200)\n", + "sse_grid = np.zeros((len(theta1_range), len(theta2_range)))\n", + "\n", + "# Use the model function from cell 5\n", + "for i, t1 in enumerate(theta1_range):\n", + " for j, t2 in enumerate(theta2_range):\n", + " y_sim = np.array([model(x, t1, t2) for x in conc])\n", + " sse = np.sum((vel - y_sim) ** 2)\n", + " sse_grid[i, j] = sse\n", + "\n", + "# Plot the objective space\n", + "plt.figure(figsize=(8, 6))\n", + "X, Y = np.meshgrid(theta2_range, theta1_range)\n", + "cp = plt.contourf(X, Y, sse_grid, levels=50, cmap='viridis')\n", + "plt.colorbar(cp, label='Sum of Squared Errors (SSE)')\n", + "plt.xlabel('theta_2')\n", + "plt.ylabel('theta_1')\n", + "plt.title('Objective Space: SSE over theta_1 and theta_2')\n", + "# Optionally, mark the minimum SSE location on the grid\n", + "min_idx = np.unravel_index(np.argmin(sse_grid), sse_grid.shape)\n", + "plt.scatter([theta2_range[min_idx[1]]], [theta1_range[min_idx[0]]], color='red', label='Grid Minimum')\n", + "plt.scatter([true_params['theta2']], [true_params['theta1']], color='white', marker='*', s=100, label='True Params')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "5eb58543", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the effect of varying each parameter (theta_1 and theta_2) one at a time\n", + "\n", + "fig, axes = plt.subplots(1, 2, figsize=(14, 5), sharey=True)\n", + "\n", + "# Vary theta_1, fix theta_2 at true value\n", + "theta1_vals = theta1_range\n", + "theta2_fixed = true_params[\"theta2\"]\n", + "sse_theta1 = []\n", + "for t1 in theta1_vals:\n", + " y_sim = np.array([model(x, t1, theta2_fixed) for x in conc])\n", + " sse = np.sum((vel - y_sim) ** 2)\n", + " sse_theta1.append(sse)\n", + "axes[0].plot(theta1_vals, sse_theta1, label=f'Fixed theta_2={theta2_fixed:.3f}')\n", + "axes[0].axvline(true_params['theta1'], color='green', linestyle='--', label='True theta_1')\n", + "axes[0].set_xlabel('theta_1')\n", + "axes[0].set_ylabel('Sum of Squared Errors (SSE)')\n", + "axes[0].set_title('SSE vs theta_1 (theta_2 fixed)')\n", + "axes[0].legend()\n", + "\n", + "# Vary theta_2, fix theta_1 at true value\n", + "theta2_vals = theta2_range\n", + "theta1_fixed = true_params[\"theta1\"]\n", + "sse_theta2 = []\n", + "for t2 in theta2_vals:\n", + " y_sim = np.array([model(x, theta1_fixed, t2) for x in conc])\n", + " sse = np.sum((vel - y_sim) ** 2)\n", + " sse_theta2.append(sse)\n", + "axes[1].plot(theta2_vals, sse_theta2, label=f'Fixed theta_1={theta1_fixed:.3f}')\n", + "axes[1].axvline(true_params['theta2'], color='green', linestyle='--', label='True theta_2')\n", + "axes[1].set_xlabel('theta_2')\n", + "axes[1].set_title('SSE vs theta_2 (theta_1 fixed)')\n", + "axes[1].legend()\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "be805271-1f41-4066-ad50-f912ba61fa53", + "metadata": {}, + "source": [ + "# PARMEST" + ] + }, + { + "cell_type": "markdown", + "id": "2b95ecb7-a1c7-4b09-b32e-dafe3fca3944", + "metadata": {}, + "source": [ + "## Creating an experiment class for both DOE and ParmEst\n", + "We will use this class for both parmest (to estimate the parameters) and DOE (To calculate the FIM and factorial design)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "a5e84eb1-94a6-40ec-a940-2306778a4f71", + "metadata": {}, + "outputs": [], + "source": [ + "class Simple_Multimodal(experiment.Experiment):\n", + "\n", + " # Defining the constructor for our model\n", + " def __init__(self, data, theta_initial=None):\n", + " \"\"\"\n", + " Arguments:\n", + " data: data from our experiment. type: 'dict'\n", + " theta_initial: initial guess of the parameter values, dtype: dict. pass the values as theta_initial = {1 : , 2 : }\n", + " default: {1: 100, 2: 0.05}\n", + " \n", + " \"\"\"\n", + " self.conc = data[\"x\"]\n", + " self.vel = data[\"y\"]\n", + " self.model = None \n", + " self.theta_initial = theta_initial\n", + " if self.theta_initial is None:\n", + " self.theta_initial = {1: -1.5, 2: 0.5} # default initial guess of theta[1] & theta[2]\n", + " else:\n", + " self.theta_initial = theta_initial\n", + "\n", + " # Creating the get_labeled_model which is a must for ``DOE`` and ``ParmEst``\n", + " def get_labeled_model(self):\n", + " if self.model is None:\n", + " self.create_model()\n", + " self.label_model()\n", + " self.finalize_model()\n", + " return self.model\n", + "\n", + " \n", + " def create_model(self):\n", + " \"\"\"\n", + " Here, we will create different variables, parameters, and constraints.\n", + " The index set for data points will be the actual substrate concentrations (x values).\n", + " \"\"\"\n", + " m = self.model = pyo.ConcreteModel()\n", + "\n", + " # theta_1 and theta_2 as parameters to be estimated\n", + " m.theta_1 = pyo.Var(initialize=self.theta_initial[1], bounds=(-2, 2))\n", + " m.theta_2 = pyo.Var(initialize=self.theta_initial[2], bounds=(-2, 2))\n", + "\n", + " # Use the actual conc values as the index set\n", + " m.x_set = pyo.Set(initialize=[float(val) for val in self.conc], ordered=True)\n", + "\n", + " # Substrate concentration (x) as a parameter, indexed by x_set\n", + " m.x = pyo.Param(m.x_set, initialize={float(val): float(val) for val in self.conc}, mutable=False)\n", + "\n", + " # Measured variable for each data point (indexed by x_set)\n", + " m.measured_var = pyo.Var(m.x_set)\n", + "\n", + " # Constraint for each data point\n", + " def meas_con(m, xval):\n", + " return m.measured_var[xval] == (m.theta_1 * m.x[xval]**3 - m.theta_2 * m.x[xval]**2 + 2 * m.x[xval] - 1)**2 + (m.theta_1 - m.theta_2)**2 + (m.x[xval]**2 - 1)**2\n", + " m.meas_con = pyo.Constraint(m.x_set, rule=meas_con)\n", + "\n", + " # Objective function: sum of squared errors over all data points\n", + " m.FirstStageCost = pyo.Expression(initialize=0)\n", + " m.SecondStageCost = pyo.Expression(expr=sum((self.vel[i] - m.measured_var[float(self.conc[i])]) ** 2 for i in range(len(self.conc))))\n", + " m.Total_Cost_Obj = pyo.Objective(expr=(m.FirstStageCost + m.SecondStageCost), sense=pyo.minimize)\n", + "\n", + " return m\n", + "\n", + " \n", + " def finalize_model(self):\n", + " \"\"\"\n", + " Finalizing the model. Here, we will set the experimental conditions (e.g, initial conditions),\n", + " fixing the parameter values (if needed), update `t` values, and discretize the model (if model is dynamic). \n", + " It makes a solvable model.\n", + " \"\"\" \n", + " m=self.model\n", + "\n", + " # fixing the parameters\n", + " m.theta_1.fix(self.theta_initial[1]) \n", + " m.theta_2.fix(self.theta_initial[2]) \n", + "\n", + "\n", + " return m\n", + "\n", + " \n", + " def label_model(self):\n", + " \"\"\"\n", + " The model is updated with outputs, and unknown parameters. This makes the model labeled with full experiment.\n", + " In `ParmEst` output (given data) is the most important. For `DOE` input is most important.\n", + " \"\"\"\n", + " m = self.model\n", + "\n", + " m.experiment_outputs = pyo.Suffix(direction = pyo.Suffix.LOCAL) \n", + " m.experiment_outputs.update([(m.measured_var, self.vel)]) # Pass the data as a list of `tuple`\n", + " # If we only use ``DOE``, we could use ``m.experiment_ouputs.update([(m.x, None)])``.\n", + " # Output is not important for ``DOE``\n", + "\n", + " # m.experiment_inputs = pyo.Suffix(direction = pyo.Suffix.LOCAL) \n", + " # # m.experiment_inputs[m.x] = self.conc\n", + " # m.experiment_inputs.update([(m.x, self.conc)])\n", + " # # If we only use ``DOE``, we could use ``m.experiment_inputs.update([(m.x, None)])``\n", + "\n", + " m.unknown_parameters = pyo.Suffix(direction = pyo.Suffix.LOCAL)\n", + " m.unknown_parameters.update((p, pyo.value(p)) for p in [m.theta_1, m.theta_2])\n", + " # m.unknown_parameters[m.theta_1]= self.theta_initial[1]\n", + " # m.unknown_parameters[m.theta_2]= self.theta_initial[2]\n", + "\n", + " m.measurement_error = pyo.Suffix(direction = pyo.Suffix.LOCAL)\n", + " # m.measurement_error[m.measured_var] = 0.03\n", + " m.measurement_error.update([(m.measured_var, 0.03**2)]) # variance of the measurement error, b/c in doe, the measurement error is passed as variance, not std\n", + " # this will be fixed in later update \n", + "\n", + " return m" + ] + }, + { + "cell_type": "markdown", + "id": "c303c406-ac5a-4ff6-9535-48967393acc4", + "metadata": {}, + "source": [ + "## Parmest Example\n", + "We will evaluate diffent parameters, visualize pairwise plots and show bootstrap table" + ] + }, + { + "cell_type": "markdown", + "id": "32d11b62-0040-4a65-b4f7-035730272094", + "metadata": {}, + "source": [ + "### Treated velocity\n", + "Here, we will calculate objective value at optimum parameter value, paramater values, $\\theta$, and covariance matrix for treated velocity." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "09b0eae6-e1d1-404c-be2a-98a1a8e776bf", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ipopt 3.13.2: bound_relax_factor=0\n", + "honor_original_bounds=no\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", + " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", + " 3 4.2816330e+05 4.18e-12 2.10e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", + " 4 4.2816330e+05 4.18e-12 1.51e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", + " 5 4.2816330e+05 5.09e-12 2.77e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", + " 6 4.2816330e+05 3.50e-12 1.50e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 6\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", + "Dual infeasibility......: 1.4985094187954244e-12 2.8984606156203206e-10\n", + "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", + "Complementarity.........: 2.5059035597864302e-09 4.8469917395796542e-07\n", + "Overall NLP error.......: 2.5059035597864302e-09 4.8469917395796542e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 7\n", + "Number of objective gradient evaluations = 7\n", + "Number of equality constraint evaluations = 7\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 7\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 6\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", + "\n", + "theta values:\n", + "theta_1 -1.500300\n", + "theta_2 0.501315\n", + "dtype: float64\n", + "\n", + "SSE value: 428163.2965187811\n", + "\n", + "Covariance matrix:\n", + " theta_1 theta_2\n", + "theta_1 4.667923e-08 3.978519e-08\n", + "theta_2 3.978519e-08 6.376757e-07\n" + ] + } + ], + "source": [ + "exp_list = [] \n", + "conc = data_df[\"x\"].values # substrate concentration (control variable)\n", + "vel = data_df[\"y\"].values # reaction velocity (output variable)\n", + "n_exp = 1\n", + "\n", + "# exp_list to separate each experiment\n", + "# for i in range(n_exp):\n", + "exp_list.append(Simple_Multimodal(data_df))\n", + "\n", + "# Creating an Estimator object\n", + "pest = parmest.Estimator(exp_list, tee = True) \n", + "\n", + "# Estimating d\n", + "obj, theta, cov = pest.theta_est(calc_cov=True, cov_n=len(conc),)\n", + "\n", + "# ``parmest`` can mess up the order of the theta estimates and also the covariance. So we need to fix the order so that we can manipulate it properly.\n", + "t_order = [\"theta_1\", \"theta_2\"]\n", + "theta_perm = theta.loc[t_order] # ``theta`` in the order we want\n", + "cov_perm = cov.loc[t_order, t_order] # ``covariance`` matrix in the order we want\n", + "\n", + "print(\"\\ntheta values:\")\n", + "print(theta_perm)\n", + "\n", + "print(\"\\nSSE value: \", obj)\n", + "\n", + "print(\"\\nCovariance matrix:\")\n", + "print(cov_perm)" + ] + }, + { + "cell_type": "markdown", + "id": "fe378b08", + "metadata": {}, + "source": [ + "### Manual multistart test\n", + "\n", + "Made a for loop version of multistart, to see expected results from solver being accessed each time. Sobol sampling, n = 50, seed = 12345" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "5a2cc375", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ipopt 3.13.2: bound_relax_factor=0\n", + "honor_original_bounds=no\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 8.33e+03 9.73e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 1.9834030e+07 2.32e+03 1.15e+05 -1.0 9.25e+03 - 2.54e-01 1.00e+00f 1\n", + " 2 2.6443991e+07 1.71e+02 1.35e+04 -1.0 9.35e+01 - 8.83e-01 1.00e+00h 1\n", + " 3 2.7180925e+07 1.23e+00 3.23e+02 -1.0 1.06e+01 - 1.00e+00 1.00e+00h 1\n", + " 4 2.7186293e+07 5.91e-05 4.09e-02 -1.0 7.54e-02 - 1.00e+00 1.00e+00h 1\n", + " 5 2.7186294e+07 3.89e-12 1.48e-10 -2.5 1.72e-05 - 1.00e+00 1.00e+00h 1\n", + " 6 2.7186294e+07 5.57e-12 6.31e-10 -3.8 5.65e-07 - 1.00e+00 1.00e+00h 1\n", + " 7 2.7186294e+07 4.69e-12 1.10e-09 -5.7 3.14e-08 - 1.00e+00 1.00e+00h 1\n", + " 8 2.7186294e+07 4.12e-12 7.61e-10 -8.6 3.90e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 8\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 1.4055363392027403e+05 2.7186293738994990e+07\n", + "Dual infeasibility......: 7.6089379903002177e-10 1.4717429743825483e-07\n", + "Constraint violation....: 7.7375360819269268e-14 4.1211478674085811e-12\n", + "Complementarity.........: 2.5059035597865472e-09 4.8469917395798797e-07\n", + "Overall NLP error.......: 2.5059035597865472e-09 4.8469917395798797e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 9\n", + "Number of objective gradient evaluations = 9\n", + "Number of equality constraint evaluations = 9\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 9\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 8\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.005\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Applications/anaconda3/envs/parmest-dev-mac2/lib/python3.13/site-packages/scipy/stats/_qmc.py:993: UserWarning: The balance properties of Sobol' points require n to be a power of 2.\n", + " sample = self._random(n, workers=workers)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ipopt 3.13.2: bound_relax_factor=0\n", + "honor_original_bounds=no\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 2.15e+04 9.85e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 9.7588988e+06 2.78e+03 5.57e+04 -1.0 9.41e+03 - 1.46e-02 1.00e+00f 1\n", + " 2 2.6386162e+07 2.49e+02 1.19e+04 -1.0 2.49e+02 - 9.29e-01 1.00e+00h 1\n", + " 3 2.7175624e+07 2.55e+00 5.81e+02 -1.0 1.51e+01 - 1.00e+00 1.00e+00h 1\n", + " 4 2.7186293e+07 2.61e-04 1.56e-01 -1.0 1.51e-01 - 1.00e+00 1.00e+00h 1\n", + " 5 2.7186294e+07 5.40e-12 3.64e-09 -2.5 3.02e-05 - 1.00e+00 1.00e+00h 1\n", + " 6 2.7186294e+07 4.41e-12 1.03e-09 -3.8 5.65e-07 - 1.00e+00 1.00e+00h 1\n", + " 7 2.7186294e+07 4.69e-12 1.23e-09 -5.7 3.14e-08 - 1.00e+00 1.00e+00h 1\n", + " 8 2.7186294e+07 4.12e-12 5.64e-10 -8.6 3.90e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 8\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 1.4055363392027401e+05 2.7186293738994986e+07\n", + "Dual infeasibility......: 5.6444294344696339e-10 1.0917620008427717e-07\n", + "Constraint violation....: 6.8047436072523522e-14 4.1211478674085811e-12\n", + "Complementarity.........: 2.5059035597865472e-09 4.8469917395798797e-07\n", + "Overall NLP error.......: 2.5059035597865472e-09 4.8469917395798797e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 9\n", + "Number of objective gradient evaluations = 9\n", + "Number of equality constraint evaluations = 9\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 9\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 8\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.005\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", + "Ipopt 3.13.2: bound_relax_factor=0\n", + "honor_original_bounds=no\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 1.10e+03 1.06e+02 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 2.2271200e+09 1.06e+04 5.22e+06 -1.0 1.41e+04 - 1.55e-01 2.75e-01f 1\n", + " 2 4.9620454e+06 1.15e+03 1.42e+06 -1.0 6.05e+03 - 1.49e-02 1.00e+00f 1\n", + " 3 4.3909756e+05 6.85e+01 8.18e+04 -1.0 3.03e+02 - 9.74e-01 1.00e+00f 1\n", + " 4 4.2816904e+05 2.93e-01 4.24e+02 -1.0 1.76e+01 - 1.00e+00 1.00e+00f 1\n", + " 5 4.2816330e+05 6.80e-06 1.39e-02 -1.0 1.19e-01 - 1.00e+00 1.00e+00h 1\n", + " 6 4.2816330e+05 5.03e-12 1.55e-10 -2.5 2.53e-05 - 1.00e+00 1.00e+00h 1\n", + " 7 4.2816330e+05 4.18e-12 4.43e-12 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", + " 8 4.2816330e+05 5.09e-12 2.61e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", + " 9 4.2816330e+05 3.50e-12 6.22e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 9\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 2.2136120434349154e+03 4.2816329651878146e+05\n", + "Dual infeasibility......: 6.2153811273360234e-12 1.2021971422198111e-09\n", + "Constraint violation....: 3.4631683699249438e-13 3.4958702599396929e-12\n", + "Complementarity.........: 2.5059035597864302e-09 4.8469917395796542e-07\n", + "Overall NLP error.......: 2.5059035597864302e-09 4.8469917395796542e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 10\n", + "Number of objective gradient evaluations = 10\n", + "Number of equality constraint evaluations = 10\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 10\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 9\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.005\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", + "Ipopt 3.13.2: bound_relax_factor=0\n", + "honor_original_bounds=no\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 1.07e+03 8.68e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 4.4414889e+09 1.02e+03 2.34e+05 -1.0 1.25e+04 - 2.26e-01 5.25e-02f 1\n", + " 2 4.4306624e+09 1.02e+03 2.34e+05 -1.0 7.54e+03 - 9.36e-01 1.31e-03f 1\n", + " 3 4.4294973e+09 1.02e+03 6.00e+06 -1.0 3.07e+03 - 1.00e+00 6.02e-04f 1\n", + " 4 3.0552690e+09 9.12e+02 1.82e+07 -1.0 2.98e+03 - 1.00e+00 9.96e-01f 1\n", + " 5 1.5613347e+09 1.89e+03 3.20e+07 -1.0 6.63e+03 -2.0 4.47e-01 1.00e+00f 1\n", + " 6 1.0074442e+09 2.86e+01 4.84e+06 -1.0 1.43e+03 - 1.37e-02 1.00e+00f 1\n", + " 7 9.7365090e+08 1.54e+00 4.43e+06 -1.0 2.29e+02 - 1.36e-04 1.00e+00f 1\n", + " 8 1.4779113e+08 2.15e+03 2.83e+07 -1.0 3.47e+03 -1.6 1.90e-02 1.00e+00f 1\n", + " 9 2.9134338e+06 2.92e+01 1.03e+06 -1.0 1.61e+03 - 1.00e+00 1.00e+00f 1\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 10 4.2930909e+05 2.52e+00 2.09e+04 -1.0 2.46e+02 - 1.00e+00 1.00e+00f 1\n", + " 11 4.2816335e+05 2.53e-03 1.42e+01 -1.0 5.43e+00 - 1.00e+00 1.00e+00f 1\n", + " 12 4.2816330e+05 1.59e-09 7.66e-06 -1.0 3.76e-03 - 1.00e+00 1.00e+00h 1\n", + " 13 4.2816330e+05 3.21e-12 2.17e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", + " 14 4.2816330e+05 4.18e-12 2.30e-03 -3.8 7.13e-07 - 1.00e+00 5.00e-01h 2\n", + " 15 4.2816330e+05 4.01e-12 1.68e-11 -3.8 3.56e-07 - 1.00e+00 1.00e+00h 1\n", + " 16 4.2816330e+05 5.09e-12 2.77e-12 -5.7 3.95e-08 - 1.00e+00 1.00e+00h 1\n", + " 17 4.2816330e+05 3.50e-12 9.85e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 17\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", + "Dual infeasibility......: 9.8533599348512529e-12 1.9058656118194543e-09\n", + "Constraint violation....: 2.9162136132700857e-13 3.4958702599396929e-12\n", + "Complementarity.........: 2.5059035597866419e-09 4.8469917395800629e-07\n", + "Overall NLP error.......: 2.5059035597866419e-09 4.8469917395800629e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 20\n", + "Number of objective gradient evaluations = 18\n", + "Number of equality constraint evaluations = 20\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 18\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 17\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.010\n", + "Total CPU secs in NLP function evaluations = 0.002\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", + "Ipopt 3.13.2: bound_relax_factor=0\n", + "honor_original_bounds=no\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 9.72e+03 9.63e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 2.6711997e+07 4.94e+01 4.67e+03 -1.0 9.15e+03 - 8.25e-01 1.00e+00f 1\n", + " 2 2.7185876e+07 1.30e-01 3.12e+01 -1.0 5.38e+00 - 1.00e+00 1.00e+00h 1\n", + " 3 2.7186294e+07 8.26e-07 2.97e-04 -1.0 7.99e-03 - 1.00e+00 1.00e+00h 1\n", + " 4 2.7186294e+07 3.41e-12 2.29e-10 -2.5 2.05e-05 - 1.00e+00 1.00e+00h 1\n", + " 5 2.7186294e+07 4.41e-12 4.78e-10 -3.8 5.65e-07 - 1.00e+00 1.00e+00h 1\n", + " 6 2.7186294e+07 3.67e-12 9.05e-10 -5.7 3.14e-08 - 1.00e+00 1.00e+00h 1\n", + " 7 2.7186294e+07 4.12e-12 2.65e-10 -8.6 3.89e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 7\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 1.4055363392027403e+05 2.7186293738994990e+07\n", + "Dual infeasibility......: 2.6501622456984170e-10 5.1260210965743845e-08\n", + "Constraint violation....: 4.6417226757976225e-14 4.1211478674085811e-12\n", + "Complementarity.........: 2.5059035597868338e-09 4.8469917395804345e-07\n", + "Overall NLP error.......: 2.5059035597868338e-09 4.8469917395804345e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 8\n", + "Number of objective gradient evaluations = 8\n", + "Number of equality constraint evaluations = 8\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 8\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 7\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", + "Ipopt 3.13.2: bound_relax_factor=0\n", + "honor_original_bounds=no\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 2.37e+03 9.51e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 5.2544727e+08 1.26e+04 4.35e+05 -1.0 9.02e+03 - 2.75e-01 6.86e-01f 1\n", + " 2 2.2794582e+07 1.25e+03 9.75e+04 -1.0 2.97e+03 - 2.39e-02 1.00e+00f 1\n", + " 3 2.7074814e+07 3.89e+01 6.86e+03 -1.0 5.61e+01 - 1.00e+00 1.00e+00h 1\n", + " 4 2.7186179e+07 4.02e-02 1.85e+01 -1.0 1.74e+00 - 1.00e+00 1.00e+00h 1\n", + " 5 2.7186294e+07 3.87e-08 4.24e-05 -1.0 3.08e-03 - 1.00e+00 1.00e+00h 1\n", + " 6 2.7186294e+07 4.58e-12 3.65e-10 -2.5 2.05e-05 - 1.00e+00 1.00e+00h 1\n", + " 7 2.7186294e+07 4.41e-12 3.77e-10 -3.8 5.65e-07 - 1.00e+00 1.00e+00h 1\n", + " 8 2.7186294e+07 4.21e-12 9.78e-10 -5.7 3.14e-08 - 1.00e+00 1.00e+00h 1\n", + " 9 2.7186294e+07 4.12e-12 7.89e-10 -8.6 3.89e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 9\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 1.4055363392027401e+05 2.7186293738994986e+07\n", + "Dual infeasibility......: 7.8853941762349164e-10 1.5252159360353654e-07\n", + "Constraint violation....: 1.8412683274148882e-13 4.1211478674085811e-12\n", + "Complementarity.........: 2.5059035597868338e-09 4.8469917395804345e-07\n", + "Overall NLP error.......: 2.5059035597868338e-09 4.8469917395804345e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 10\n", + "Number of objective gradient evaluations = 10\n", + "Number of equality constraint evaluations = 10\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 10\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 9\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.005\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", + "Ipopt 3.13.2: bound_relax_factor=0\n", + "honor_original_bounds=no\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 8.36e+03 1.01e+02 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 3.6567139e+06 2.70e+03 6.69e+04 -1.0 9.84e+03 - 3.42e-01 1.00e+00f 1\n", + " 2 4.3352737e+05 1.69e+02 6.65e+03 -1.0 1.86e+02 - 8.09e-01 1.00e+00f 1\n", + " 3 4.2816873e+05 8.05e-01 4.30e+01 -1.0 7.01e+00 - 1.00e+00 1.00e+00f 1\n", + " 4 4.2816330e+05 1.82e-05 3.52e-03 -1.0 3.07e-02 - 1.00e+00 1.00e+00h 1\n", + " 5 4.2816330e+05 3.69e-12 7.46e-11 -2.5 2.58e-05 - 1.00e+00 1.00e+00h 1\n", + " 6 4.2816330e+05 4.18e-12 7.82e-12 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", + " 7 4.2816330e+05 5.09e-12 1.10e-11 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", + " 8 4.2816330e+05 3.50e-12 2.88e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 8\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 2.2136120434349154e+03 4.2816329651878146e+05\n", + "Dual infeasibility......: 2.8795658903932589e-12 5.5697403157450258e-10\n", + "Constraint violation....: 9.8520816457861569e-14 3.4958702599396929e-12\n", + "Complementarity.........: 2.5059035597864302e-09 4.8469917395796542e-07\n", + "Overall NLP error.......: 2.5059035597864302e-09 4.8469917395796542e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 9\n", + "Number of objective gradient evaluations = 9\n", + "Number of equality constraint evaluations = 9\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 9\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 8\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.005\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", + "Ipopt 3.13.2: bound_relax_factor=0\n", + "honor_original_bounds=no\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 8.43e+03 1.01e+02 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 1.1035343e+07 5.64e+03 1.01e+06 -1.0 9.86e+03 - 1.52e-01 1.00e+00f 1\n", + " 2 7.7062599e+05 7.40e+02 1.98e+05 -1.0 4.05e+02 - 6.58e-01 1.00e+00f 1\n", + " 3 4.2910167e+05 2.42e+01 8.77e+03 -1.0 8.67e+01 - 1.00e+00 1.00e+00f 1\n", + " 4 4.2816393e+05 3.08e-02 1.65e+01 -1.0 3.60e+00 - 1.00e+00 1.00e+00h 1\n", + " 5 4.2816330e+05 5.42e-08 4.48e-05 -1.0 5.94e-03 - 1.00e+00 1.00e+00h 1\n", + " 6 4.2816330e+05 3.89e-12 2.24e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", + " 7 4.2816330e+05 5.31e-12 1.87e-12 -3.8 7.13e-07 - 1.00e+00 1.00e+00H 1\n", + " 8 4.2816330e+05 3.04e-12 1.45e-11 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", + " 9 4.2816330e+05 3.50e-12 2.62e-11 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 9\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", + "Dual infeasibility......: 2.6224264566340445e-11 5.0723737245673013e-09\n", + "Constraint violation....: 1.6351447811689176e-13 3.4958702599396925e-12\n", + "Complementarity.........: 2.5059035597864302e-09 4.8469917395796542e-07\n", + "Overall NLP error.......: 2.5059035597864302e-09 4.8469917395796542e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 11\n", + "Number of objective gradient evaluations = 10\n", + "Number of equality constraint evaluations = 11\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 10\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 9\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.005\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", + "Ipopt 3.13.2: bound_relax_factor=0\n", + "honor_original_bounds=no\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 8.08e+02 9.23e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 3.5436370e+08 1.58e+04 3.23e+06 -1.0 9.13e+03 - 4.82e-01 7.24e-01f 1\n", + " 2 3.0476400e+07 1.98e+03 6.74e+05 -1.0 2.56e+03 - 3.12e-02 1.00e+00f 1\n", + " 3 2.7331790e+07 9.12e+01 5.74e+04 -1.0 5.85e+01 - 1.00e+00 1.00e+00f 1\n", + " 4 2.7186712e+07 2.48e-01 3.36e+02 -1.0 7.25e+00 - 1.00e+00 1.00e+00f 1\n", + " 5 2.7186294e+07 3.56e-06 7.20e-03 -1.0 4.95e-02 - 1.00e+00 1.00e+00f 1\n", + " 6 2.7186294e+07 3.58e-12 1.45e-10 -2.5 2.01e-05 - 1.00e+00 1.00e+00h 1\n", + " 7 2.7186294e+07 4.41e-12 2.03e-10 -3.8 5.65e-07 - 1.00e+00 1.00e+00h 1\n", + " 8 2.7186294e+07 4.69e-12 2.62e-10 -5.7 3.14e-08 - 1.00e+00 1.00e+00h 1\n", + " 9 2.7186294e+07 4.12e-12 7.32e-10 -8.6 3.90e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 9\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 1.4055363392027401e+05 2.7186293738994986e+07\n", + "Dual infeasibility......: 7.3178996857371158e-10 1.4154494968219492e-07\n", + "Constraint violation....: 1.6008713042790100e-13 4.1211478674085811e-12\n", + "Complementarity.........: 2.5059035597865472e-09 4.8469917395798797e-07\n", + "Overall NLP error.......: 2.5059035597865472e-09 4.8469917395798797e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 10\n", + "Number of objective gradient evaluations = 10\n", + "Number of equality constraint evaluations = 10\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 10\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 9\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.005\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", + "Ipopt 3.13.2: bound_relax_factor=0\n", + "honor_original_bounds=no\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 1.21e+04 9.67e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 2.3904826e+07 7.92e+02 1.65e+04 -1.0 9.20e+03 - 6.85e-01 1.00e+00f 1\n", + " 2 2.7078421e+07 2.47e+01 2.40e+03 -1.0 4.93e+01 - 1.00e+00 1.00e+00h 1\n", + " 3 2.7186180e+07 2.61e-02 8.33e+00 -1.0 1.57e+00 - 1.00e+00 1.00e+00h 1\n", + " 4 2.7186294e+07 2.61e-08 2.15e-05 -1.0 1.65e-03 - 1.00e+00 1.00e+00h 1\n", + " 5 2.7186294e+07 3.58e-12 2.84e-10 -2.5 2.05e-05 - 1.00e+00 1.00e+00h 1\n", + " 6 2.7186294e+07 4.41e-12 8.12e-10 -3.8 5.65e-07 - 1.00e+00 1.00e+00h 1\n", + " 7 2.7186294e+07 4.69e-12 1.17e-10 -5.7 3.14e-08 - 1.00e+00 1.00e+00h 1\n", + " 8 2.7186294e+07 4.12e-12 6.44e-10 -8.6 3.90e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 8\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 1.4055363392027401e+05 2.7186293738994986e+07\n", + "Dual infeasibility......: 6.4447847720351047e-10 1.2465690641376951e-07\n", + "Constraint violation....: 5.4622972811557702e-14 4.1211478674085811e-12\n", + "Complementarity.........: 2.5059035597865472e-09 4.8469917395798797e-07\n", + "Overall NLP error.......: 2.5059035597865472e-09 4.8469917395798797e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 9\n", + "Number of objective gradient evaluations = 9\n", + "Number of equality constraint evaluations = 9\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 9\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 8\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.005\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", + "Ipopt 3.13.2: bound_relax_factor=0\n", + "honor_original_bounds=no\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 2.11e+03 1.00e+02 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 4.8316028e+09 2.06e+03 8.87e+03 -1.0 6.09e+03 - 3.06e-01 2.48e-02f 1\n", + " 2 4.8286802e+09 2.06e+03 2.41e+04 -1.0 6.72e+03 - 6.43e-01 3.26e-04f 1\n", + " 3 4.8221153e+09 2.05e+03 5.20e+06 -1.0 1.77e+03 - 1.00e+00 4.84e-03f 1\n", + " 4 3.6291264e+09 1.18e+01 2.25e+06 -1.0 1.75e+03 - 1.00e+00 1.00e+00f 1\n", + " 5 3.6164199e+09 1.93e-02 1.33e+04 -1.0 1.77e+01 0.0 1.00e+00 1.00e+00f 1\n", + " 6 3.6142797e+09 9.44e-02 3.42e+04 -1.0 2.49e+01 -0.5 1.00e+00 1.00e+00f 1\n", + " 7 3.6063319e+09 1.26e+00 6.91e+03 -1.0 9.04e+01 -1.0 1.00e+00 1.00e+00f 1\n", + " 8 3.5609956e+09 4.91e+01 2.62e+05 -1.0 5.47e+02 -1.4 1.00e+00 1.00e+00f 1\n", + " 9 3.4536087e+09 1.18e+01 3.09e+05 -1.0 2.54e+02 -1.0 1.00e+00 1.00e+00f 1\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 10 3.1279033e+09 4.24e+02 2.06e+06 -1.0 1.38e+03 -1.5 1.00e+00 1.00e+00f 1\n", + " 11 3.4934385e+09 1.21e+02 2.69e+07 -1.0 1.18e+04 -2.0 3.42e-01 5.36e-02H 1\n", + " 12 2.9025111e+09 9.96e+02 5.96e+06 -1.0 2.40e+03 -1.5 1.00e+00 1.00e+00f 1\n", + " 13 3.0988933e+09 8.23e+02 3.70e+07 -1.0 3.37e+04 - 3.57e-01 1.95e-02H 1\n", + " 14 1.4289790e+09 1.38e+03 1.82e+07 -1.0 5.41e+03 -2.0 7.49e-01 1.00e+00f 1\n", + " 15 9.1847716e+08 2.03e+00 4.74e+06 -1.0 1.66e+03 - 6.80e-02 1.00e+00f 1\n", + " 16 9.1753645e+08 3.84e-02 4.80e+06 -1.0 3.84e+01 - 7.33e-04 1.00e+00f 1\n", + " 17 2.6734760e+08 8.34e+02 1.25e+07 -1.0 2.22e+03 -1.6 4.98e-04 1.00e+00f 1\n", + " 18 3.1392273e+07 4.23e+01 1.36e+06 -1.0 1.71e+03 - 9.07e-01 1.00e+00f 1\n", + " 19 2.7168841e+07 4.01e+00 3.90e+04 -1.0 3.38e+02 - 1.00e+00 1.00e+00f 1\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 20 2.7186259e+07 7.87e-03 4.66e+01 -1.0 9.43e+00 - 1.00e+00 1.00e+00h 1\n", + " 21 2.7186294e+07 1.60e-08 7.92e-05 -1.0 1.13e-02 - 1.00e+00 1.00e+00h 1\n", + " 22 2.7186294e+07 4.09e-12 4.46e-10 -2.5 2.05e-05 - 1.00e+00 1.00e+00h 1\n", + " 23 2.7186294e+07 4.41e-12 1.77e-10 -3.8 5.65e-07 - 1.00e+00 1.00e+00h 1\n", + " 24 2.7186294e+07 4.69e-12 4.44e-10 -5.7 3.14e-08 - 1.00e+00 1.00e+00h 1\n", + " 25 2.7186294e+07 4.12e-12 9.32e-10 -8.6 3.90e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 25\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 1.4055363392027403e+05 2.7186293738994990e+07\n", + "Dual infeasibility......: 9.3170592537955412e-10 1.8021327701920809e-07\n", + "Constraint violation....: 2.8906604310374570e-13 4.1211478674085811e-12\n", + "Complementarity.........: 2.5059035597865472e-09 4.8469917395798797e-07\n", + "Overall NLP error.......: 2.5059035597865472e-09 4.8469917395798797e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 28\n", + "Number of objective gradient evaluations = 26\n", + "Number of equality constraint evaluations = 28\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 26\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 25\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.018\n", + "Total CPU secs in NLP function evaluations = 0.003\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", + "Ipopt 3.13.2: bound_relax_factor=0\n", + "honor_original_bounds=no\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 3.71e+03 1.00e+02 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 3.6253900e+06 7.03e+03 4.94e+05 -1.0 9.73e+03 - 5.66e-01 1.00e+00f 1\n", + " 2 6.3155920e+05 9.68e+02 1.03e+05 -1.0 2.11e+02 - 3.06e-01 1.00e+00f 1\n", + " 3 4.2943835e+05 3.66e+01 6.18e+03 -1.0 6.27e+01 - 1.00e+00 1.00e+00f 1\n", + " 4 4.2816462e+05 6.43e-02 1.50e+01 -1.0 3.60e+00 - 1.00e+00 1.00e+00h 1\n", + " 5 4.2816330e+05 2.08e-07 6.53e-05 -1.0 7.59e-03 - 1.00e+00 1.00e+00h 1\n", + " 6 4.2816330e+05 3.92e-12 2.10e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", + " 7 4.2816330e+05 4.18e-12 3.09e-12 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", + " 8 4.2816330e+05 5.09e-12 6.41e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", + " 9 4.2816330e+05 3.50e-12 4.40e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 9\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", + "Dual infeasibility......: 4.3995938736571737e-12 8.5098227662596132e-10\n", + "Constraint violation....: 1.2311475077887451e-13 3.4958702599396929e-12\n", + "Complementarity.........: 2.5059035597864302e-09 4.8469917395796542e-07\n", + "Overall NLP error.......: 2.5059035597864302e-09 4.8469917395796542e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 10\n", + "Number of objective gradient evaluations = 10\n", + "Number of equality constraint evaluations = 10\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 10\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 9\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.005\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", + "Ipopt 3.13.2: bound_relax_factor=0\n", + "honor_original_bounds=no\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 2.36e+04 9.68e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 2.9411233e+07 2.24e+03 2.14e+04 -1.0 9.12e+03 - 9.31e-02 1.00e+00f 1\n", + " 2 2.7249209e+07 1.10e+02 1.53e+03 -1.0 2.59e+01 - 9.65e-01 1.00e+00f 1\n", + " 3 2.7186190e+07 3.26e-01 3.14e+00 -1.0 1.24e+00 - 1.00e+00 1.00e+00f 1\n", + " 4 2.7186294e+07 2.93e-06 8.94e-05 -1.0 5.33e-03 - 1.00e+00 1.00e+00h 1\n", + " 5 2.7186294e+07 4.32e-12 6.52e-10 -2.5 2.05e-05 - 1.00e+00 1.00e+00h 1\n", + " 6 2.7186294e+07 4.41e-12 6.15e-11 -3.8 5.65e-07 - 1.00e+00 1.00e+00h 1\n", + " 7 2.7186294e+07 4.69e-12 2.62e-10 -5.7 3.14e-08 - 1.00e+00 1.00e+00h 1\n", + " 8 2.7186294e+07 4.12e-12 1.03e-09 -8.6 3.90e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 8\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 1.4055363392027398e+05 2.7186293738994978e+07\n", + "Dual infeasibility......: 1.0262933743643622e-09 1.9850865722675085e-07\n", + "Constraint violation....: 4.3964831775156199e-14 4.1211478674085811e-12\n", + "Complementarity.........: 2.5059035597865472e-09 4.8469917395798797e-07\n", + "Overall NLP error.......: 2.5059035597865472e-09 4.8469917395798797e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 9\n", + "Number of objective gradient evaluations = 9\n", + "Number of equality constraint evaluations = 9\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 9\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 8\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.005\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", + "Ipopt 3.13.2: bound_relax_factor=0\n", + "honor_original_bounds=no\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 5.50e+03 9.77e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 1.5759216e+08 1.17e+04 2.18e+05 -1.0 9.43e+03 - 1.70e-01 8.24e-01f 1\n", + " 2 2.3277921e+07 1.37e+03 9.11e+04 -1.0 1.41e+03 - 3.04e-02 1.00e+00f 1\n", + " 3 2.7051971e+07 4.76e+01 1.10e+03 -1.0 6.63e+01 - 1.00e+00 1.00e+00h 1\n", + " 4 2.7186107e+07 6.48e-02 2.18e+00 -1.0 2.12e+00 - 1.00e+00 1.00e+00h 1\n", + " 5 2.7186294e+07 1.19e-07 1.67e-05 -1.0 2.83e-03 - 1.00e+00 1.00e+00h 1\n", + " 6 2.7186294e+07 4.69e-12 1.19e-09 -2.5 2.05e-05 - 1.00e+00 1.00e+00h 1\n", + " 7 2.7186294e+07 4.41e-12 3.60e-10 -3.8 5.65e-07 - 1.00e+00 1.00e+00h 1\n", + " 8 2.7186294e+07 4.69e-12 2.91e-10 -5.7 3.14e-08 - 1.00e+00 1.00e+00h 1\n", + " 9 2.7186294e+07 4.12e-12 5.86e-10 -8.6 3.90e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 9\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 1.4055363392027401e+05 2.7186293738994986e+07\n", + "Dual infeasibility......: 5.8627081629004306e-10 1.1339821090148592e-07\n", + "Constraint violation....: 1.7015480447604767e-13 4.1211478674085811e-12\n", + "Complementarity.........: 2.5059035597865472e-09 4.8469917395798797e-07\n", + "Overall NLP error.......: 2.5059035597865472e-09 4.8469917395798797e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 10\n", + "Number of objective gradient evaluations = 10\n", + "Number of equality constraint evaluations = 10\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 10\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 9\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.005\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", + "Ipopt 3.13.2: bound_relax_factor=0\n", + "honor_original_bounds=no\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 1.08e+04 9.98e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 9.8648968e+05 4.31e+02 1.25e+04 -1.0 9.71e+03 - 5.64e-01 1.00e+00f 1\n", + " 2 4.2829757e+05 9.37e+00 2.57e+02 -1.0 7.16e+01 - 1.00e+00 1.00e+00f 1\n", + " 3 4.2816336e+05 3.72e-03 4.88e-01 -1.0 2.97e-01 - 1.00e+00 1.00e+00h 1\n", + " 4 4.2816330e+05 5.74e-10 2.35e-07 -1.7 1.82e-04 - 1.00e+00 1.00e+00h 1\n", + " 5 4.2816330e+05 5.51e-12 6.63e-12 -3.8 5.28e-06 - 1.00e+00 1.00e+00h 1\n", + " 6 4.2816330e+05 1.88e-12 2.17e-11 -5.7 3.96e-08 - 1.00e+00 1.00e+00H 1\n", + " 7 4.2816330e+05 3.50e-12 3.72e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 7\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", + "Dual infeasibility......: 3.7174728473274775e-12 7.1904443859138779e-10\n", + "Constraint violation....: 6.7272184213622484e-14 3.4958702599396929e-12\n", + "Complementarity.........: 2.5059035597866419e-09 4.8469917395800629e-07\n", + "Overall NLP error.......: 2.5059035597866419e-09 4.8469917395800629e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 9\n", + "Number of objective gradient evaluations = 8\n", + "Number of equality constraint evaluations = 9\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 8\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 7\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", + "Ipopt 3.13.2: bound_relax_factor=0\n", + "honor_original_bounds=no\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 1.51e+04 9.87e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 4.4591296e+05 4.94e+02 3.70e+03 -1.0 9.66e+03 - 6.72e-01 1.00e+00f 1\n", + " 2 4.2820756e+05 6.55e+00 1.50e+02 -1.0 1.56e+01 - 1.00e+00 1.00e+00f 1\n", + " 3 4.2816331e+05 1.24e-03 2.54e-02 -1.0 2.82e-01 - 1.00e+00 1.00e+00h 1\n", + " 4 4.2816330e+05 4.29e-11 1.22e-08 -2.5 4.22e-05 - 1.00e+00 1.00e+00h 1\n", + " 5 4.2816330e+05 4.49e-12 2.73e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", + " 6 4.2816330e+05 5.09e-12 1.09e-11 -5.7 3.95e-08 - 1.00e+00 1.00e+00h 1\n", + " 7 4.2816330e+05 3.50e-12 1.67e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 7\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 2.2136120434349154e+03 4.2816329651878146e+05\n", + "Dual infeasibility......: 1.6711097683383890e-12 3.2323092448766719e-10\n", + "Constraint violation....: 4.4162799930954544e-14 3.4958702599396929e-12\n", + "Complementarity.........: 2.5059035597866419e-09 4.8469917395800629e-07\n", + "Overall NLP error.......: 2.5059035597866419e-09 4.8469917395800629e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 8\n", + "Number of objective gradient evaluations = 8\n", + "Number of equality constraint evaluations = 8\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 8\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 7\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", + "Ipopt 3.13.2: bound_relax_factor=0\n", + "honor_original_bounds=no\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 3.14e+03 9.52e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 3.2143856e+07 3.08e+03 8.73e+04 -1.0 9.13e+03 - 7.71e-01 1.00e+00f 1\n", + " 2 2.7516639e+07 2.13e+02 1.12e+04 -1.0 4.02e+01 - 8.52e-01 1.00e+00f 1\n", + " 3 2.7187562e+07 1.86e+00 1.48e+02 -1.0 3.04e+00 - 1.00e+00 1.00e+00f 1\n", + " 4 2.7186294e+07 1.48e-04 1.42e-02 -1.0 2.39e-02 - 1.00e+00 1.00e+00f 1\n", + " 5 2.7186294e+07 5.83e-12 4.05e-10 -2.5 1.58e-05 - 1.00e+00 1.00e+00h 1\n", + " 6 2.7186294e+07 4.60e-12 7.46e-10 -3.8 5.65e-07 - 1.00e+00 1.00e+00h 1\n", + " 7 2.7186294e+07 4.69e-12 1.22e-10 -5.7 3.14e-08 - 1.00e+00 1.00e+00h 1\n", + " 8 2.7186294e+07 4.12e-12 6.01e-10 -8.6 3.90e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 8\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 1.4055363392027401e+05 2.7186293738994986e+07\n", + "Dual infeasibility......: 6.0082273151840992e-10 1.1621288477955682e-07\n", + "Constraint violation....: 7.9483276450609387e-14 4.1211478674085811e-12\n", + "Complementarity.........: 2.5059035597865472e-09 4.8469917395798797e-07\n", + "Overall NLP error.......: 2.5059035597865472e-09 4.8469917395798797e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 9\n", + "Number of objective gradient evaluations = 9\n", + "Number of equality constraint evaluations = 9\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 9\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 8\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.005\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", + "Ipopt 3.13.2: bound_relax_factor=0\n", + "honor_original_bounds=no\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 2.03e+04 9.73e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 2.2603904e+07 2.57e+03 2.37e+04 -1.0 9.24e+03 - 2.68e-01 1.00e+00f 1\n", + " 2 2.6435802e+07 1.99e+02 7.37e+03 -1.0 8.27e+01 - 9.14e-01 1.00e+00h 1\n", + " 3 2.7178955e+07 1.68e+00 2.58e+02 -1.0 1.10e+01 - 1.00e+00 1.00e+00h 1\n", + " 4 2.7186293e+07 1.18e-04 5.10e-02 -1.0 1.04e-01 - 1.00e+00 1.00e+00h 1\n", + " 5 2.7186294e+07 4.18e-12 4.94e-10 -2.5 1.35e-05 - 1.00e+00 1.00e+00h 1\n", + " 6 2.7186294e+07 5.57e-12 3.19e-10 -3.8 5.65e-07 - 1.00e+00 1.00e+00h 1\n", + " 7 2.7186294e+07 4.69e-12 3.46e-10 -5.7 3.14e-08 - 1.00e+00 1.00e+00h 1\n", + " 8 2.7186294e+07 4.12e-12 7.83e-10 -8.6 3.90e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 8\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 1.4055363392027401e+05 2.7186293738994986e+07\n", + "Dual infeasibility......: 7.8272167187299556e-10 1.5139630825544308e-07\n", + "Constraint violation....: 5.4622972811557702e-14 4.1211478674085811e-12\n", + "Complementarity.........: 2.5059035597865472e-09 4.8469917395798797e-07\n", + "Overall NLP error.......: 2.5059035597865472e-09 4.8469917395798797e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 9\n", + "Number of objective gradient evaluations = 9\n", + "Number of equality constraint evaluations = 9\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 9\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 8\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.005\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", + "Ipopt 3.13.2: bound_relax_factor=0\n", + "honor_original_bounds=no\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 6.27e+03 1.05e+02 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 2.8071968e+08 1.32e+04 5.04e+06 -1.0 1.14e+04 - 1.01e-01 7.28e-01f 1\n", + " 2 2.1061322e+06 1.58e+03 1.08e+06 -1.0 1.60e+03 - 3.02e-02 1.00e+00f 1\n", + " 3 4.3722926e+05 7.27e+01 7.33e+04 -1.0 2.26e+02 - 1.00e+00 1.00e+00f 1\n", + " 4 4.2816494e+05 2.11e-01 3.44e+02 -1.0 1.68e+01 - 1.00e+00 1.00e+00f 1\n", + " 5 4.2816330e+05 2.17e-06 5.34e-03 -1.0 7.53e-02 - 1.00e+00 1.00e+00h 1\n", + " 6 4.2816330e+05 3.81e-12 1.86e-10 -2.5 2.47e-05 - 1.00e+00 1.00e+00h 1\n", + " 7 4.2816330e+05 5.31e-12 3.37e-12 -3.8 7.13e-07 - 1.00e+00 1.00e+00H 1\n", + " 8 4.2816330e+05 3.04e-12 7.53e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", + " 9 4.2816330e+05 3.50e-12 5.59e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 9\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", + "Dual infeasibility......: 5.5912355767736014e-12 1.0814730897694733e-09\n", + "Constraint violation....: 2.6290081223123707e-13 3.4958702599396925e-12\n", + "Complementarity.........: 2.5059035597864302e-09 4.8469917395796542e-07\n", + "Overall NLP error.......: 2.5059035597864302e-09 4.8469917395796542e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 11\n", + "Number of objective gradient evaluations = 10\n", + "Number of equality constraint evaluations = 11\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 10\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 9\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.005\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", + "Ipopt 3.13.2: bound_relax_factor=0\n", + "honor_original_bounds=no\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 1.08e+03 1.04e+02 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 4.9442460e+09 2.90e+02 1.62e+06 -1.0 8.40e+01 0.0 9.06e-01 1.00e+00f 1\n", + " 2 3.8682267e+09 4.36e+03 3.75e+06 -1.0 7.36e+03 - 2.57e-01 2.22e-01f 1\n", + " 3 1.0445680e+09 2.36e+03 1.30e+07 -1.0 8.06e+03 - 5.91e-02 1.00e+00f 1\n", + " 4 1.8847235e+09 1.86e+03 8.68e+06 -1.0 1.76e+04 - 1.00e+00 1.41e-01H 1\n", + " 5 1.8845015e+09 1.84e+03 8.61e+06 -1.0 2.22e+02 -0.5 1.00e+00 8.89e-03f 1\n", + " 6 1.8845004e+09 1.84e+03 8.61e+06 -1.0 2.48e+02 -1.0 1.00e+00 8.80e-05f 1\n", + " 7 1.0031971e+09 1.86e+01 4.64e+06 -1.0 2.13e+03 - 6.41e-02 1.00e+00f 1\n", + " 8 9.8333743e+08 5.43e-01 4.40e+06 -1.0 1.34e+02 - 7.30e-04 1.00e+00f 1\n", + " 9 3.6753899e+08 8.32e+02 1.39e+07 -1.0 2.13e+03 -1.4 1.80e-02 1.00e+00f 1\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 10 1.6993277e+07 9.18e+01 2.49e+06 -1.0 2.09e+03 - 8.49e-01 1.00e+00f 1\n", + " 11 4.7911844e+05 1.48e+01 1.42e+05 -1.0 6.73e+02 - 1.00e+00 1.00e+00f 1\n", + " 12 4.2816647e+05 1.06e-01 6.23e+02 -1.0 3.67e+01 - 1.00e+00 1.00e+00f 1\n", + " 13 4.2816330e+05 2.92e-06 1.44e-02 -1.0 1.64e-01 - 1.00e+00 1.00e+00h 1\n", + " 14 4.2816330e+05 3.58e-12 1.88e-10 -2.5 2.55e-05 - 1.00e+00 1.00e+00h 1\n", + " 15 4.2816330e+05 4.18e-12 6.06e-12 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", + " 16 4.2816330e+05 5.09e-12 3.46e-12 -5.7 3.95e-08 - 1.00e+00 1.00e+00h 1\n", + " 17 4.2816330e+05 3.50e-12 5.36e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 17\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", + "Dual infeasibility......: 5.3638619013303694e-12 1.0374938104246155e-09\n", + "Constraint violation....: 4.9127553950366949e-13 3.4958702599396933e-12\n", + "Complementarity.........: 2.5059035597864302e-09 4.8469917395796542e-07\n", + "Overall NLP error.......: 2.5059035597864302e-09 4.8469917395796542e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 19\n", + "Number of objective gradient evaluations = 18\n", + "Number of equality constraint evaluations = 19\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 18\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 17\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.011\n", + "Total CPU secs in NLP function evaluations = 0.002\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", + "Ipopt 3.13.2: bound_relax_factor=0\n", + "honor_original_bounds=no\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 1.50e+04 9.72e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 2.1140621e+07 5.69e+02 1.74e+04 -1.0 9.20e+03 - 9.48e-02 1.00e+00f 1\n", + " 2 2.7192445e+07 1.01e+01 6.77e+02 -1.0 6.67e+01 - 1.00e+00 1.00e+00h 1\n", + " 3 2.7186294e+07 2.68e-03 2.93e-01 -1.0 3.18e-01 - 1.00e+00 1.00e+00f 1\n", + " 4 2.7186294e+07 1.79e-10 9.00e-08 -1.7 1.58e-04 - 1.00e+00 1.00e+00h 1\n", + " 5 2.7186294e+07 5.57e-12 6.03e-10 -3.8 4.19e-06 - 1.00e+00 1.00e+00h 1\n", + " 6 2.7186294e+07 4.69e-12 1.24e-09 -5.7 3.14e-08 - 1.00e+00 1.00e+00h 1\n", + " 7 2.7186294e+07 4.12e-12 7.28e-10 -8.6 3.90e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 7\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 1.4055363392027401e+05 2.7186293738994986e+07\n", + "Dual infeasibility......: 7.2797911218231232e-10 1.4080784272619497e-07\n", + "Constraint violation....: 5.4978276510060899e-14 4.1211478674085811e-12\n", + "Complementarity.........: 2.5059035597865472e-09 4.8469917395798797e-07\n", + "Overall NLP error.......: 2.5059035597865472e-09 4.8469917395798797e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 8\n", + "Number of objective gradient evaluations = 8\n", + "Number of equality constraint evaluations = 8\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 8\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 7\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", + "Ipopt 3.13.2: bound_relax_factor=0\n", + "honor_original_bounds=no\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 1.70e+03 1.00e+02 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 4.6387946e+09 1.63e+03 2.20e+04 -1.0 7.94e+03 - 2.58e-01 4.56e-02f 1\n", + " 2 4.6312518e+09 1.62e+03 2.19e+04 -1.0 8.01e+03 - 7.47e-01 8.57e-04f 1\n", + " 3 4.6299809e+09 1.62e+03 6.03e+06 -1.0 1.30e+03 - 1.00e+00 9.08e-04f 1\n", + " 4 3.4045767e+09 1.71e+02 8.63e+06 -1.0 1.24e+03 - 1.00e+00 1.00e+00f 1\n", + " 5 3.2357580e+09 1.04e-06 1.04e+07 -1.0 1.71e+02 0.0 3.67e-01 1.00e+00f 1\n", + " 6 3.2335364e+09 7.43e-03 1.67e+07 -1.0 5.07e+02 - 1.00e+00 1.34e-02f 1\n", + " 7 3.0814424e+09 4.30e+01 1.10e+07 -1.0 5.19e+02 - 1.00e+00 1.00e+00f 1\n", + " 8 3.0074467e+09 1.00e+00 2.28e+05 -1.0 1.35e+02 -0.5 1.00e+00 1.00e+00f 1\n", + " 9 2.8905662e+09 1.28e+01 3.92e+05 -1.0 3.38e+02 -1.0 1.00e+00 1.00e+00f 1\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 10 2.4797474e+09 1.64e+02 1.29e+06 -1.0 1.31e+03 -1.4 1.00e+00 1.00e+00f 1\n", + " 11 2.7418252e+09 5.14e+01 1.73e+07 -1.0 8.37e+04 - 2.44e-01 2.23e-02H 1\n", + " 12 1.9145132e+09 1.64e+03 9.70e+06 -1.0 4.53e+03 -1.9 7.97e-01 1.00e+00f 1\n", + " 13 1.0117309e+09 5.41e+01 4.54e+06 -1.0 3.05e+03 - 3.70e-02 1.00e+00f 1\n", + " 14 9.9642132e+08 4.47e-01 4.35e+06 -1.0 8.39e+01 - 4.55e-04 1.00e+00f 1\n", + " 15 2.6824003e+08 1.38e+03 2.06e+07 -1.0 2.71e+03 -1.5 1.25e-03 1.00e+00f 1\n", + " 16 3.7706102e+06 2.82e+01 1.15e+06 -1.0 1.91e+03 - 1.00e+00 1.00e+00f 1\n", + " 17 4.3043555e+05 3.35e+00 2.95e+04 -1.0 2.95e+02 - 1.00e+00 1.00e+00f 1\n", + " 18 4.2816340e+05 4.89e-03 2.80e+01 -1.0 7.70e+00 - 1.00e+00 1.00e+00f 1\n", + " 19 4.2816330e+05 6.04e-09 2.94e-05 -1.0 7.38e-03 - 1.00e+00 1.00e+00h 1\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 20 4.2816330e+05 3.69e-12 1.91e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", + " 21 4.2816330e+05 4.18e-12 4.47e-12 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", + " 22 4.2816330e+05 1.88e-12 2.88e-12 -5.7 3.95e-08 - 1.00e+00 1.00e+00H 1\n", + " 23 4.2816330e+05 3.50e-12 6.44e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 23\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 2.2136120434349154e+03 4.2816329651878146e+05\n", + "Dual infeasibility......: 6.4351411026732109e-12 1.2447037574879725e-09\n", + "Constraint violation....: 3.8099280283971188e-13 3.4958702599396929e-12\n", + "Complementarity.........: 2.5059035597864302e-09 4.8469917395796542e-07\n", + "Overall NLP error.......: 2.5059035597864302e-09 4.8469917395796542e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 26\n", + "Number of objective gradient evaluations = 24\n", + "Number of equality constraint evaluations = 26\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 24\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 23\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.015\n", + "Total CPU secs in NLP function evaluations = 0.003\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", + "Ipopt 3.13.2: bound_relax_factor=0\n", + "honor_original_bounds=no\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 5.14e+03 9.96e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 5.1361724e+05 1.16e+03 5.52e+03 -1.0 9.68e+03 - 7.33e-01 1.00e+00f 1\n", + " 2 4.2850802e+05 3.35e+01 5.98e+02 -1.0 3.02e+01 - 1.00e+00 1.00e+00f 1\n", + " 3 4.2816351e+05 3.18e-02 2.65e+00 -1.0 1.13e+00 - 1.00e+00 1.00e+00h 1\n", + " 4 4.2816330e+05 2.81e-08 6.99e-06 -1.0 1.21e-03 - 1.00e+00 1.00e+00h 1\n", + " 5 4.2816330e+05 3.47e-12 2.02e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", + " 6 4.2816330e+05 5.31e-12 1.01e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00H 1\n", + " 7 4.2816330e+05 3.04e-12 1.11e-11 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", + " 8 4.2816330e+05 3.50e-12 7.58e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 8\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", + "Dual infeasibility......: 7.5828253298624225e-12 1.4666921874539710e-09\n", + "Constraint violation....: 8.0852985719428312e-14 3.4958702599396929e-12\n", + "Complementarity.........: 2.5059035597864302e-09 4.8469917395796542e-07\n", + "Overall NLP error.......: 2.5059035597864302e-09 4.8469917395796542e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 10\n", + "Number of objective gradient evaluations = 9\n", + "Number of equality constraint evaluations = 10\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 9\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 8\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.005\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", + "Ipopt 3.13.2: bound_relax_factor=0\n", + "honor_original_bounds=no\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 8.79e+03 9.90e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 4.3423860e+05 1.42e+02 3.82e+03 -1.0 9.65e+03 - 9.02e-01 1.00e+00f 1\n", + " 2 4.2817922e+05 8.43e-01 1.02e+02 -1.0 4.91e+00 - 1.00e+00 1.00e+00f 1\n", + " 3 4.2816330e+05 2.99e-05 1.09e-02 -1.0 4.22e-02 - 1.00e+00 1.00e+00h 1\n", + " 4 4.2816330e+05 3.18e-12 1.14e-10 -2.5 2.42e-05 - 1.00e+00 1.00e+00h 1\n", + " 5 4.2816330e+05 2.76e-12 2.30e-03 -3.8 7.13e-07 - 1.00e+00 5.00e-01h 2\n", + " 6 4.2816330e+05 3.69e-12 1.15e-03 -3.8 3.56e-07 - 1.00e+00 5.00e-01h 2\n", + " 7 4.2816330e+05 4.18e-12 2.30e-11 -5.7 2.18e-07 - 1.00e+00 1.00e+00H 1\n", + " 8 4.2816330e+05 3.50e-12 1.89e-11 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 8\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 2.2136120434349154e+03 4.2816329651878146e+05\n", + "Dual infeasibility......: 1.8948306952157019e-11 3.6650367855318503e-09\n", + "Constraint violation....: 5.8616380029777494e-14 3.4958702599396925e-12\n", + "Complementarity.........: 2.5059035597864302e-09 4.8469917395796542e-07\n", + "Overall NLP error.......: 2.5059035597864302e-09 4.8469917395796542e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 14\n", + "Number of objective gradient evaluations = 9\n", + "Number of equality constraint evaluations = 14\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 9\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 8\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.005\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", + "Ipopt 3.13.2: bound_relax_factor=0\n", + "honor_original_bounds=no\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 2.01e+03 1.03e+02 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 3.1611112e+09 1.31e+04 1.30e+07 -1.0 5.97e+03 -2.0 8.06e-02 3.16e-01f 1\n", + " 2 1.7336542e+07 2.79e+03 2.12e+06 -1.0 7.56e+03 - 1.17e-02 1.00e+00f 1\n", + " 3 2.7661414e+07 1.95e+02 2.35e+05 -1.0 7.82e+02 - 8.45e-01 1.00e+00h 1\n", + " 4 2.7187779e+07 1.42e+00 3.85e+03 -1.0 6.38e+01 - 1.00e+00 1.00e+00f 1\n", + " 5 2.7186293e+07 2.57e-04 9.05e-01 -1.0 1.01e+00 - 1.00e+00 1.00e+00f 1\n", + " 6 2.7186294e+07 1.00e-11 4.03e-08 -1.7 2.25e-04 - 1.00e+00 1.00e+00h 1\n", + " 7 2.7186294e+07 3.52e-12 6.33e-10 -3.8 4.19e-06 - 1.00e+00 1.00e+00h 1\n", + " 8 2.7186294e+07 4.69e-12 1.07e-09 -5.7 3.14e-08 - 1.00e+00 1.00e+00h 1\n", + " 9 2.7186294e+07 4.12e-12 7.17e-10 -8.6 3.90e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 9\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 1.4055363392027401e+05 2.7186293738994986e+07\n", + "Dual infeasibility......: 7.1723805334492121e-10 1.3873027580404212e-07\n", + "Constraint violation....: 3.9907987116676576e-13 4.1211478674085811e-12\n", + "Complementarity.........: 2.5059035597865472e-09 4.8469917395798797e-07\n", + "Overall NLP error.......: 2.5059035597865472e-09 4.8469917395798797e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 10\n", + "Number of objective gradient evaluations = 10\n", + "Number of equality constraint evaluations = 10\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 10\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 9\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.006\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", + "Ipopt 3.13.2: bound_relax_factor=0\n", + "honor_original_bounds=no\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 9.28e+03 9.77e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 8.7268227e+06 3.80e+03 3.76e+04 -1.0 9.35e+03 - 2.32e-01 1.00e+00f 1\n", + " 2 2.6617881e+07 3.09e+02 1.04e+04 -1.0 2.12e+02 - 7.03e-01 1.00e+00h 1\n", + " 3 2.7179492e+07 2.60e+00 1.35e+02 -1.0 1.25e+01 - 1.00e+00 1.00e+00h 1\n", + " 4 2.7186293e+07 1.89e-04 3.26e-02 -1.0 1.05e-01 - 1.00e+00 1.00e+00h 1\n", + " 5 2.7186294e+07 4.97e-12 8.21e-10 -2.5 2.46e-05 - 1.00e+00 1.00e+00h 1\n", + " 6 2.7186294e+07 3.75e-12 3.04e-10 -3.8 5.65e-07 - 1.00e+00 1.00e+00h 1\n", + " 7 2.7186294e+07 4.69e-12 6.58e-10 -5.7 3.14e-08 - 1.00e+00 1.00e+00h 1\n", + " 8 2.7186294e+07 4.12e-12 9.83e-10 -8.6 3.90e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 8\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 1.4055363392027403e+05 2.7186293738994990e+07\n", + "Dual infeasibility......: 9.8263762867883811e-10 1.9006463559245624e-07\n", + "Constraint violation....: 1.0134854702842251e-13 4.1211478674085811e-12\n", + "Complementarity.........: 2.5059035597865472e-09 4.8469917395798797e-07\n", + "Overall NLP error.......: 2.5059035597865472e-09 4.8469917395798797e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 9\n", + "Number of objective gradient evaluations = 9\n", + "Number of equality constraint evaluations = 9\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 9\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 8\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.005\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", + "Ipopt 3.13.2: bound_relax_factor=0\n", + "honor_original_bounds=no\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 4.05e+02 7.12e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 4.2427232e+09 1.39e+03 1.78e+06 -1.0 6.75e+03 -2.0 2.33e-01 1.21e-01f 1\n", + " 2 4.2335006e+09 1.39e+03 1.77e+06 -1.0 1.56e+04 - 3.72e-01 7.80e-04f 1\n", + " 3 4.2319049e+09 1.39e+03 2.67e+06 -1.0 5.42e+03 - 7.99e-01 4.52e-04f 1\n", + " 4 2.4661840e+09 1.62e+03 1.40e+07 -1.0 5.45e+03 - 1.00e+00 8.02e-01f 1\n", + " 5 1.4734739e+09 1.44e+03 1.59e+07 -1.0 7.78e+03 - 2.47e-01 1.00e+00f 1\n", + " 6 9.7692393e+08 2.37e+02 5.76e+06 -1.0 2.22e+03 - 5.27e-03 1.00e+00f 1\n", + " 7 6.1520348e+08 7.93e+01 5.07e+06 -1.0 9.78e+02 - 5.43e-05 1.00e+00f 1\n", + " 8 1.7354679e+08 2.47e+03 1.42e+07 -1.0 1.01e+04 - 9.76e-02 5.00e-01f 2\n", + " 9 3.1087330e+07 3.84e+02 1.69e+06 -1.0 1.44e+03 - 1.00e+00 1.00e+00f 1\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 10 2.7129946e+07 1.89e+01 7.70e+04 -1.0 3.15e+02 - 1.00e+00 1.00e+00f 1\n", + " 11 2.7186030e+07 6.02e-02 2.49e+02 -1.0 1.83e+01 - 1.00e+00 1.00e+00h 1\n", + " 12 2.7186294e+07 6.36e-07 2.66e-03 -1.0 6.05e-02 - 1.00e+00 1.00e+00h 1\n", + " 13 2.7186294e+07 3.58e-12 1.12e-09 -2.5 2.04e-05 - 1.00e+00 1.00e+00h 1\n", + " 14 2.7186294e+07 4.41e-12 4.03e-10 -3.8 5.65e-07 - 1.00e+00 1.00e+00h 1\n", + " 15 2.7186294e+07 4.69e-12 8.72e-11 -5.7 3.14e-08 - 1.00e+00 1.00e+00h 1\n", + " 16 2.7186294e+07 4.12e-12 5.21e-10 -8.6 3.90e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 16\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 1.4055363392027401e+05 2.7186293738994986e+07\n", + "Dual infeasibility......: 5.2078719776239223e-10 1.0073217845016686e-07\n", + "Constraint violation....: 3.0797477427240214e-13 4.1211478674085811e-12\n", + "Complementarity.........: 2.5059035597865472e-09 4.8469917395798797e-07\n", + "Overall NLP error.......: 2.5059035597865472e-09 4.8469917395798797e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 20\n", + "Number of objective gradient evaluations = 17\n", + "Number of equality constraint evaluations = 20\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 17\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 16\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.010\n", + "Total CPU secs in NLP function evaluations = 0.002\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", + "Ipopt 3.13.2: bound_relax_factor=0\n", + "honor_original_bounds=no\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 4.47e+03 9.77e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 4.8483214e+09 1.07e+03 1.08e+07 -1.0 3.06e+02 0.0 1.33e-01 1.00e+00f 1\n", + " 2 2.7753495e+09 2.82e+03 8.80e+06 -1.0 1.07e+04 - 3.66e-01 3.05e-01f 1\n", + " 3 2.6846467e+09 2.73e+03 8.51e+06 -1.0 5.59e+03 - 1.00e+00 3.18e-02f 1\n", + " 4 2.6837149e+09 2.72e+03 8.51e+06 -1.0 5.41e+03 - 1.00e+00 3.43e-04f 1\n", + " 5 2.6591967e+09 2.70e+03 8.42e+06 -1.0 4.45e+03 - 1.00e+00 1.08e-02f 1\n", + " 6 1.2784608e+09 1.22e+02 1.09e+07 -1.0 4.43e+03 - 1.00e+00 1.00e+00f 1\n", + " 7 1.1089604e+09 3.44e+02 6.21e+06 -1.0 3.45e+03 - 2.95e-01 1.00e+00f 1\n", + " 8 1.0049821e+09 2.85e+01 4.47e+06 -1.0 8.11e+02 - 5.20e-03 1.00e+00f 1\n", + " 9 3.7408506e+08 2.96e+03 1.10e+07 -1.0 4.03e+03 - 2.70e-05 1.00e+00f 1\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 10 5.0448479e+06 1.49e+02 9.10e+05 -1.0 2.54e+03 - 1.00e+00 1.00e+00f 1\n", + " 11 4.2998863e+05 4.45e+00 3.44e+04 -1.0 2.81e+02 - 1.00e+00 1.00e+00f 1\n", + " 12 4.2816332e+05 3.34e-03 2.10e+01 -1.0 6.77e+00 - 1.00e+00 1.00e+00f 1\n", + " 13 4.2816330e+05 1.51e-09 8.78e-06 -1.0 4.22e-03 - 1.00e+00 1.00e+00h 1\n", + " 14 4.2816330e+05 3.69e-12 2.07e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", + " 15 4.2816330e+05 5.31e-12 2.82e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00H 1\n", + " 16 4.2816330e+05 3.04e-12 2.81e-11 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", + " 17 4.2816330e+05 3.50e-12 5.99e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 17\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", + "Dual infeasibility......: 5.9912096017597981e-12 1.1588372320399661e-09\n", + "Constraint violation....: 1.1815048168495169e-12 3.4958702599396929e-12\n", + "Complementarity.........: 2.5059035597864302e-09 4.8469917395796542e-07\n", + "Overall NLP error.......: 2.5059035597864302e-09 4.8469917395796542e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 19\n", + "Number of objective gradient evaluations = 18\n", + "Number of equality constraint evaluations = 19\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 18\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 17\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.010\n", + "Total CPU secs in NLP function evaluations = 0.002\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", + "Ipopt 3.13.2: bound_relax_factor=0\n", + "honor_original_bounds=no\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 1.25e+04 9.65e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 2.7174519e+07 6.12e+02 8.07e+03 -1.0 9.16e+03 - 6.72e-01 1.00e+00f 1\n", + " 2 2.7157675e+07 1.43e+01 5.15e+02 -1.0 1.08e+01 - 1.00e+00 1.00e+00f 1\n", + " 3 2.7186261e+07 8.71e-03 1.33e+00 -1.0 5.48e-01 - 1.00e+00 1.00e+00h 1\n", + " 4 2.7186294e+07 3.15e-09 1.28e-06 -1.0 4.74e-04 - 1.00e+00 1.00e+00h 1\n", + " 5 2.7186294e+07 3.52e-12 3.69e-10 -2.5 2.05e-05 - 1.00e+00 1.00e+00h 1\n", + " 6 2.7186294e+07 4.41e-12 8.31e-11 -3.8 5.65e-07 - 1.00e+00 1.00e+00h 1\n", + " 7 2.7186294e+07 4.69e-12 3.86e-10 -5.7 3.14e-08 - 1.00e+00 1.00e+00h 1\n", + " 8 2.7186294e+07 4.12e-12 7.90e-10 -8.6 3.90e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 8\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 1.4055363392027401e+05 2.7186293738994986e+07\n", + "Dual infeasibility......: 7.8999762948675547e-10 1.5280364519439663e-07\n", + "Constraint violation....: 4.7101559432420681e-14 4.1211478674085811e-12\n", + "Complementarity.........: 2.5059035597865472e-09 4.8469917395798797e-07\n", + "Overall NLP error.......: 2.5059035597865472e-09 4.8469917395798797e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 9\n", + "Number of objective gradient evaluations = 9\n", + "Number of equality constraint evaluations = 9\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 9\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 8\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.005\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", + "Ipopt 3.13.2: bound_relax_factor=0\n", + "honor_original_bounds=no\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 4.14e+03 9.57e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 2.4927485e+07 2.77e+03 5.05e+04 -1.0 9.14e+03 - 6.25e-01 1.00e+00f 1\n", + " 2 2.6826558e+07 1.63e+02 5.13e+03 -1.0 3.63e+01 - 8.14e-01 1.00e+00h 1\n", + " 3 2.7184194e+07 7.34e-01 6.95e+01 -1.0 5.26e+00 - 1.00e+00 1.00e+00h 1\n", + " 4 2.7186294e+07 1.49e-05 3.78e-03 -1.0 3.04e-02 - 1.00e+00 1.00e+00h 1\n", + " 5 2.7186294e+07 2.47e-12 8.83e-10 -2.5 2.11e-05 - 1.00e+00 1.00e+00h 1\n", + " 6 2.7186294e+07 4.41e-12 6.32e-10 -3.8 5.65e-07 - 1.00e+00 1.00e+00h 1\n", + " 7 2.7186294e+07 4.69e-12 2.33e-10 -5.7 3.14e-08 - 1.00e+00 1.00e+00h 1\n", + " 8 2.7186294e+07 4.12e-12 8.30e-10 -8.6 3.90e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 8\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 1.4055363392027401e+05 2.7186293738994986e+07\n", + "Dual infeasibility......: 8.2984251878098616e-10 1.6051055987271177e-07\n", + "Constraint violation....: 8.3214129700933826e-14 4.1211478674085811e-12\n", + "Complementarity.........: 2.5059035597865472e-09 4.8469917395798797e-07\n", + "Overall NLP error.......: 2.5059035597865472e-09 4.8469917395798797e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 9\n", + "Number of objective gradient evaluations = 9\n", + "Number of equality constraint evaluations = 9\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 9\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 8\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.005\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", + "Ipopt 3.13.2: bound_relax_factor=0\n", + "honor_original_bounds=no\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 2.01e+04 1.01e+02 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 5.1232904e+06 2.26e+03 2.68e+04 -1.0 9.80e+03 - 1.37e-01 1.00e+00f 1\n", + " 2 4.4576929e+05 1.76e+02 6.61e+03 -1.0 2.13e+02 - 1.00e+00 1.00e+00f 1\n", + " 3 4.2819162e+05 1.33e+00 2.00e+02 -1.0 1.07e+01 - 1.00e+00 1.00e+00f 1\n", + " 4 4.2816330e+05 7.39e-05 3.52e-02 -1.0 7.94e-02 - 1.00e+00 1.00e+00h 1\n", + " 5 4.2816330e+05 3.84e-12 2.26e-10 -2.5 2.16e-05 - 1.00e+00 1.00e+00h 1\n", + " 6 4.2816330e+05 4.18e-12 1.33e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", + " 7 4.2816330e+05 1.88e-12 1.97e-12 -5.7 3.95e-08 - 1.00e+00 1.00e+00H 1\n", + " 8 4.2816330e+05 3.50e-12 8.34e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 8\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 2.2136120434349154e+03 4.2816329651878146e+05\n", + "Dual infeasibility......: 8.3365341010308284e-12 1.6124767358510909e-09\n", + "Constraint violation....: 6.5831840901751260e-14 3.4958702599396929e-12\n", + "Complementarity.........: 2.5059035597864302e-09 4.8469917395796542e-07\n", + "Overall NLP error.......: 2.5059035597864302e-09 4.8469917395796542e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 10\n", + "Number of objective gradient evaluations = 9\n", + "Number of equality constraint evaluations = 10\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 9\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 8\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.005\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", + "Ipopt 3.13.2: bound_relax_factor=0\n", + "honor_original_bounds=no\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 2.29e+04 9.88e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 7.8310154e+05 1.91e+03 9.41e+03 -1.0 9.72e+03 - 6.19e-02 1.00e+00f 1\n", + " 2 4.3005112e+05 8.54e+01 1.56e+03 -1.0 6.58e+01 - 1.00e+00 1.00e+00f 1\n", + " 3 4.2816476e+05 2.09e-01 6.47e+00 -1.0 3.77e+00 - 1.00e+00 1.00e+00f 1\n", + " 4 4.2816330e+05 1.24e-06 1.73e-04 -1.0 8.41e-03 - 1.00e+00 1.00e+00h 1\n", + " 5 4.2816330e+05 3.92e-12 2.06e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", + " 6 4.2816330e+05 4.18e-12 2.30e-03 -3.8 7.13e-07 - 1.00e+00 5.00e-01h 2\n", + " 7 4.2816330e+05 3.69e-12 1.15e-03 -3.8 3.56e-07 - 1.00e+00 5.00e-01h 2\n", + " 8 4.2816330e+05 4.18e-12 2.66e-11 -5.7 2.18e-07 - 1.00e+00 1.00e+00H 1\n", + " 9 4.2816330e+05 3.50e-12 4.40e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 9\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 2.2136120434349154e+03 4.2816329651878146e+05\n", + "Dual infeasibility......: 4.3963917237901670e-12 8.5036290746094841e-10\n", + "Constraint violation....: 3.5289081297329319e-14 3.4958702599396929e-12\n", + "Complementarity.........: 2.5059035597864302e-09 4.8469917395796542e-07\n", + "Overall NLP error.......: 2.5059035597864302e-09 4.8469917395796542e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 15\n", + "Number of objective gradient evaluations = 10\n", + "Number of equality constraint evaluations = 15\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 10\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 9\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.006\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", + "Ipopt 3.13.2: bound_relax_factor=0\n", + "honor_original_bounds=no\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 4.99e+03 9.57e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 2.8459805e+07 9.05e+02 3.08e+03 -1.0 9.14e+03 - 8.90e-01 1.00e+00f 1\n", + " 2 2.7214552e+07 2.03e+01 2.33e+02 -1.0 1.11e+01 - 1.00e+00 1.00e+00f 1\n", + " 3 2.7186309e+07 1.13e-02 3.06e-01 -1.0 2.51e-01 - 1.00e+00 1.00e+00f 1\n", + " 4 2.7186294e+07 3.47e-09 1.40e-07 -1.7 1.44e-04 - 1.00e+00 1.00e+00h 1\n", + " 5 2.7186294e+07 4.43e-12 5.75e-10 -3.8 4.19e-06 - 1.00e+00 1.00e+00h 1\n", + " 6 2.7186294e+07 4.69e-12 6.00e-10 -5.7 3.14e-08 - 1.00e+00 1.00e+00h 1\n", + " 7 2.7186294e+07 4.12e-12 7.10e-10 -8.6 3.90e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 7\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 1.4055363392027401e+05 2.7186293738994986e+07\n", + "Dual infeasibility......: 7.0996209573073779e-10 1.3732293886500667e-07\n", + "Constraint violation....: 6.4371955330339621e-14 4.1211478674085811e-12\n", + "Complementarity.........: 2.5059035597865472e-09 4.8469917395798797e-07\n", + "Overall NLP error.......: 2.5059035597865472e-09 4.8469917395798797e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 8\n", + "Number of objective gradient evaluations = 8\n", + "Number of equality constraint evaluations = 8\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 8\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 7\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", + "Ipopt 3.13.2: bound_relax_factor=0\n", + "honor_original_bounds=no\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 1.30e+04 9.67e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 2.6124087e+07 9.63e+02 1.22e+04 -1.0 9.18e+03 - 6.10e-01 1.00e+00f 1\n", + " 2 2.7077188e+07 3.41e+01 1.82e+03 -1.0 2.77e+01 - 1.00e+00 1.00e+00h 1\n", + " 3 2.7186081e+07 5.00e-02 9.45e+00 -1.0 1.70e+00 - 1.00e+00 1.00e+00h 1\n", + " 4 2.7186294e+07 1.02e-07 5.14e-05 -1.0 2.98e-03 - 1.00e+00 1.00e+00h 1\n", + " 5 2.7186294e+07 2.67e-12 4.46e-10 -2.5 2.05e-05 - 1.00e+00 1.00e+00h 1\n", + " 6 2.7186294e+07 4.41e-12 3.04e-10 -3.8 5.65e-07 - 1.00e+00 1.00e+00h 1\n", + " 7 2.7186294e+07 4.69e-12 5.16e-10 -5.7 3.14e-08 - 1.00e+00 1.00e+00h 1\n", + " 8 2.7186294e+07 4.12e-12 5.64e-10 -8.6 3.90e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 8\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 1.4055363392027401e+05 2.7186293738994986e+07\n", + "Dual infeasibility......: 5.6444294344749279e-10 1.0917620008437956e-07\n", + "Constraint violation....: 5.0470631526657618e-14 4.1211478674085811e-12\n", + "Complementarity.........: 2.5059035597865472e-09 4.8469917395798797e-07\n", + "Overall NLP error.......: 2.5059035597865472e-09 4.8469917395798797e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 9\n", + "Number of objective gradient evaluations = 9\n", + "Number of equality constraint evaluations = 9\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 9\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 8\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.005\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", + "Ipopt 3.13.2: bound_relax_factor=0\n", + "honor_original_bounds=no\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 4.15e+03 1.10e+02 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 4.9476936e+09 1.04e+03 3.45e+05 -1.0 1.14e+02 0.0 5.04e-01 1.00e+00f 1\n", + " 2 3.7919093e+09 2.35e+03 8.76e+05 -1.0 6.79e+03 - 5.62e-01 4.46e-01f 1\n", + " 3 3.4291421e+09 5.47e+02 1.25e+07 -1.0 6.72e+02 -0.5 1.00e+00 1.00e+00f 1\n", + " 4 1.4862561e+09 1.08e+03 2.19e+07 -1.0 2.79e+03 - 7.85e-01 1.00e+00f 1\n", + " 5 1.0803970e+09 6.74e+03 3.96e+07 -1.0 1.93e+04 - 1.00e+00 5.85e-01f 1\n", + " 6 7.1117865e+07 1.32e+03 7.64e+06 -1.0 3.91e+03 - 1.70e-01 1.00e+00f 1\n", + " 7 1.2641918e+06 1.32e+02 7.40e+05 -1.0 1.21e+03 - 1.00e+00 1.00e+00f 1\n", + " 8 4.2836369e+05 1.99e+00 1.09e+04 -1.0 1.45e+02 - 1.00e+00 1.00e+00f 1\n", + " 9 4.2816330e+05 4.66e-04 2.53e+00 -1.0 2.20e+00 - 1.00e+00 1.00e+00h 1\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 10 4.2816330e+05 2.83e-11 1.37e-07 -1.0 5.11e-04 - 1.00e+00 1.00e+00h 1\n", + " 11 4.2816330e+05 4.92e-12 2.04e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", + " 12 4.2816330e+05 5.31e-12 1.30e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00H 1\n", + " 13 4.2816330e+05 3.04e-12 1.29e-11 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", + " 14 4.2816330e+05 3.50e-12 1.17e-11 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 14\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", + "Dual infeasibility......: 1.1672349337973593e-11 2.2576998464963993e-09\n", + "Constraint violation....: 7.6028072726330720e-13 3.4958702599396929e-12\n", + "Complementarity.........: 2.5059035597864302e-09 4.8469917395796542e-07\n", + "Overall NLP error.......: 2.5059035597864302e-09 4.8469917395796542e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 16\n", + "Number of objective gradient evaluations = 15\n", + "Number of equality constraint evaluations = 16\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 15\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 14\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.009\n", + "Total CPU secs in NLP function evaluations = 0.002\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", + "Ipopt 3.13.2: bound_relax_factor=0\n", + "honor_original_bounds=no\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 4.57e+02 9.29e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 4.9537123e+09 3.20e+02 4.79e+05 -1.0 1.07e+02 0.0 6.34e-01 1.00e+00f 1\n", + " 2 4.7229788e+09 2.17e+02 2.38e+06 -1.0 2.42e+02 -0.5 7.40e-01 1.00e+00f 1\n", + " 3 4.6081291e+09 1.51e+01 1.98e+05 -1.0 8.20e+01 -0.1 1.00e+00 1.00e+00f 1\n", + " 4 3.3364039e+09 4.66e+03 3.38e+06 -1.0 1.97e+04 - 1.21e-01 1.01e-01f 1\n", + " 5 8.5294751e+08 1.47e+03 1.28e+07 -1.0 6.99e+03 - 1.00e+00 1.00e+00f 1\n", + " 6 1.7203645e+09 1.37e+03 8.91e+06 -1.0 2.40e+04 - 3.10e-01 9.05e-02H 1\n", + " 7 1.7200201e+09 1.35e+03 8.82e+06 -1.0 1.79e+02 -0.5 1.00e+00 1.03e-02f 1\n", + " 8 1.7199986e+09 1.35e+03 8.82e+06 -1.0 2.26e+02 -1.0 1.00e+00 1.29e-04f 1\n", + " 9 1.0216189e+09 7.78e+01 5.04e+06 -1.0 1.88e+03 - 1.92e-01 1.00e+00f 1\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 10 9.9725562e+08 3.93e+00 4.37e+06 -1.0 3.37e+02 - 2.74e-03 1.00e+00f 1\n", + " 11 2.7996079e+08 1.31e+03 1.96e+07 -1.0 2.64e+03 -1.5 1.29e-03 1.00e+00f 1\n", + " 12 4.8482859e+06 2.85e+01 1.31e+06 -1.0 1.93e+03 - 8.31e-01 1.00e+00f 1\n", + " 13 4.3209489e+05 4.39e+00 3.90e+04 -1.0 3.41e+02 - 1.00e+00 1.00e+00f 1\n", + " 14 4.2816348e+05 8.48e-03 4.87e+01 -1.0 1.02e+01 - 1.00e+00 1.00e+00f 1\n", + " 15 4.2816330e+05 1.82e-08 8.87e-05 -1.0 1.28e-02 - 1.00e+00 1.00e+00h 1\n", + " 16 4.2816330e+05 4.18e-12 2.22e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", + " 17 4.2816330e+05 5.31e-12 1.01e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00H 1\n", + " 18 4.2816330e+05 3.04e-12 1.54e-11 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", + " 19 4.2816330e+05 3.50e-12 4.40e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 19\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", + "Dual infeasibility......: 4.3995938736571737e-12 8.5098227662596132e-10\n", + "Constraint violation....: 4.6942418461511737e-13 3.4958702599396929e-12\n", + "Complementarity.........: 2.5059035597864302e-09 4.8469917395796542e-07\n", + "Overall NLP error.......: 2.5059035597864302e-09 4.8469917395796542e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 22\n", + "Number of objective gradient evaluations = 20\n", + "Number of equality constraint evaluations = 22\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 20\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 19\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.013\n", + "Total CPU secs in NLP function evaluations = 0.002\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", + "Ipopt 3.13.2: bound_relax_factor=0\n", + "honor_original_bounds=no\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 8.99e+03 9.64e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 2.5839765e+07 3.16e+02 8.96e+03 -1.0 9.16e+03 - 6.70e-01 1.00e+00f 1\n", + " 2 2.7166753e+07 4.34e+00 8.37e+01 -1.0 1.51e+01 - 1.00e+00 1.00e+00h 1\n", + " 3 2.7186290e+07 8.47e-04 9.25e-02 -1.0 2.86e-01 - 1.00e+00 1.00e+00h 1\n", + " 4 2.7186294e+07 3.28e-11 9.72e-09 -2.5 3.34e-05 - 1.00e+00 1.00e+00h 1\n", + " 5 2.7186294e+07 5.43e-12 1.38e-09 -3.8 5.65e-07 - 1.00e+00 1.00e+00h 1\n", + " 6 2.7186294e+07 4.69e-12 1.41e-09 -5.7 3.14e-08 - 1.00e+00 1.00e+00h 1\n", + " 7 2.7186294e+07 4.12e-12 8.95e-10 -8.6 3.90e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 7\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 1.4055363392027401e+05 2.7186293738994986e+07\n", + "Dual infeasibility......: 8.9532613730863700e-10 1.7317659232403083e-07\n", + "Constraint violation....: 5.0554756559712512e-14 4.1211478674085811e-12\n", + "Complementarity.........: 2.5059035597865472e-09 4.8469917395798797e-07\n", + "Overall NLP error.......: 2.5059035597865472e-09 4.8469917395798797e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 8\n", + "Number of objective gradient evaluations = 8\n", + "Number of equality constraint evaluations = 8\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 8\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 7\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", + "Ipopt 3.13.2: bound_relax_factor=0\n", + "honor_original_bounds=no\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 2.98e+03 1.00e+02 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 4.0130337e+09 1.42e+04 3.05e+06 -1.0 9.58e+03 - 2.26e-02 1.43e-01f 1\n", + " 2 2.0683948e+07 6.26e+02 4.78e+05 -1.0 8.07e+03 - 2.73e-02 1.00e+00f 1\n", + " 3 2.7200387e+07 8.95e+00 1.34e+04 -1.0 1.07e+02 - 1.00e+00 1.00e+00h 1\n", + " 4 2.7186293e+07 2.26e-03 2.41e+00 -1.0 3.61e+00 - 1.00e+00 1.00e+00f 1\n", + " 5 2.7186294e+07 4.02e-10 1.02e-06 -1.0 9.28e-04 - 1.00e+00 1.00e+00h 1\n", + " 6 2.7186294e+07 3.18e-12 1.31e-09 -2.5 2.05e-05 - 1.00e+00 1.00e+00h 1\n", + " 7 2.7186294e+07 4.41e-12 7.76e-10 -3.8 5.65e-07 - 1.00e+00 1.00e+00h 1\n", + " 8 2.7186294e+07 4.69e-12 3.74e-10 -5.7 3.14e-08 - 1.00e+00 1.00e+00h 1\n", + " 9 2.7186294e+07 4.12e-12 8.08e-10 -8.6 3.90e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 9\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 1.4055363392027401e+05 2.7186293738994986e+07\n", + "Dual infeasibility......: 8.0801464593843588e-10 1.5628854905560542e-07\n", + "Constraint violation....: 1.3908096331519524e-12 4.1211478674085811e-12\n", + "Complementarity.........: 2.5059035597865472e-09 4.8469917395798797e-07\n", + "Overall NLP error.......: 2.5059035597865472e-09 4.8469917395798797e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 10\n", + "Number of objective gradient evaluations = 10\n", + "Number of equality constraint evaluations = 10\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 10\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 9\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.005\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", + "Ipopt 3.13.2: bound_relax_factor=0\n", + "honor_original_bounds=no\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 9.84e+03 1.00e+02 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 1.7575504e+06 9.13e+02 1.38e+04 -1.0 9.75e+03 - 4.47e-01 1.00e+00f 1\n", + " 2 4.2824668e+05 2.49e+01 1.18e+03 -1.0 1.05e+02 - 1.00e+00 1.00e+00f 1\n", + " 3 4.2816335e+05 1.64e-02 2.86e+00 -1.0 6.45e-01 - 1.00e+00 1.00e+00h 1\n", + " 4 4.2816330e+05 6.88e-09 3.30e-06 -1.0 4.97e-04 - 1.00e+00 1.00e+00h 1\n", + " 5 4.2816330e+05 3.58e-12 2.27e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", + " 6 4.2816330e+05 4.18e-12 1.86e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", + " 7 4.2816330e+05 1.88e-12 6.52e-12 -5.7 3.95e-08 - 1.00e+00 1.00e+00H 1\n", + " 8 4.2816330e+05 3.50e-12 8.34e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 8\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 2.2136120434349154e+03 4.2816329651878146e+05\n", + "Dual infeasibility......: 8.3365341010308284e-12 1.6124767358510909e-09\n", + "Constraint violation....: 7.9278799075454343e-14 3.4958702599396933e-12\n", + "Complementarity.........: 2.5059035597864302e-09 4.8469917395796542e-07\n", + "Overall NLP error.......: 2.5059035597864302e-09 4.8469917395796542e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 10\n", + "Number of objective gradient evaluations = 9\n", + "Number of equality constraint evaluations = 10\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 9\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 8\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.005\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", + "Ipopt 3.13.2: bound_relax_factor=0\n", + "honor_original_bounds=no\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 5.18e+03 9.94e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 4.4742544e+05 1.01e+03 5.01e+03 -1.0 9.66e+03 - 8.96e-01 1.00e+00f 1\n", + " 2 4.2822932e+05 2.50e+01 3.20e+02 -1.0 1.24e+01 - 1.00e+00 1.00e+00f 1\n", + " 3 4.2816333e+05 1.69e-02 4.70e-01 -1.0 3.08e-01 - 1.00e+00 1.00e+00h 1\n", + " 4 4.2816330e+05 7.77e-09 5.77e-07 -1.7 1.95e-04 - 1.00e+00 1.00e+00h 1\n", + " 5 4.2816330e+05 5.83e-12 6.32e-12 -3.8 5.28e-06 - 1.00e+00 1.00e+00h 1\n", + " 6 4.2816330e+05 5.09e-12 5.58e-12 -5.7 3.95e-08 - 1.00e+00 1.00e+00h 1\n", + " 7 4.2816330e+05 3.50e-12 2.86e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 7\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", + "Dual infeasibility......: 2.8627514714548167e-12 5.5372173763117912e-10\n", + "Constraint violation....: 7.8113496119784019e-14 3.4958702599396929e-12\n", + "Complementarity.........: 2.5059035597864302e-09 4.8469917395796542e-07\n", + "Overall NLP error.......: 2.5059035597864302e-09 4.8469917395796542e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 8\n", + "Number of objective gradient evaluations = 8\n", + "Number of equality constraint evaluations = 8\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 8\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 7\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", + "Ipopt 3.13.2: bound_relax_factor=0\n", + "honor_original_bounds=no\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 1.70e+03 1.00e+02 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 4.8345448e+09 1.66e+03 6.22e+02 -1.0 7.58e+03 - 3.85e-01 2.30e-02f 1\n", + " 2 4.8321364e+09 1.66e+03 6.32e+04 -1.0 7.40e+03 - 9.64e-01 2.63e-04f 1\n", + " 3 4.7840829e+09 1.64e+03 4.48e+06 -1.0 4.93e+03 - 1.00e+00 1.38e-02f 1\n", + " 4 2.5854958e+09 1.81e+03 2.34e+07 -1.0 5.10e+03 - 1.00e+00 1.00e+00f 1\n", + " 5 2.4006679e+09 2.48e+03 3.67e+07 -1.0 1.01e+04 - 2.59e-01 1.00e+00f 1\n", + " 6 1.0935406e+09 5.82e+02 7.41e+06 -1.0 3.94e+03 - 6.77e-03 1.00e+00f 1\n", + " 7 6.7957486e+08 5.18e+01 5.05e+06 -1.0 9.85e+02 - 8.72e-05 1.00e+00f 1\n", + " 8 2.6794664e+08 3.62e+03 1.93e+07 -1.0 2.03e+04 - 3.53e-02 2.81e-01f 2\n", + " 9 3.6194377e+07 6.17e+02 2.61e+06 -1.0 1.73e+03 - 1.00e+00 1.00e+00f 1\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 10 2.7100647e+07 4.15e+01 1.63e+05 -1.0 4.49e+02 - 1.00e+00 1.00e+00f 1\n", + " 11 2.7185103e+07 2.75e-01 1.11e+03 -1.0 3.84e+01 - 1.00e+00 1.00e+00h 1\n", + " 12 2.7186294e+07 1.30e-05 5.36e-02 -1.0 2.71e-01 - 1.00e+00 1.00e+00h 1\n", + " 13 2.7186294e+07 3.52e-12 3.60e-10 -2.5 1.90e-05 - 1.00e+00 1.00e+00h 1\n", + " 14 2.7186294e+07 4.41e-12 1.67e-10 -3.8 5.65e-07 - 1.00e+00 1.00e+00h 1\n", + " 15 2.7186294e+07 4.69e-12 1.53e-10 -5.7 3.14e-08 - 1.00e+00 1.00e+00h 1\n", + " 16 2.7186294e+07 4.12e-12 6.92e-10 -8.6 3.90e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 16\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 1.4055363392027401e+05 2.7186293738994986e+07\n", + "Dual infeasibility......: 6.9159932411192459e-10 1.3377115803112010e-07\n", + "Constraint violation....: 1.6697754290362354e-13 4.1211478674085811e-12\n", + "Complementarity.........: 2.5059035597865472e-09 4.8469917395798797e-07\n", + "Overall NLP error.......: 2.5059035597865472e-09 4.8469917395798797e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 20\n", + "Number of objective gradient evaluations = 17\n", + "Number of equality constraint evaluations = 20\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 17\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 16\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.009\n", + "Total CPU secs in NLP function evaluations = 0.002\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", + "Ipopt 3.13.2: bound_relax_factor=0\n", + "honor_original_bounds=no\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 1.40e+04 9.84e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 6.7882418e+06 2.02e+03 4.86e+04 -1.0 9.42e+03 - 1.15e-01 1.00e+00f 1\n", + " 2 2.7335745e+07 1.17e+02 1.07e+04 -1.0 2.72e+02 - 9.36e-01 1.00e+00h 1\n", + " 3 2.7186688e+07 3.81e-01 7.08e+01 -1.0 1.50e+00 - 1.00e+00 1.00e+00f 1\n", + " 4 2.7186294e+07 4.25e-06 1.56e-03 -1.0 8.68e-03 - 1.00e+00 1.00e+00f 1\n", + " 5 2.7186294e+07 4.12e-12 8.98e-10 -2.5 2.03e-05 - 1.00e+00 1.00e+00h 1\n", + " 6 2.7186294e+07 4.41e-12 1.60e-10 -3.8 5.65e-07 - 1.00e+00 1.00e+00h 1\n", + " 7 2.7186294e+07 4.69e-12 8.78e-11 -5.7 3.14e-08 - 1.00e+00 1.00e+00h 1\n", + " 8 2.7186294e+07 4.12e-12 5.06e-10 -8.6 3.90e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 8\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 1.4055363392027401e+05 2.7186293738994986e+07\n", + "Dual infeasibility......: 5.0623528253402496e-10 9.7917504572095877e-08\n", + "Constraint violation....: 8.9958290210069930e-14 4.1211478674085811e-12\n", + "Complementarity.........: 2.5059035597865472e-09 4.8469917395798797e-07\n", + "Overall NLP error.......: 2.5059035597865472e-09 4.8469917395798797e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 9\n", + "Number of objective gradient evaluations = 9\n", + "Number of equality constraint evaluations = 9\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 9\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 8\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.005\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", + "Ipopt 3.13.2: bound_relax_factor=0\n", + "honor_original_bounds=no\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 5.24e+02 1.06e+02 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 2.8138975e+09 9.77e+03 5.01e+06 -1.0 1.59e+04 - 1.46e-01 1.92e-01f 1\n", + " 2 1.2458106e+07 2.49e+03 2.37e+06 -1.0 6.91e+03 - 1.10e-02 1.00e+00f 1\n", + " 3 5.9638509e+05 2.45e+02 2.68e+05 -1.0 5.41e+02 - 8.34e-01 1.00e+00f 1\n", + " 4 4.2833613e+05 3.87e+00 6.05e+03 -1.0 7.33e+01 - 1.00e+00 1.00e+00f 1\n", + " 5 4.2816332e+05 1.27e-03 2.81e+00 -1.0 1.76e+00 - 1.00e+00 1.00e+00h 1\n", + " 6 4.2816330e+05 1.69e-10 4.83e-07 -1.0 7.86e-04 - 1.00e+00 1.00e+00h 1\n", + " 7 4.2816330e+05 3.38e-12 1.99e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", + " 8 4.2816330e+05 4.18e-12 2.79e-12 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", + " 9 4.2816330e+05 5.09e-12 4.90e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 10 4.2816330e+05 3.50e-12 6.96e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 10\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", + "Dual infeasibility......: 6.9554776294329938e-12 1.3453487658386204e-09\n", + "Constraint violation....: 4.1257311072239357e-13 3.4958702599396933e-12\n", + "Complementarity.........: 2.5059035597864302e-09 4.8469917395796542e-07\n", + "Overall NLP error.......: 2.5059035597864302e-09 4.8469917395796542e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 11\n", + "Number of objective gradient evaluations = 11\n", + "Number of equality constraint evaluations = 11\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 11\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 10\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.006\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", + "Ipopt 3.13.2: bound_relax_factor=0\n", + "honor_original_bounds=no\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 5.14e+03 1.01e+02 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 1.2558366e+07 9.25e+03 1.72e+06 -1.0 9.81e+03 - 3.06e-01 1.00e+00f 1\n", + " 2 1.4246827e+06 1.46e+03 3.89e+05 -1.0 4.34e+02 - 2.34e-01 1.00e+00f 1\n", + " 3 4.3546419e+05 8.26e+01 3.35e+04 -1.0 1.54e+02 - 9.56e-01 1.00e+00f 1\n", + " 4 4.2817095e+05 3.63e-01 2.26e+02 -1.0 1.32e+01 - 1.00e+00 1.00e+00f 1\n", + " 5 4.2816330e+05 7.79e-06 7.54e-03 -1.0 7.81e-02 - 1.00e+00 1.00e+00h 1\n", + " 6 4.2816330e+05 3.69e-12 1.66e-10 -2.5 2.53e-05 - 1.00e+00 1.00e+00h 1\n", + " 7 4.2816330e+05 4.18e-12 4.88e-12 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", + " 8 4.2816330e+05 5.09e-12 4.37e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", + " 9 4.2816330e+05 3.50e-12 1.42e-11 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 9\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", + "Dual infeasibility......: 1.4181073467955190e-11 2.7429445833665322e-09\n", + "Constraint violation....: 1.7763568394002505e-13 3.4958702599396925e-12\n", + "Complementarity.........: 2.5059035597864302e-09 4.8469917395796542e-07\n", + "Overall NLP error.......: 2.5059035597864302e-09 4.8469917395796542e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 10\n", + "Number of objective gradient evaluations = 10\n", + "Number of equality constraint evaluations = 10\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 10\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 9\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.005\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", + "Ipopt 3.13.2: bound_relax_factor=0\n", + "honor_original_bounds=no\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 1.81e+04 9.66e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 2.9003335e+07 1.13e+03 1.55e+04 -1.0 9.14e+03 - 3.32e-01 1.00e+00f 1\n", + " 2 2.7238948e+07 3.20e+01 1.01e+03 -1.0 1.69e+01 - 1.00e+00 1.00e+00f 1\n", + " 3 2.7186331e+07 4.27e-02 2.05e+00 -1.0 5.65e-01 - 1.00e+00 1.00e+00f 1\n", + " 4 2.7186294e+07 7.75e-08 3.69e-06 -1.0 5.69e-04 - 1.00e+00 1.00e+00h 1\n", + " 5 2.7186294e+07 3.21e-12 5.51e-10 -2.5 2.05e-05 - 1.00e+00 1.00e+00h 1\n", + " 6 2.7186294e+07 4.41e-12 1.76e-10 -3.8 5.65e-07 - 1.00e+00 1.00e+00h 1\n", + " 7 2.7186294e+07 4.69e-12 2.40e-10 -5.7 3.14e-08 - 1.00e+00 1.00e+00h 1\n", + " 8 2.7186294e+07 4.12e-12 8.01e-10 -8.6 3.90e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 8\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 1.4055363392027401e+05 2.7186293738994986e+07\n", + "Dual infeasibility......: 8.0073868832425246e-10 1.5488121211656997e-07\n", + "Constraint violation....: 3.7925743719988039e-14 4.1211478674085811e-12\n", + "Complementarity.........: 2.5059035597865472e-09 4.8469917395798797e-07\n", + "Overall NLP error.......: 2.5059035597865472e-09 4.8469917395798797e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 9\n", + "Number of objective gradient evaluations = 9\n", + "Number of equality constraint evaluations = 9\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 9\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 8\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.005\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", + "Ipopt 3.13.2: bound_relax_factor=0\n", + "honor_original_bounds=no\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 3.55e+03 9.62e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 2.5339484e+08 1.19e+04 3.21e+05 -1.0 9.18e+03 - 2.52e-01 7.84e-01f 1\n", + " 2 2.2997591e+07 1.30e+03 4.50e+04 -1.0 1.97e+03 - 2.63e-02 1.00e+00f 1\n", + " 3 2.7064893e+07 4.24e+01 5.28e+03 -1.0 6.00e+01 - 1.00e+00 1.00e+00h 1\n", + " 4 2.7186154e+07 4.87e-02 1.67e+01 -1.0 1.89e+00 - 1.00e+00 1.00e+00h 1\n", + " 5 2.7186294e+07 5.94e-08 4.87e-05 -1.0 2.66e-03 - 1.00e+00 1.00e+00h 1\n", + " 6 2.7186294e+07 2.79e-12 3.95e-10 -2.5 2.05e-05 - 1.00e+00 1.00e+00h 1\n", + " 7 2.7186294e+07 4.41e-12 1.99e-10 -3.8 5.65e-07 - 1.00e+00 1.00e+00h 1\n", + " 8 2.7186294e+07 4.69e-12 3.32e-11 -5.7 3.14e-08 - 1.00e+00 1.00e+00h 1\n", + " 9 2.7186294e+07 4.12e-12 5.94e-10 -8.6 3.90e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 9\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 1.4055363392027401e+05 2.7186293738994986e+07\n", + "Dual infeasibility......: 5.9354677390422649e-10 1.1480554784052137e-07\n", + "Constraint violation....: 1.6765154534897615e-13 4.1211478674085811e-12\n", + "Complementarity.........: 2.5059035597865472e-09 4.8469917395798797e-07\n", + "Overall NLP error.......: 2.5059035597865472e-09 4.8469917395798797e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 10\n", + "Number of objective gradient evaluations = 10\n", + "Number of equality constraint evaluations = 10\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 10\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 9\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.005\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", + "Ipopt 3.13.2: bound_relax_factor=0\n", + "honor_original_bounds=no\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 1.41e+04 1.01e+02 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 2.9401960e+06 1.02e+03 1.97e+04 -1.0 9.77e+03 - 3.26e-01 1.00e+00f 1\n", + " 2 4.2927250e+05 4.60e+01 1.72e+03 -1.0 1.52e+02 - 1.00e+00 1.00e+00f 1\n", + " 3 4.2816494e+05 8.94e-02 1.45e+01 -1.0 1.80e+00 - 1.00e+00 1.00e+00h 1\n", + " 4 4.2816330e+05 3.28e-07 1.56e-04 -1.0 4.73e-03 - 1.00e+00 1.00e+00h 1\n", + " 5 4.2816330e+05 3.58e-12 2.16e-10 -2.5 2.58e-05 - 1.00e+00 1.00e+00h 1\n", + " 6 4.2816330e+05 5.31e-12 6.42e-12 -3.8 7.13e-07 - 1.00e+00 1.00e+00H 1\n", + " 7 4.2816330e+05 3.04e-12 1.36e-11 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", + " 8 4.2816330e+05 3.50e-12 6.22e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 8\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", + "Dual infeasibility......: 6.2153811273360234e-12 1.2021971422198111e-09\n", + "Constraint violation....: 7.1908036628402804e-14 3.4958702599396929e-12\n", + "Complementarity.........: 2.5059035597864302e-09 4.8469917395796542e-07\n", + "Overall NLP error.......: 2.5059035597864302e-09 4.8469917395796542e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 10\n", + "Number of objective gradient evaluations = 9\n", + "Number of equality constraint evaluations = 10\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 9\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 8\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.005\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", + "Ipopt 3.13.2: bound_relax_factor=0\n", + "honor_original_bounds=no\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 1.12e+04 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 4.4982169e+05 6.60e+01 4.87e+03 -1.0 9.66e+03 - 7.20e-01 1.00e+00f 1\n", + " 2 4.2816538e+05 2.24e-01 6.53e+01 -1.0 1.30e+01 - 1.00e+00 1.00e+00f 1\n", + " 3 4.2816330e+05 1.71e-06 1.54e-03 -1.0 1.79e-02 - 1.00e+00 1.00e+00h 1\n", + " 4 4.2816330e+05 3.64e-12 1.98e-10 -2.5 2.58e-05 - 1.00e+00 1.00e+00h 1\n", + " 5 4.2816330e+05 4.18e-12 7.60e-12 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", + " 6 4.2816330e+05 5.09e-12 3.60e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", + " 7 4.2816330e+05 3.50e-12 3.49e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 7\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", + "Dual infeasibility......: 3.4900991718842455e-12 6.7506515924652994e-10\n", + "Constraint violation....: 5.1473574040930074e-14 3.4958702599396925e-12\n", + "Complementarity.........: 2.5059035597864302e-09 4.8469917395796542e-07\n", + "Overall NLP error.......: 2.5059035597864302e-09 4.8469917395796542e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 8\n", + "Number of objective gradient evaluations = 8\n", + "Number of equality constraint evaluations = 8\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 8\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 7\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", + "Ipopt 3.13.2: bound_relax_factor=0\n", + "honor_original_bounds=no\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 5.28e+03 9.78e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 1.9323923e+07 7.90e+03 4.80e+05 -1.0 9.33e+03 - 2.42e-01 1.00e+00f 1\n", + " 2 2.3574045e+07 1.13e+03 1.06e+05 -1.0 1.29e+02 - 6.53e-01 1.00e+00h 1\n", + " 3 2.6987010e+07 4.63e+01 1.05e+04 -1.0 5.24e+01 - 1.00e+00 1.00e+00h 1\n", + " 4 2.7185919e+07 8.56e-02 5.03e+01 -1.0 2.77e+00 - 1.00e+00 1.00e+00h 1\n", + " 5 2.7186294e+07 2.43e-07 3.68e-04 -1.0 1.09e-02 - 1.00e+00 1.00e+00h 1\n", + " 6 2.7186294e+07 3.52e-12 9.77e-10 -2.5 2.05e-05 - 1.00e+00 1.00e+00h 1\n", + " 7 2.7186294e+07 4.41e-12 3.25e-10 -3.8 5.65e-07 - 1.00e+00 1.00e+00h 1\n", + " 8 2.7186294e+07 4.69e-12 4.15e-10 -5.7 3.14e-08 - 1.00e+00 1.00e+00h 1\n", + " 9 2.7186294e+07 4.12e-12 7.50e-10 -8.6 3.90e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 9\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 1.4055363392027401e+05 2.7186293738994986e+07\n", + "Dual infeasibility......: 7.4980698502496848e-10 1.4502985354332181e-07\n", + "Constraint violation....: 1.1133569257438686e-13 4.1211478674085811e-12\n", + "Complementarity.........: 2.5059035597865472e-09 4.8469917395798797e-07\n", + "Overall NLP error.......: 2.5059035597865472e-09 4.8469917395798797e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 10\n", + "Number of objective gradient evaluations = 10\n", + "Number of equality constraint evaluations = 10\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 10\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 9\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.005\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", + "Ipopt 3.13.2: bound_relax_factor=0\n", + "honor_original_bounds=no\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 2.18e+04 9.74e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 2.2432629e+07 2.91e+03 2.66e+04 -1.0 9.25e+03 - 1.74e-01 1.00e+00f 1\n", + " 2 2.6283816e+07 2.45e+02 8.03e+03 -1.0 8.73e+01 - 8.79e-01 1.00e+00h 1\n", + " 3 2.7175244e+07 2.54e+00 3.60e+02 -1.0 1.33e+01 - 1.00e+00 1.00e+00h 1\n", + " 4 2.7186293e+07 2.70e-04 1.09e-01 -1.0 1.57e-01 - 1.00e+00 1.00e+00h 1\n", + " 5 2.7186294e+07 5.40e-12 1.77e-09 -2.5 1.89e-05 - 1.00e+00 1.00e+00h 1\n", + " 6 2.7186294e+07 4.41e-12 1.55e-10 -3.8 5.65e-07 - 1.00e+00 1.00e+00h 1\n", + " 7 2.7186294e+07 4.69e-12 6.07e-10 -5.7 3.14e-08 - 1.00e+00 1.00e+00h 1\n", + " 8 2.7186294e+07 4.12e-12 9.83e-10 -8.6 3.90e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 8\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 1.4055363392027403e+05 2.7186293738994990e+07\n", + "Dual infeasibility......: 9.8263762867873223e-10 1.9006463559243576e-07\n", + "Constraint violation....: 5.4622972811557702e-14 4.1211478674085811e-12\n", + "Complementarity.........: 2.5059035597865472e-09 4.8469917395798797e-07\n", + "Overall NLP error.......: 2.5059035597865472e-09 4.8469917395798797e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 9\n", + "Number of objective gradient evaluations = 9\n", + "Number of equality constraint evaluations = 9\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 9\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 8\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.005\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", + " run theta1_init theta2_init theta1_est theta2_est objective\n", + "0 0 0.904117 -1.570500 1.204754 -0.646637 2.718629e+07\n", + "1 1 1.646200 1.969240 1.204754 -0.646637 2.718629e+07\n", + "2 2 -0.500088 -0.410770 -1.500300 0.501315 4.281633e+05\n", + "3 3 -0.032914 1.388731 -1.500300 0.501315 4.281633e+05\n", + "4 4 1.175866 -0.947448 1.204754 -0.646637 2.718629e+07\n", + "5 5 0.434035 0.596308 1.204754 -0.646637 2.718629e+07\n", + "6 6 -1.290840 -1.037834 -1.500300 0.501315 4.281633e+05\n", + "7 7 -1.114986 1.637305 -1.500300 0.501315 4.281633e+05\n", + "8 8 0.218859 -0.196021 1.204754 -0.646637 2.718629e+07\n", + "9 9 1.477024 0.343782 1.204754 -0.646637 2.718629e+07\n", + "10 10 -0.372907 -1.785309 1.204754 -0.646637 2.718629e+07\n", + "11 11 -0.840302 0.764188 -1.500300 0.501315 4.281633e+05\n", + "12 12 1.947122 -1.322903 1.204754 -0.646637 2.718629e+07\n", + "13 13 0.689208 1.220790 1.204754 -0.646637 2.718629e+07\n", + "14 14 -1.582145 -0.662319 -1.500300 0.501315 4.281633e+05\n", + "15 15 -1.744031 1.077411 -1.500300 0.501315 4.281633e+05\n", + "16 16 0.605718 -0.511126 1.204754 -0.646637 2.718629e+07\n", + "17 17 1.847797 0.909993 1.204754 -0.646637 2.718629e+07\n", + "18 18 -0.985865 -1.476522 -1.500300 0.501315 4.281633e+05\n", + "19 19 -0.453270 0.450540 -1.500300 0.501315 4.281633e+05\n", + "20 20 1.317900 -1.884256 1.204754 -0.646637 2.718629e+07\n", + "21 21 0.076058 1.533004 -1.500300 0.501315 4.281633e+05\n", + "22 22 -1.211183 -0.099532 -1.500300 0.501315 4.281633e+05\n", + "23 23 -1.379087 0.702927 -1.500300 0.501315 4.281633e+05\n", + "24 24 0.267140 -1.136644 1.204754 -0.646637 2.718629e+07\n", + "25 25 1.025302 1.284538 1.204754 -0.646637 2.718629e+07\n", + "26 26 -0.137004 -0.851065 1.204754 -0.646637 2.718629e+07\n", + "27 27 -0.669836 1.826000 -1.500300 0.501315 4.281633e+05\n", + "28 28 1.554977 -0.259714 1.204754 -0.646637 2.718629e+07\n", + "29 29 0.797052 0.157482 1.204754 -0.646637 2.718629e+07\n", + "30 30 -1.911667 -1.724012 -1.500300 0.501315 4.281633e+05\n", + "31 31 -1.986316 1.915811 -1.500300 0.501315 4.281633e+05\n", + "32 32 0.848017 -0.482338 1.204754 -0.646637 2.718629e+07\n", + "33 33 1.605694 0.067732 1.204754 -0.646637 2.718629e+07\n", + "34 34 -0.743749 -1.501449 -1.500300 0.501315 4.281633e+05\n", + "35 35 -0.211159 0.542803 -1.500300 0.501315 4.281633e+05\n", + "36 36 1.075776 -1.109333 1.204754 -0.646637 2.718629e+07\n", + "37 37 0.318351 1.444601 1.204754 -0.646637 2.718629e+07\n", + "38 38 -1.453490 -0.878315 -1.500300 0.501315 4.281633e+05\n", + "39 39 -1.136904 0.290293 -1.500300 0.501315 4.281633e+05\n", + "40 40 0.024975 -1.856822 1.204754 -0.646637 2.718629e+07\n", + "41 41 1.267545 1.693189 1.204754 -0.646637 2.718629e+07\n", + "42 42 -0.379231 -0.126904 -1.500300 0.501315 4.281633e+05\n", + "43 43 -0.912072 1.167345 -1.500300 0.501315 4.281633e+05\n", + "44 44 1.797196 -0.733872 1.204754 -0.646637 2.718629e+07\n", + "45 45 0.554877 0.820121 1.204754 -0.646637 2.718629e+07\n", + "46 46 -1.669509 -1.253837 -1.500300 0.501315 4.281633e+05\n", + "47 47 -1.500417 0.979125 -1.500300 0.501315 4.281633e+05\n", + "48 48 0.646298 -1.420653 1.204754 -0.646637 2.718629e+07\n", + "49 49 1.903972 1.005914 1.204754 -0.646637 2.718629e+07\n", + "\n", + "Unique estimated theta_1 values:\n", + "[ 1.20475361 -1.50030035]\n", + "\n", + "Unique estimated theta_2 values:\n", + "[-0.64663711 0.5013147 -0.64663711]\n" + ] + } + ], + "source": [ + "\n", + "# Run parmest estimation for multiple random initial guesses of theta within bounds\n", + "\n", + "num_runs = 50 # Number of random initializations\n", + "theta_names = ['theta_1', 'theta_2']\n", + "theta1_bounds = (-2, 2)\n", + "theta2_bounds = (-2, 2)\n", + "\n", + "results = []\n", + "\n", + "for run in range(num_runs):\n", + " # Sobol sampling for initial values\n", + " if run == 0:\n", + " sampler = scipy.stats.qmc.Sobol(d=len(theta_names), seed = 12345)\n", + " sobol_samples = sampler.random(num_runs + 1)[1:]\n", + " sobol_theta1 = theta1_bounds[0] + (theta1_bounds[1] - theta1_bounds[0]) * sobol_samples[:, 0]\n", + " sobol_theta2 = theta2_bounds[0] + (theta2_bounds[1] - theta2_bounds[0]) * sobol_samples[:, 1]\n", + " theta1_init = sobol_theta1[run]\n", + " theta2_init = sobol_theta2[run]\n", + " theta_initial = {1: theta1_init, 2: theta2_init}\n", + " # Create experiment and estimator\n", + " exp = Simple_Multimodal(data_df, theta_initial=theta_initial)\n", + " pest = parmest.Estimator([exp], tee=True)\n", + " \n", + " # Estimate parameters\n", + " obj, theta, cov = pest.theta_est(calc_cov=True, cov_n=len(conc))\n", + " results.append({\n", + " 'run': run,\n", + " 'theta1_init': theta1_init,\n", + " 'theta2_init': theta2_init,\n", + " 'theta1_est': theta['theta_1'],\n", + " 'theta2_est': theta['theta_2'],\n", + " 'objective': obj\n", + " })\n", + "\n", + "# Convert results to DataFrame for inspection\n", + "random_init_results_df = pd.DataFrame(results)\n", + "print(random_init_results_df)\n", + "\n", + "# Print unique values of estimated parameters\n", + "print(\"\\nUnique estimated theta_1 values:\")\n", + "print(random_init_results_df['theta1_est'].unique())\n", + "print(\"\\nUnique estimated theta_2 values:\")\n", + "print(random_init_results_df['theta2_est'].unique())\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "44c57a03", + "metadata": {}, + "outputs": [], + "source": [ + "# # Print the model ef_instance from the Estimator\n", + "# ef_instance = pest.ef_instance\n", + "\n", + "# ef_instance.pprint()" + ] + }, + { + "cell_type": "markdown", + "id": "7301ae3c", + "metadata": {}, + "source": [ + "### Integrated Multistart test\n", + "\n", + "Now checking results against embedded feature version of multistart. Same settings" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "e6841131", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting multistart optimization with 50 restarts using sobol sampling method.\n", + "Setting theta_2 to -1.570499699562788\n", + "Current value of theta_1 is 0.9041174054145813\n", + "Current value of theta_2 is -1.570499699562788\n", + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", + " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", + " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", + " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", + " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", + " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 6\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", + "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", + "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", + "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 7\n", + "Number of objective gradient evaluations = 7\n", + "Number of equality constraint evaluations = 7\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 7\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 6\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Restart 1/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", + "theta_2 0.501315\n", + "dtype: float64\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Applications/anaconda3/envs/parmest-dev-mac2/lib/python3.13/site-packages/scipy/stats/_qmc.py:993: UserWarning: The balance properties of Sobol' points require n to be a power of 2.\n", + " sample = self._random(n, workers=workers)\n", + "/Users/scini/Documents/GitHub/pyomo/pyomo/contrib/parmest/parmest.py:1208: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " results_df.at[i, f'converged_{name}'] = converged_theta[j] if not np.isnan(converged_theta_vals[i, j]) else np.nan\n", + "/Users/scini/Documents/GitHub/pyomo/pyomo/contrib/parmest/parmest.py:1212: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value 'successful' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.\n", + " results_df.at[i, \"solver termination\"] = solver_termination if 'solver_termination' in locals() else np.nan\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Setting theta_2 to 1.9692404232919216\n", + "Current value of theta_1 is 1.6461998745799065\n", + "Current value of theta_2 is 1.9692404232919216\n", + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", + " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", + " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", + " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", + " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", + " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 6\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", + "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", + "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", + "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 7\n", + "Number of objective gradient evaluations = 7\n", + "Number of equality constraint evaluations = 7\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 7\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 6\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Restart 2/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", + "theta_2 0.501315\n", + "dtype: float64\n", + "Setting theta_2 to -0.4107704162597656\n", + "Current value of theta_1 is -0.5000881142914295\n", + "Current value of theta_2 is -0.4107704162597656\n", + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", + " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", + " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", + " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", + " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", + " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 6\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", + "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", + "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", + "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 7\n", + "Number of objective gradient evaluations = 7\n", + "Number of equality constraint evaluations = 7\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 7\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 6\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Restart 3/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", + "theta_2 0.501315\n", + "dtype: float64\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/scini/Documents/GitHub/pyomo/pyomo/contrib/parmest/parmest.py:1208: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " results_df.at[i, f'converged_{name}'] = converged_theta[j] if not np.isnan(converged_theta_vals[i, j]) else np.nan\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Setting theta_2 to 1.3887314423918724\n", + "Current value of theta_1 is -0.03291422128677368\n", + "Current value of theta_2 is 1.3887314423918724\n", + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", + " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", + " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", + " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", + " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", + " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 6\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", + "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", + "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", + "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 7\n", + "Number of objective gradient evaluations = 7\n", + "Number of equality constraint evaluations = 7\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 7\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 6\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Restart 4/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", + "theta_2 0.501315\n", + "dtype: float64\n", + "Setting theta_2 to -0.9474484585225582\n", + "Current value of theta_1 is 1.1758659072220325\n", + "Current value of theta_2 is -0.9474484585225582\n", + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", + " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", + " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", + " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", + " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", + " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 6\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", + "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", + "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", + "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 7\n", + "Number of objective gradient evaluations = 7\n", + "Number of equality constraint evaluations = 7\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 7\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 6\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Restart 5/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", + "theta_2 0.501315\n", + "dtype: float64\n", + "Setting theta_2 to 0.5963075272738934\n", + "Current value of theta_1 is 0.4340350441634655\n", + "Current value of theta_2 is 0.5963075272738934\n", + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", + " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", + " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", + " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", + " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", + " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 6\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", + "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", + "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", + "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 7\n", + "Number of objective gradient evaluations = 7\n", + "Number of equality constraint evaluations = 7\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 7\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 6\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Restart 6/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", + "theta_2 0.501315\n", + "dtype: float64\n", + "Setting theta_2 to -1.03783418238163\n", + "Current value of theta_1 is -1.2908396199345589\n", + "Current value of theta_2 is -1.03783418238163\n", + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", + " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", + " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", + " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", + " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", + " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 6\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", + "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", + "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", + "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 7\n", + "Number of objective gradient evaluations = 7\n", + "Number of equality constraint evaluations = 7\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 7\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 6\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Restart 7/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", + "theta_2 0.501315\n", + "dtype: float64\n", + "Setting theta_2 to 1.637305174022913\n", + "Current value of theta_1 is -1.114986129105091\n", + "Current value of theta_2 is 1.637305174022913\n", + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", + " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", + " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", + " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", + " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", + " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 6\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", + "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", + "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", + "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 7\n", + "Number of objective gradient evaluations = 7\n", + "Number of equality constraint evaluations = 7\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 7\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 6\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Restart 8/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", + "theta_2 0.501315\n", + "dtype: float64\n", + "Setting theta_2 to -0.19602123647928238\n", + "Current value of theta_1 is 0.21885917708277702\n", + "Current value of theta_2 is -0.19602123647928238\n", + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", + " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", + " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", + " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", + " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", + " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 6\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", + "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", + "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", + "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 7\n", + "Number of objective gradient evaluations = 7\n", + "Number of equality constraint evaluations = 7\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 7\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 6\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Restart 9/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", + "theta_2 0.501315\n", + "dtype: float64\n", + "Setting theta_2 to 0.34378181397914886\n", + "Current value of theta_1 is 1.4770240969955921\n", + "Current value of theta_2 is 0.34378181397914886\n", + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", + " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", + " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", + " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", + " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", + " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 6\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", + "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", + "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", + "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 7\n", + "Number of objective gradient evaluations = 7\n", + "Number of equality constraint evaluations = 7\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 7\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 6\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Restart 10/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", + "theta_2 0.501315\n", + "dtype: float64\n", + "Setting theta_2 to -1.7853094227612019\n", + "Current value of theta_1 is -0.37290745973587036\n", + "Current value of theta_2 is -1.7853094227612019\n", + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", + " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", + " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", + " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", + " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", + " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 6\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", + "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", + "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", + "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 7\n", + "Number of objective gradient evaluations = 7\n", + "Number of equality constraint evaluations = 7\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 7\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 6\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Restart 11/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", + "theta_2 0.501315\n", + "dtype: float64\n", + "Setting theta_2 to 0.764187891036272\n", + "Current value of theta_1 is -0.8403021283447742\n", + "Current value of theta_2 is 0.764187891036272\n", + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", + " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", + " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", + " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", + " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", + " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 6\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", + "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", + "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", + "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 7\n", + "Number of objective gradient evaluations = 7\n", + "Number of equality constraint evaluations = 7\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 7\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 6\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Restart 12/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", + "theta_2 0.501315\n", + "dtype: float64\n", + "Setting theta_2 to -1.3229034766554832\n", + "Current value of theta_1 is 1.9471220299601555\n", + "Current value of theta_2 is -1.3229034766554832\n", + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", + " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", + " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", + " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", + " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", + " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 6\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", + "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", + "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", + "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 7\n", + "Number of objective gradient evaluations = 7\n", + "Number of equality constraint evaluations = 7\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 7\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 6\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Restart 13/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", + "theta_2 0.501315\n", + "dtype: float64\n", + "Setting theta_2 to 1.2207895517349243\n", + "Current value of theta_1 is 0.6892080903053284\n", + "Current value of theta_2 is 1.2207895517349243\n", + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", + " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", + " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", + " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", + " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", + " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 6\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", + "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", + "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", + "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 7\n", + "Number of objective gradient evaluations = 7\n", + "Number of equality constraint evaluations = 7\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 7\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 6\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Restart 14/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", + "theta_2 0.501315\n", + "dtype: float64\n", + "Setting theta_2 to -0.6623185910284519\n", + "Current value of theta_1 is -1.5821445994079113\n", + "Current value of theta_2 is -0.6623185910284519\n", + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", + " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", + " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", + " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", + " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", + " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 6\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", + "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", + "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", + "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 7\n", + "Number of objective gradient evaluations = 7\n", + "Number of equality constraint evaluations = 7\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 7\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 6\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Restart 15/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", + "theta_2 0.501315\n", + "dtype: float64\n", + "Setting theta_2 to 1.077410764992237\n", + "Current value of theta_1 is -1.7440308779478073\n", + "Current value of theta_2 is 1.077410764992237\n", + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", + " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", + " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", + " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", + " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", + " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 6\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", + "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", + "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", + "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 7\n", + "Number of objective gradient evaluations = 7\n", + "Number of equality constraint evaluations = 7\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 7\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 6\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Restart 16/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", + "theta_2 0.501315\n", + "dtype: float64\n", + "Setting theta_2 to -0.5111263506114483\n", + "Current value of theta_1 is 0.6057177819311619\n", + "Current value of theta_2 is -0.5111263506114483\n", + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", + " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", + " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", + " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", + " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", + " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 6\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", + "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", + "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", + "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 7\n", + "Number of objective gradient evaluations = 7\n", + "Number of equality constraint evaluations = 7\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 7\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 6\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Restart 17/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", + "theta_2 0.501315\n", + "dtype: float64\n", + "Setting theta_2 to 0.909992840141058\n", + "Current value of theta_1 is 1.84779679402709\n", + "Current value of theta_2 is 0.909992840141058\n", + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", + " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", + " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", + " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", + " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", + " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 6\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", + "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", + "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", + "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 7\n", + "Number of objective gradient evaluations = 7\n", + "Number of equality constraint evaluations = 7\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 7\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 6\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Restart 18/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", + "theta_2 0.501315\n", + "dtype: float64\n", + "Setting theta_2 to -1.4765218794345856\n", + "Current value of theta_1 is -0.9858651980757713\n", + "Current value of theta_2 is -1.4765218794345856\n", + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", + " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", + " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", + " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", + " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", + " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 6\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", + "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", + "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", + "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 7\n", + "Number of objective gradient evaluations = 7\n", + "Number of equality constraint evaluations = 7\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 7\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 6\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Restart 19/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", + "theta_2 0.501315\n", + "dtype: float64\n", + "Setting theta_2 to 0.4505395218729973\n", + "Current value of theta_1 is -0.45326995477080345\n", + "Current value of theta_2 is 0.4505395218729973\n", + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", + " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", + " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", + " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", + " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", + " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 6\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", + "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", + "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", + "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 7\n", + "Number of objective gradient evaluations = 7\n", + "Number of equality constraint evaluations = 7\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 7\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 6\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Restart 20/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", + "theta_2 0.501315\n", + "dtype: float64\n", + "Setting theta_2 to -1.8842555843293667\n", + "Current value of theta_1 is 1.3178998976945877\n", + "Current value of theta_2 is -1.8842555843293667\n", + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", + " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", + " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", + " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", + " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", + " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 6\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", + "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", + "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", + "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 7\n", + "Number of objective gradient evaluations = 7\n", + "Number of equality constraint evaluations = 7\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 7\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 6\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Restart 21/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", + "theta_2 0.501315\n", + "dtype: float64\n", + "Setting theta_2 to 1.5330041460692883\n", + "Current value of theta_1 is 0.07605770975351334\n", + "Current value of theta_2 is 1.5330041460692883\n", + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", + " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", + " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", + " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", + " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", + " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 6\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", + "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", + "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", + "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 7\n", + "Number of objective gradient evaluations = 7\n", + "Number of equality constraint evaluations = 7\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 7\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 6\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Restart 22/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", + "theta_2 0.501315\n", + "dtype: float64\n", + "Setting theta_2 to -0.09953175485134125\n", + "Current value of theta_1 is -1.211183074861765\n", + "Current value of theta_2 is -0.09953175485134125\n", + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", + " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", + " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", + " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", + " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", + " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 6\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", + "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", + "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", + "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 7\n", + "Number of objective gradient evaluations = 7\n", + "Number of equality constraint evaluations = 7\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 7\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 6\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Restart 23/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", + "theta_2 0.501315\n", + "dtype: float64\n", + "Setting theta_2 to 0.7029270566999912\n", + "Current value of theta_1 is -1.3790867365896702\n", + "Current value of theta_2 is 0.7029270566999912\n", + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", + " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", + " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", + " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", + " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", + " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 6\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", + "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", + "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", + "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 7\n", + "Number of objective gradient evaluations = 7\n", + "Number of equality constraint evaluations = 7\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 7\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 6\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Restart 24/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", + "theta_2 0.501315\n", + "dtype: float64\n", + "Setting theta_2 to -1.136644072830677\n", + "Current value of theta_1 is 0.2671399489045143\n", + "Current value of theta_2 is -1.136644072830677\n", + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", + " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", + " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", + " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", + " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", + " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 6\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", + "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", + "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", + "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 7\n", + "Number of objective gradient evaluations = 7\n", + "Number of equality constraint evaluations = 7\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 7\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 6\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Restart 25/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", + "theta_2 0.501315\n", + "dtype: float64\n", + "Setting theta_2 to 1.2845380455255508\n", + "Current value of theta_1 is 1.0253018885850906\n", + "Current value of theta_2 is 1.2845380455255508\n", + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", + " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", + " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", + " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", + " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", + " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 6\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", + "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", + "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", + "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 7\n", + "Number of objective gradient evaluations = 7\n", + "Number of equality constraint evaluations = 7\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 7\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 6\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Restart 26/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", + "theta_2 0.501315\n", + "dtype: float64\n", + "Setting theta_2 to -0.8510647006332874\n", + "Current value of theta_1 is -0.13700413331389427\n", + "Current value of theta_2 is -0.8510647006332874\n", + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", + " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", + " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", + " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", + " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", + " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 6\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", + "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", + "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", + "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 7\n", + "Number of objective gradient evaluations = 7\n", + "Number of equality constraint evaluations = 7\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 7\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 6\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Restart 27/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", + "theta_2 0.501315\n", + "dtype: float64\n", + "Setting theta_2 to 1.8259997852146626\n", + "Current value of theta_1 is -0.6698363646864891\n", + "Current value of theta_2 is 1.8259997852146626\n", + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", + " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", + " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", + " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", + " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", + " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 6\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", + "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", + "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", + "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 7\n", + "Number of objective gradient evaluations = 7\n", + "Number of equality constraint evaluations = 7\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 7\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 6\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Restart 28/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", + "theta_2 0.501315\n", + "dtype: float64\n", + "Setting theta_2 to -0.25971441715955734\n", + "Current value of theta_1 is 1.5549770556390285\n", + "Current value of theta_2 is -0.25971441715955734\n", + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", + " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", + " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", + " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", + " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", + " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 6\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", + "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", + "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", + "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 7\n", + "Number of objective gradient evaluations = 7\n", + "Number of equality constraint evaluations = 7\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 7\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 6\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Restart 29/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", + "theta_2 0.501315\n", + "dtype: float64\n", + "Setting theta_2 to 0.1574823558330536\n", + "Current value of theta_1 is 0.7970522679388523\n", + "Current value of theta_2 is 0.1574823558330536\n", + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", + " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", + " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", + " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", + " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", + " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 6\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", + "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", + "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", + "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 7\n", + "Number of objective gradient evaluations = 7\n", + "Number of equality constraint evaluations = 7\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 7\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 6\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Restart 30/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", + "theta_2 0.501315\n", + "dtype: float64\n", + "Setting theta_2 to -1.7240123488008976\n", + "Current value of theta_1 is -1.9116670340299606\n", + "Current value of theta_2 is -1.7240123488008976\n", + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", + " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", + " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", + " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", + " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", + " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 6\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", + "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", + "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", + "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 7\n", + "Number of objective gradient evaluations = 7\n", + "Number of equality constraint evaluations = 7\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 7\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 6\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Restart 31/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", + "theta_2 0.501315\n", + "dtype: float64\n", + "Setting theta_2 to 1.9158114604651928\n", + "Current value of theta_1 is -1.9863164275884628\n", + "Current value of theta_2 is 1.9158114604651928\n", + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", + " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", + " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", + " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", + " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", + " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 6\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", + "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", + "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", + "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 7\n", + "Number of objective gradient evaluations = 7\n", + "Number of equality constraint evaluations = 7\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 7\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 6\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Restart 32/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", + "theta_2 0.501315\n", + "dtype: float64\n", + "Setting theta_2 to -0.4823381155729294\n", + "Current value of theta_1 is 0.8480171598494053\n", + "Current value of theta_2 is -0.4823381155729294\n", + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", + " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", + " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", + " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", + " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", + " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 6\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", + "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", + "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", + "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 7\n", + "Number of objective gradient evaluations = 7\n", + "Number of equality constraint evaluations = 7\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 7\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 6\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Restart 33/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", + "theta_2 0.501315\n", + "dtype: float64\n", + "Setting theta_2 to 0.06773220747709274\n", + "Current value of theta_1 is 1.6056938730180264\n", + "Current value of theta_2 is 0.06773220747709274\n", + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", + " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", + " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", + " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", + " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", + " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 6\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", + "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", + "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", + "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 7\n", + "Number of objective gradient evaluations = 7\n", + "Number of equality constraint evaluations = 7\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 7\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 6\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Restart 34/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", + "theta_2 0.501315\n", + "dtype: float64\n", + "Setting theta_2 to -1.5014492236077785\n", + "Current value of theta_1 is -0.7437494024634361\n", + "Current value of theta_2 is -1.5014492236077785\n", + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", + " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", + " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", + " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", + " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", + " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 6\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", + "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", + "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", + "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 7\n", + "Number of objective gradient evaluations = 7\n", + "Number of equality constraint evaluations = 7\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 7\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 6\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Restart 35/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", + "theta_2 0.501315\n", + "dtype: float64\n", + "Setting theta_2 to 0.5428028963506222\n", + "Current value of theta_1 is -0.21115940436720848\n", + "Current value of theta_2 is 0.5428028963506222\n", + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", + " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", + " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", + " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", + " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", + " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 6\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", + "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", + "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", + "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 7\n", + "Number of objective gradient evaluations = 7\n", + "Number of equality constraint evaluations = 7\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 7\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 6\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Restart 36/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", + "theta_2 0.501315\n", + "dtype: float64\n", + "Setting theta_2 to -1.1093328893184662\n", + "Current value of theta_1 is 1.0757764726877213\n", + "Current value of theta_2 is -1.1093328893184662\n", + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", + " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", + " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", + " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", + " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", + " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 6\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", + "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", + "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", + "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 7\n", + "Number of objective gradient evaluations = 7\n", + "Number of equality constraint evaluations = 7\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 7\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 6\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Restart 37/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", + "theta_2 0.501315\n", + "dtype: float64\n", + "Setting theta_2 to 1.4446007087826729\n", + "Current value of theta_1 is 0.3183508887887001\n", + "Current value of theta_2 is 1.4446007087826729\n", + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", + " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", + " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", + " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", + " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", + " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 6\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", + "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", + "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", + "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 7\n", + "Number of objective gradient evaluations = 7\n", + "Number of equality constraint evaluations = 7\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 7\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 6\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Restart 38/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", + "theta_2 0.501315\n", + "dtype: float64\n", + "Setting theta_2 to -0.8783153407275677\n", + "Current value of theta_1 is -1.4534900821745396\n", + "Current value of theta_2 is -0.8783153407275677\n", + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", + " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", + " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", + " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", + " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", + " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 6\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", + "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", + "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", + "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 7\n", + "Number of objective gradient evaluations = 7\n", + "Number of equality constraint evaluations = 7\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 7\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 6\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Restart 39/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", + "theta_2 0.501315\n", + "dtype: float64\n", + "Setting theta_2 to 0.29029324650764465\n", + "Current value of theta_1 is -1.1369038261473179\n", + "Current value of theta_2 is 0.29029324650764465\n", + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", + " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", + " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", + " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", + " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", + " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 6\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", + "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", + "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", + "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 7\n", + "Number of objective gradient evaluations = 7\n", + "Number of equality constraint evaluations = 7\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 7\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 6\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Restart 40/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", + "theta_2 0.501315\n", + "dtype: float64\n", + "Setting theta_2 to -1.8568222858011723\n", + "Current value of theta_1 is 0.024974681437015533\n", + "Current value of theta_2 is -1.8568222858011723\n", + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", + " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", + " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", + " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", + " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", + " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 6\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", + "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", + "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", + "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 7\n", + "Number of objective gradient evaluations = 7\n", + "Number of equality constraint evaluations = 7\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 7\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 6\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Restart 41/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", + "theta_2 0.501315\n", + "dtype: float64\n", + "Setting theta_2 to 1.6931888945400715\n", + "Current value of theta_1 is 1.2675453573465347\n", + "Current value of theta_2 is 1.6931888945400715\n", + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", + " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", + " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", + " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", + " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", + " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 6\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", + "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", + "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", + "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 7\n", + "Number of objective gradient evaluations = 7\n", + "Number of equality constraint evaluations = 7\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 7\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 6\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Restart 42/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", + "theta_2 0.501315\n", + "dtype: float64\n", + "Setting theta_2 to -0.12690448015928268\n", + "Current value of theta_1 is -0.37923091277480125\n", + "Current value of theta_2 is -0.12690448015928268\n", + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", + " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", + " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", + " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", + " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", + " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 6\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", + "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", + "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", + "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 7\n", + "Number of objective gradient evaluations = 7\n", + "Number of equality constraint evaluations = 7\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 7\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 6\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Restart 43/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", + "theta_2 0.501315\n", + "dtype: float64\n", + "Setting theta_2 to 1.1673445254564285\n", + "Current value of theta_1 is -0.912072204053402\n", + "Current value of theta_2 is 1.1673445254564285\n", + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", + " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", + " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", + " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", + " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", + " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 6\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", + "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", + "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", + "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 7\n", + "Number of objective gradient evaluations = 7\n", + "Number of equality constraint evaluations = 7\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 7\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 6\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Restart 44/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", + "theta_2 0.501315\n", + "dtype: float64\n", + "Setting theta_2 to -0.7338721342384815\n", + "Current value of theta_1 is 1.7971962057054043\n", + "Current value of theta_2 is -0.7338721342384815\n", + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", + " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", + " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", + " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", + " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", + " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 6\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", + "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", + "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", + "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 7\n", + "Number of objective gradient evaluations = 7\n", + "Number of equality constraint evaluations = 7\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 7\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 6\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Restart 45/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", + "theta_2 0.501315\n", + "dtype: float64\n", + "Setting theta_2 to 0.8201205767691135\n", + "Current value of theta_1 is 0.5548769868910313\n", + "Current value of theta_2 is 0.8201205767691135\n", + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", + " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", + " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", + " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", + " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", + " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 6\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", + "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", + "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", + "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 7\n", + "Number of objective gradient evaluations = 7\n", + "Number of equality constraint evaluations = 7\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 7\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 6\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Restart 46/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", + "theta_2 0.501315\n", + "dtype: float64\n", + "Setting theta_2 to -1.253836639225483\n", + "Current value of theta_1 is -1.669509395956993\n", + "Current value of theta_2 is -1.253836639225483\n", + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", + " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", + " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", + " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", + " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", + " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 6\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", + "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", + "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", + "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 7\n", + "Number of objective gradient evaluations = 7\n", + "Number of equality constraint evaluations = 7\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 7\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 6\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Restart 47/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", + "theta_2 0.501315\n", + "dtype: float64\n", + "Setting theta_2 to 0.9791254512965679\n", + "Current value of theta_1 is -1.500417035073042\n", + "Current value of theta_2 is 0.9791254512965679\n", + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", + " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", + " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", + " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", + " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", + " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 6\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", + "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", + "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", + "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 7\n", + "Number of objective gradient evaluations = 7\n", + "Number of equality constraint evaluations = 7\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 7\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 6\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Restart 48/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", + "theta_2 0.501315\n", + "dtype: float64\n", + "Setting theta_2 to -1.420653060078621\n", + "Current value of theta_1 is 0.6462979316711426\n", + "Current value of theta_2 is -1.420653060078621\n", + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", + " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", + " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", + " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", + " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", + " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 6\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", + "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", + "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", + "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 7\n", + "Number of objective gradient evaluations = 7\n", + "Number of equality constraint evaluations = 7\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 7\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 6\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Restart 49/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", + "theta_2 0.501315\n", + "dtype: float64\n", + "Setting theta_2 to 1.0059135183691978\n", + "Current value of theta_1 is 1.9039716646075249\n", + "Current value of theta_2 is 1.0059135183691978\n", + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 3000\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 1003\n", + "\n", + "Total number of variables............................: 1002\n", + " variables with only lower bounds: 0\n", + " variables with lower and upper bounds: 2\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1000\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", + " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", + " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", + " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", + " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", + " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 6\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", + "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", + "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", + "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", + "\n", + "\n", + "Number of objective function evaluations = 7\n", + "Number of objective gradient evaluations = 7\n", + "Number of equality constraint evaluations = 7\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 7\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 6\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "Restart 50/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", + "theta_2 0.501315\n", + "dtype: float64\n", + "\n", + "\n", + "Results DataFrame:\n", + " theta_1 theta_2 converged_theta_1 converged_theta_2 \\\n", + "0 0.904117 -1.570500 -1.5003 0.501315 \n", + "1 1.646200 1.969240 -1.5003 0.501315 \n", + "2 -0.500088 -0.410770 -1.5003 0.501315 \n", + "3 -0.032914 1.388731 -1.5003 0.501315 \n", + "4 1.175866 -0.947448 -1.5003 0.501315 \n", + "5 0.434035 0.596308 -1.5003 0.501315 \n", + "6 -1.290840 -1.037834 -1.5003 0.501315 \n", + "7 -1.114986 1.637305 -1.5003 0.501315 \n", + "8 0.218859 -0.196021 -1.5003 0.501315 \n", + "9 1.477024 0.343782 -1.5003 0.501315 \n", + "10 -0.372907 -1.785309 -1.5003 0.501315 \n", + "11 -0.840302 0.764188 -1.5003 0.501315 \n", + "12 1.947122 -1.322903 -1.5003 0.501315 \n", + "13 0.689208 1.220790 -1.5003 0.501315 \n", + "14 -1.582145 -0.662319 -1.5003 0.501315 \n", + "15 -1.744031 1.077411 -1.5003 0.501315 \n", + "16 0.605718 -0.511126 -1.5003 0.501315 \n", + "17 1.847797 0.909993 -1.5003 0.501315 \n", + "18 -0.985865 -1.476522 -1.5003 0.501315 \n", + "19 -0.453270 0.450540 -1.5003 0.501315 \n", + "20 1.317900 -1.884256 -1.5003 0.501315 \n", + "21 0.076058 1.533004 -1.5003 0.501315 \n", + "22 -1.211183 -0.099532 -1.5003 0.501315 \n", + "23 -1.379087 0.702927 -1.5003 0.501315 \n", + "24 0.267140 -1.136644 -1.5003 0.501315 \n", + "25 1.025302 1.284538 -1.5003 0.501315 \n", + "26 -0.137004 -0.851065 -1.5003 0.501315 \n", + "27 -0.669836 1.826000 -1.5003 0.501315 \n", + "28 1.554977 -0.259714 -1.5003 0.501315 \n", + "29 0.797052 0.157482 -1.5003 0.501315 \n", + "30 -1.911667 -1.724012 -1.5003 0.501315 \n", + "31 -1.986316 1.915811 -1.5003 0.501315 \n", + "32 0.848017 -0.482338 -1.5003 0.501315 \n", + "33 1.605694 0.067732 -1.5003 0.501315 \n", + "34 -0.743749 -1.501449 -1.5003 0.501315 \n", + "35 -0.211159 0.542803 -1.5003 0.501315 \n", + "36 1.075776 -1.109333 -1.5003 0.501315 \n", + "37 0.318351 1.444601 -1.5003 0.501315 \n", + "38 -1.453490 -0.878315 -1.5003 0.501315 \n", + "39 -1.136904 0.290293 -1.5003 0.501315 \n", + "40 0.024975 -1.856822 -1.5003 0.501315 \n", + "41 1.267545 1.693189 -1.5003 0.501315 \n", + "42 -0.379231 -0.126904 -1.5003 0.501315 \n", + "43 -0.912072 1.167345 -1.5003 0.501315 \n", + "44 1.797196 -0.733872 -1.5003 0.501315 \n", + "45 0.554877 0.820121 -1.5003 0.501315 \n", + "46 -1.669509 -1.253837 -1.5003 0.501315 \n", + "47 -1.500417 0.979125 -1.5003 0.501315 \n", + "48 0.646298 -1.420653 -1.5003 0.501315 \n", + "49 1.903972 1.005914 -1.5003 0.501315 \n", + "\n", + " initial objective final objective solver termination solve_time \n", + "0 7.220190e+08 428163.296519 successful NaN \n", + "1 1.136218e+10 428163.296519 successful NaN \n", + "2 4.193775e+09 428163.296519 successful NaN \n", + "3 4.072387e+09 428163.296519 successful NaN \n", + "4 8.684940e+07 428163.296519 successful NaN \n", + "5 3.426209e+09 428163.296519 successful NaN \n", + "6 1.655079e+09 428163.296519 successful NaN \n", + "7 9.394389e+08 428163.296519 successful NaN \n", + "8 4.087402e+09 428163.296519 successful NaN \n", + "9 1.685132e+09 428163.296519 successful NaN \n", + "10 3.826184e+09 428163.296519 successful NaN \n", + "11 2.305593e+09 428163.296519 successful NaN \n", + "12 1.052740e+10 428163.296519 successful NaN \n", + "13 2.766225e+09 428163.296519 successful NaN \n", + "14 1.074960e+09 428163.296519 successful NaN \n", + "15 1.255159e+09 428163.296519 successful NaN \n", + "16 2.216156e+09 428163.296519 successful NaN \n", + "17 9.265699e+09 428163.296519 successful NaN \n", + "18 2.791493e+09 428163.296519 successful NaN \n", + "19 4.117223e+09 428163.296519 successful NaN \n", + "20 1.783957e+09 428163.296519 successful NaN \n", + "21 3.932326e+09 428163.296519 successful NaN \n", + "22 9.345576e+08 428163.296519 successful NaN \n", + "23 1.248299e+08 428163.296519 successful NaN \n", + "24 3.404587e+09 428163.296519 successful NaN \n", + "25 2.374101e+09 428163.296519 successful NaN \n", + "26 4.522286e+09 428163.296519 successful NaN \n", + "27 2.285725e+09 428163.296519 successful NaN \n", + "28 1.544874e+09 428163.296519 successful NaN \n", + "29 1.609095e+09 428163.296519 successful NaN \n", + "30 7.979763e+09 428163.296519 successful NaN \n", + "31 7.531637e+09 428163.296519 successful NaN \n", + "32 1.022801e+09 428163.296519 successful NaN \n", + "33 2.387392e+09 428163.296519 successful NaN \n", + "34 3.402058e+09 428163.296519 successful NaN \n", + "35 4.564792e+09 428163.296519 successful NaN \n", + "36 2.227816e+08 428163.296519 successful NaN \n", + "37 3.700507e+09 428163.296519 successful NaN \n", + "38 1.293657e+09 428163.296519 successful NaN \n", + "39 1.049181e+09 428163.296519 successful NaN \n", + "40 3.409571e+09 428163.296519 successful NaN \n", + "41 4.407327e+09 428163.296519 successful NaN \n", + "42 4.488860e+09 428163.296519 successful NaN \n", + "43 1.760318e+09 428163.296519 successful NaN \n", + "44 5.155549e+09 428163.296519 successful NaN \n", + "45 3.009035e+09 428163.296519 successful NaN \n", + "46 2.948710e+09 428163.296519 successful NaN \n", + "47 1.650676e+08 428163.296519 successful NaN \n", + "48 1.612714e+09 428163.296519 successful NaN \n", + "49 1.129338e+10 428163.296519 successful NaN \n", + "\n", + "Unique converged_theta_1 values: [-1.50030035]\n", + "Unique converged_theta_2 values: [0.5013147]\n" + ] + } + ], + "source": [ + "exp_list = [] \n", + "conc = data_df[\"x\"].values # substrate concentration (control variable)\n", + "vel = data_df[\"y\"].values # reaction velocity (output variable)\n", + "n_exp = 1\n", + "\n", + "# exp_list to separate each experiment\n", + "# for i in range(n_exp):\n", + "exp_list.append(Simple_Multimodal(data_df))\n", + "\n", + "# Creating an Estimator object\n", + "pest = parmest.Estimator(exp_list, tee = True) \n", + "\n", + "# obj, theta = pest.theta_est()\n", + "results_df = pest.theta_est_multistart(multistart_sampling_method=\"sobol\", n_restarts=50, seed = 12345)\n", + "\n", + "print(\"\\n\\nResults DataFrame:\")\n", + "print(results_df)\n", + "\n", + "# # Print unique parameter values\n", + "print(\"\\nUnique converged_theta_1 values:\", results_df['converged_theta_1'].unique())\n", + "print(\"Unique converged_theta_2 values:\", results_df['converged_theta_2'].unique())" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "parmest-dev-mac2", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 1695519b623cbf5bf6d1860be89362401e017012 Mon Sep 17 00:00:00 2001 From: Stephen Cini <114932899+sscini@users.noreply.github.com> Date: Thu, 5 Jun 2025 08:18:16 -0400 Subject: [PATCH 14/23] New features Thursday morning --- pyomo/contrib/parmest/parmest.py | 85 ++++++++++++++++++++------------ 1 file changed, 54 insertions(+), 31 deletions(-) diff --git a/pyomo/contrib/parmest/parmest.py b/pyomo/contrib/parmest/parmest.py index 4aa5eb81016..b6f371a9b70 100644 --- a/pyomo/contrib/parmest/parmest.py +++ b/pyomo/contrib/parmest/parmest.py @@ -65,6 +65,10 @@ import pyomo.contrib.parmest.graphics as graphics from pyomo.dae import ContinuousSet +# Add imports for HierchicalTimer +import time +from pyomo.common.timing import TicTocTimer + from pyomo.common.deprecation import deprecated from pyomo.common.deprecation import deprecation_warning @@ -209,7 +213,10 @@ def _experiment_instance_creation_callback( scen_model=instance, ) ] - + # @Reviewers, here is where the parmest model is made for each run + # This is the only way I see to pass the theta values to the model + # Can we add an optional argument to fix them or not? + # Curently, thetavals provided are fixed if not None if "ThetaVals" in outer_cb_data: thetavals = outer_cb_data["ThetaVals"] @@ -1088,28 +1095,34 @@ def theta_est_multistart( ---------- n_restarts: int, optional Number of restarts for multistart. Default is 1. - th_sampling_method: string, optional - Method used to sample theta values. Options are "uniform", "latin_hypercube", or "sobol". + multistart_sampling_method: string, optional + Method used to sample theta values. Options are "uniform", "latin_hypercube", "sobol", or "user_provided". Default is "uniform". buffer: int, optional Number of iterations to save results dynamically. Default is 10. - user_provided: pd.DataFrame, optional - User provided dataframe of theta values for multistart optimization. + user_provided: pd.DataFrame or np.ndarray, optional + User provided array or dataframe of theta values for multistart optimization. + seed: int, optional + Random seed for reproducibility. + save_results: bool, optional + If True, intermediate and final results are saved to file_name. + theta_vals: pd.DataFrame, optional + Initial theta values for restarts (overrides sampling). solver: string, optional Currently only "ef_ipopt" is supported. Default is "ef_ipopt". + file_name: str, optional + File name for saving results if save_results is True. return_values: list, optional - List of Variable names, used to return values from the model for data reconciliation - + List of Variable names, used to return values from the model for data reconciliation. Returns ------- - objectiveval: float - The objective function value - thetavals: pd.Series - Estimated values for theta - variable values: pd.DataFrame - Variable values for each variable name in return_values (only for solver='ef_ipopt') - + results_df: pd.DataFrame + DataFrame containing initial and converged theta values, objectives, and solver info for each restart. + best_theta: dict + Dictionary of theta values corresponding to the best (lowest) objective value found. + best_objectiveval: float + The best (lowest) objective function value found across all restarts. """ # check if we are using deprecated parmest @@ -1144,24 +1157,32 @@ def theta_est_multistart( theta_vals = results_df.iloc[:, :len(theta_names)] converged_theta_vals = np.zeros((n_restarts, len(theta_names))) + timer = TicTocTimer() + # Each restart uses a fresh model instance for i in range(n_restarts): - # Create a fresh model for each restart - parmest_model = self._create_parmest_model(experiment_number=0) + + # Add a timer for each restart + timer.tic(f"Restart {i+1}/{n_restarts}") + + # No longer needed, keeping until confirming update works as expected + # # Create a fresh model for each restart + # parmest_model = self._create_parmest_model(experiment_number=0) theta_vals_current = theta_vals.iloc[i, :].to_dict() - # Set current theta values in the model - for name, value in theta_vals_current.items(): - parmest_model.find_component(name).set_value(value) + # # Set current theta values in the model + # for name, value in theta_vals_current.items(): + # parmest_model.find_component(name).set_value(value) - # Optional: Print the current theta values being set - print(f"Setting {name} to {value}") - for name in theta_names: - current_value = parmest_model.find_component(name)() - print(f"Current value of {name} is {current_value}") + # # Optional: Print the current theta values being set + # print(f"Setting {name} to {value}") + # for name in theta_names: + # current_value = parmest_model.find_component(name)() + # print(f"Current value of {name} is {current_value}") # Call the _Q_opt method with the generated theta values qopt_result = self._Q_opt( + ThetaVals=theta_vals_current, bootlist=None, solver=solver, return_values=return_values, @@ -1177,7 +1198,6 @@ def theta_est_multistart( if converged_theta.isnull().any(): solver_termination = "not successful" solve_time = np.nan - thetavals = np.nan final_objectiveval = np.nan init_objectiveval = np.nan else: @@ -1194,14 +1214,17 @@ def theta_est_multistart( # # Check if the objective value is better than the best objective value # # Set a very high initial best objective value - # best_objectiveval = np.inf - # best_theta = np.inf - # if final_objectiveval < best_objectiveval: - # best_objectiveval = objectiveval - # best_theta = thetavals + best_objectiveval = np.inf + best_theta = np.inf + if final_objectiveval < best_objectiveval: + best_objectiveval = objectiveval + best_theta = theta_vals_current print(f"Restart {i+1}/{n_restarts}: Objective Value = {final_objectiveval}, Theta = {converged_theta}") + # Stop the timer for this restart + solve_time = timer.toc(f"Restart {i+1}/{n_restarts}") + # Store the results in the DataFrame for this restart # Fill converged theta values for j, name in enumerate(theta_names): @@ -1229,7 +1252,7 @@ def theta_est_multistart( results_df.to_csv(file_name, mode='a', header=False, index=False) print("Final results saved.") - return results_df # just this for now, then best_theta, best_objectiveval + return results_df, best_theta, best_objectiveval From 063401472127293441e7169d72aecb5049bfcb61 Mon Sep 17 00:00:00 2001 From: Stephen Cini <114932899+sscini@users.noreply.github.com> Date: Thu, 5 Jun 2025 10:41:11 -0400 Subject: [PATCH 15/23] First successful running multistart feature, before Alex recommended changes --- pyomo/contrib/parmest/experiment.py | 8 ++++++++ pyomo/contrib/parmest/parmest.py | 25 ++++++++++++++++++------- 2 files changed, 26 insertions(+), 7 deletions(-) diff --git a/pyomo/contrib/parmest/experiment.py b/pyomo/contrib/parmest/experiment.py index 67a8be71f66..16411858095 100644 --- a/pyomo/contrib/parmest/experiment.py +++ b/pyomo/contrib/parmest/experiment.py @@ -29,3 +29,11 @@ def __init__(self, model=None): def get_labeled_model(self): return self.model + + def reinitialize_unknown_parameters(self): + raise NotImplementedError( + "The reinitialize_unknown_parameters method should implemented in the subclass." \ + "Thi method will take new values for the unknown parameters from the Suffix " + "and allow users to reinitialize the model." + ) + diff --git a/pyomo/contrib/parmest/parmest.py b/pyomo/contrib/parmest/parmest.py index b6f371a9b70..2764983942a 100644 --- a/pyomo/contrib/parmest/parmest.py +++ b/pyomo/contrib/parmest/parmest.py @@ -91,7 +91,7 @@ def ef_nonants(ef): def _experiment_instance_creation_callback( - scenario_name, node_names=None, cb_data=None + scenario_name, node_names=None, cb_data=None, fix_vars=False, ): """ This is going to be called by mpi-sppy or the local EF and it will call into @@ -107,6 +107,8 @@ def _experiment_instance_creation_callback( that is the "callback" value. "BootList" is None or bootstrap experiment number list. (called cb_data by mpisppy) + fix_vars: `bool` If True, the theta variables are fixed to the values + provided in the cb_data["ThetaVals"] dictionary. Returns: @@ -217,6 +219,7 @@ def _experiment_instance_creation_callback( # This is the only way I see to pass the theta values to the model # Can we add an optional argument to fix them or not? # Curently, thetavals provided are fixed if not None + # Suggested fix in this function and _Q_at_theta if "ThetaVals" in outer_cb_data: thetavals = outer_cb_data["ThetaVals"] @@ -224,9 +227,14 @@ def _experiment_instance_creation_callback( for name, val in thetavals.items(): theta_cuid = ComponentUID(name) theta_object = theta_cuid.find_component_on(instance) - if val is not None: + if val is not None and fix_vars is True: # print("Fixing",vstr,"at",str(thetavals[vstr])) theta_object.fix(val) + # ADDED OPTION: Set initial value, but do not fix + elif val is not None and fix_vars is False: + # print("Setting",vstr,"to",str(thetavals[vstr])) + theta_object.set_value(val) + theta_object.unfix() else: # print("Freeing",vstr) theta_object.unfix() @@ -829,7 +837,7 @@ def _Q_at_theta(self, thetavals, initialize_parmest_model=False): # start block of code to deal with models with no constraints # (ipopt will crash or complain on such problems without special care) - instance = _experiment_instance_creation_callback("FOO0", None, dummy_cb) + instance = _experiment_instance_creation_callback("FOO0", None, dummy_cb,) try: # deal with special problems so Ipopt will not crash first = next(instance.component_objects(pyo.Constraint, active=True)) active_constraints = True @@ -846,7 +854,7 @@ def _Q_at_theta(self, thetavals, initialize_parmest_model=False): for snum in scenario_numbers: sname = "scenario_NODE" + str(snum) - instance = _experiment_instance_creation_callback(sname, None, dummy_cb) + instance = _experiment_instance_creation_callback(sname, None, dummy_cb, fix_vars=True) model_theta_names = self._expand_indexed_unknowns(instance) if initialize_parmest_model: @@ -1214,11 +1222,14 @@ def theta_est_multistart( # # Check if the objective value is better than the best objective value # # Set a very high initial best objective value - best_objectiveval = np.inf - best_theta = np.inf + if i == 0: + # Initialize best objective value and theta + best_objectiveval = np.inf + best_theta = np.inf + # Check if the final objective value is better than the best found so far if final_objectiveval < best_objectiveval: best_objectiveval = objectiveval - best_theta = theta_vals_current + best_theta = converged_theta.values print(f"Restart {i+1}/{n_restarts}: Objective Value = {final_objectiveval}, Theta = {converged_theta}") From 06e0a7289a12b601e2bad95f7eaba7ee9810f07c Mon Sep 17 00:00:00 2001 From: Stephen Cini <114932899+sscini@users.noreply.github.com> Date: Tue, 24 Jun 2025 09:33:44 -0400 Subject: [PATCH 16/23] Ran black, removed temp example --- .../Simple_Multimodal_Multistart.ipynb | 8231 ----------------- .../reactor_design/multistart_example.py | 7 +- pyomo/contrib/parmest/experiment.py | 3 +- pyomo/contrib/parmest/parmest.py | 149 +- 4 files changed, 98 insertions(+), 8292 deletions(-) delete mode 100644 pyomo/contrib/parmest/examples/Simple_Multimodal_Multistart.ipynb diff --git a/pyomo/contrib/parmest/examples/Simple_Multimodal_Multistart.ipynb b/pyomo/contrib/parmest/examples/Simple_Multimodal_Multistart.ipynb deleted file mode 100644 index 0c2a2eb2062..00000000000 --- a/pyomo/contrib/parmest/examples/Simple_Multimodal_Multistart.ipynb +++ /dev/null @@ -1,8231 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "d3d4744d-5ea5-45d6-8e8b-aefc5d8b8118", - "metadata": {}, - "source": [ - "This example is created by Stephen Cini, inspired by example in Montana's manuscript and advice from Dr. Alex Dowling. Purpose is to test multistart function's ability to find local minima.\n", - "\n", - "**Equation:** \n", - " $$f(x, \\theta) = (\\theta_1 x^3 - \\theta_2 x^2 + 2x - 1)^2 + (\\theta_1 - \\theta_2)^2 + (x^2 - 1)^2$$ \n", - "$\\qquad f(x, \\theta)\\rightarrow$ (response / output variable) \n", - "$\\qquad \\theta_1, \\theta_2 \\rightarrow$ parameters \n", - "$\\qquad x\\rightarrow$ (control / decision variable) \n", - "\n", - "**Data:** \n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
SubstrateVelocity
Concentration(counts/min2)
(ppm)(a) Treated(b) Untreated
0.0276
47		67
51
0.0697
107		84
86
0.11123
139		98
115
0.22159
152		131
124
0.56191
201		144
158
1.10207
200		160
\n", - "\n", - "[Find data or remove table]" - ] - }, - { - "cell_type": "markdown", - "id": "7f241a55-464a-41f6-9bc2-28f7467c2234", - "metadata": {}, - "source": [ - "# Importing packages" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "2a458e95", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Pyomo version after reload:\n", - "6.9.3.dev0\n", - "pyomo.__file__: /Users/scini/Documents/GitHub/pyomo/pyomo/__init__.py\n" - ] - } - ], - "source": [ - "# Get version of pyomo\n", - "\n", - "# Force reload of pyomo module to ensure we get the latest version\n", - "import importlib\n", - "import pyomo # your .py file without the .py extension\n", - "importlib.reload(pyomo)\n", - "print(\"Pyomo version after reload:\")\n", - "print(pyomo.__version__)\n", - "print(\"pyomo.__file__:\", pyomo.__file__)\n", - "\n", - "import pyomo.environ as pyo\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "f6c3dd5b-0200-4262-9315-de03690bec0b", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import scipy\n", - "import seaborn as sns # This package is only needed for pairplot\n", - "import matplotlib.pyplot as plt\n", - "import pyomo.environ as pyo\n", - "import pyomo.contrib.parmest.parmest as parmest # import parmest\n", - "import pyomo.contrib.parmest.experiment as experiment\n", - "import pyomo.contrib.doe as doe\n", - "import pandas as pd\n", - "from itertools import product\n", - "import idaes" - ] - }, - { - "cell_type": "markdown", - "id": "5915d030-b939-4162-a85c-9f90c28a17c9", - "metadata": {}, - "source": [ - "# Data\n", - "Data for this model in Montana's manuscript was generated by simulating the model with true parameter values and adding 1% Gaussian error. So this is replicated here." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "231649a2-75e9-43af-938f-00b71ff136e8", - "metadata": {}, - "outputs": [], - "source": [ - "# True param values\n", - "true_params = {\n", - " 'theta1': -1.5,\n", - " 'theta2': 0.5,\n", - "}\n", - "\n", - "# $$f(x, \\theta) = (\\theta_1 x^3 - \\theta_2 x^2 + 2x - 1)^2 + (\\theta_1 - \\theta_2)^2 + (x^2 - 1)^2$$ \n", - "\n", - "def model(x, theta1, theta2):\n", - " return ((theta1 * x**3 - theta2 * x**2 + 2 * x - 1)**2 +\n", - " (theta1 - theta2)**2 +\n", - " (x**2 - 1)**2)\n", - "\n", - "def generate_data(num_samples=1000):\n", - " x_values = np.linspace(-4, 4, num_samples)\n", - " y_values = np.array([model(x, true_params['theta1'], true_params['theta2']) for x in x_values])\n", - "\n", - " # Add 1% Gaussian noise to the y values\n", - " noise = np.random.normal(0, 0.01 * np.abs(y_values), size=y_values.shape)\n", - " y_values += noise\n", - " # Create a DataFrame with the x and y values\n", - " # and return it\n", - " return pd.DataFrame({'x': x_values, 'y': y_values})\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "ae697125", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Data generated with 1000 samples.\n", - " x y\n", - "0 -4.000000 6515.412188\n", - "1 -3.991992 6367.720590\n", - "2 -3.983984 6257.737250\n", - "3 -3.975976 6182.891231\n", - "4 -3.967968 6013.270490\n" - ] - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Make some data\n", - "data_df = generate_data()\n", - "print(\"Data generated with {} samples.\".format(len(data_df)))\n", - "\n", - "print(data_df.head())\n", - "\n", - "# Plot the data\n", - "plt.figure(figsize=(10, 6))\n", - "sns.scatterplot(data=data_df, x='x', y='y', color='blue', label='Data')\n", - "plt.title('Generated Data')\n", - "plt.xlabel('x')\n", - "plt.ylabel('y')\n", - "plt.legend()\n", - "plt.grid()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "580a2f7a", - "metadata": {}, - "outputs": [], - "source": [ - "conc = data_df['x'].values\n", - "vel = data_df['y'].values" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "7172aa46", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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Vq1YAgNLSUrz66qv44IMP8N5771l1GAsKCmLLucP169cRExNj88dYVFQUtFqt1X06cuRIbNq0CZmZmejWrRv7x45ZgAHu964ZR/nFfKhcubLVunnkB/N7/++//wIApkyZgilTpnBqX3m43mfeYPPmzRg4cCAef/xxvPzyy4iJiYFWq8WyZcvs9sov//oAZa+R5WfDZDI5/KwJgZzfo1OnTqFLly6oV68edu7cqaiRRcTmzp07aN68OUaMGIH+/fu7fbzRaERwcDDGjRuHTZs22S1z8eJF9OzZE88++yzWrl2LH3/8ES+++CKqVq3K65z+Akmtn6HRaNCxY0d89913+Pvvv+3K0N9//40TJ06gR48eVlEIoKyzUfXq1dl1g8GA69ev2/2CMVOlShVUrlwZ3333nd395miLOdL4999/C9oDd8SIEdi4cSMyMzMRFxeHY8eOYdmyZez+SpUqQaPRYMiQIQ6jxAkJCW6ft3HjxnjiiSewcOFC/PHHH2xnDKDsdSxPXl4e6tatCwD49ddfcfr0aWRkZGDYsGFsmT///NPqmMjISGg0GpcdPKpUqYJmzZph1qxZdvdXq1aN83W54qGHHkLXrl2xdetWXL16FVFRUXbLBQQEYNq0afjggw8E6yVfuXJl/PTTT2AYxkpsr169CoPBgCpVqrDbunXrhmrVqmHVqlXo1q0bVq1ahbZt21qN3sH13jXDZexToTBfS3p6ulXHNkvq16/vtA6u95k3WLt2LRISErBhwwar19Gyg587VKpUCSqVyuFnzRtI9T06deoUOnfujPj4eOzevRsRERG86/JHevTowf7KZQ+9Xo8333wTX3zxBW7duoUmTZpg7ty5bAfqChUqsN9BP/74I27dumVTx/LlyxEXF8eOk9ywYUMcP34c8+bNI6l1AkmtH5Keno5vv/0WL774IrZs2WIlrkajES+88AIYhkF6errNsV988QWSkpLY9a+++goGg8HpZAuPPPIIvvzySxiNRrRt29Zhua5du0Kj0WDZsmVISUlxWM4yGsOFrl27onr16li1ahXi4uIQFBRkFUEMCQlBx44dcerUKTRr1sxhVM4R169fR1hYmN3jfv/9dwC20vjFF19YPZgOHz6My5cv45lnngFwX47KR09WrFhhtR4cHIz27dtj48aNmDVrlpW0WfLII49g586dqFOnDipVquTW9Tni33//RdWqVW16vBuNRly4cAEhISFsdDo3N9duFNP806lQUt2pUyd89dVX2Lp1K/r27ctuX7NmDbvfjPkPmYULF+LgwYM4fvy4zevL9d7lg7v3cXnq16+PevXq4fTp05g9e7bLcwGwOR/X+8wbqFQqBAYGWgltXl6e3dEPuFChQgW0adMGmzdvxvvvv8+mINy+fRs7duzgVIcS36OsrCx07twZNWrUQGZmpmDPA+I+I0aMwKVLl/Dll1+iWrVq2LJlC7p3745ffvkF9erV41THkSNH0LVrV6tt3bp1w8qVK1FaWurwVzd/h6TWD0lLS8PChQsxYcIEtGvXDmPHjkVcXBw7+cJPP/2EhQsXIjU11ebYzZs3Q6vVokuXLjh79iymTp2K5s2bY+DAgQ7P98QTT+CLL75Az549MX78eLRp0wYBAQH4+++/sXfvXvTp0wd9+/ZFrVq18Prrr2PmzJkoKirC4MGDERERgXPnzuHatWvs8EhNmzbF5s2bsWzZMiQlJUGtVjv9qVqj0WDo0KFYsGABwsPD0a9fP5vIxKJFi9CuXTs8+OCDeOGFF1CrVi3cvn0bf/75J3bs2IE9e/Y4rH/v3r0YP348nnrqKaSmpqJy5cq4evUq1q9fj++++w5Dhw61iYgfP34czzzzDB5//HHk5OTgjTfeQPXq1fHiiy8CABo0aIA6dergtddeA8MwiIyMxI4dO5CZmWlz/gULFqBdu3Zo27YtXnvtNdStWxf//vsvtm/fjhUrViAsLAxvv/02MjMzkZqainHjxqF+/fooLi7GpUuXsHPnTixfvpxt46hRo7B69Wr89ddfTvNqP//8c6xYsQJPPvkkWrdujYiICPz999/49NNPcfbsWbz11lus6Hfr1g01atRA79690aBBA5hMJmRlZWH+/PkIDQ3F+PHjreouKipymMfsbPzaoUOHYsmSJRg2bBguXbqEpk2b4tChQ5g9ezZ69uyJzp07W5UfOXIk5s6diyeffBLBwcEYNGiQ1X6u9y4f3L2P7bFixQr06NED3bp1w/Dhw1G9enXcuHEDv/32G06ePImNGzcCAJo0aQIA+PjjjxEWFoagoCAkJCS4dZ9x4dy5czh37hyAMiG9e/cu/u///g9A2fjVzsawNg+H9uKLL2LAgAHIycnBzJkzERsbiwsXLvBqz8yZM9G9e3d06dIFkydPhtFoxNy5c1GhQgXcuHHD5fFKe4/Onz/PfgZmzZqFCxcuWL22derUcZqbT7jmr7/+wvr16/H333+zf6xPmTIF3333HVatWuXyjxszeXl5iI6OttoWHR0Ng8GAa9euCZrqpCh82UuN8C1HjhxhBgwYwERHRzNarZaJiopi+vXrxxw+fNimrHn0gxMnTjC9e/dmQkNDmbCwMGbw4MHsCAlm2rdvz3To0MFqW2lpKTNv3jymefPmTFBQEBMaGso0aNCAef7555kLFy5YlV2zZg3TunVrtlxiYiI79A3DlI1eMGDAAKZixYqMSqWy6sWMcqMfmPnjjz/YHvSZmZl2X4+LFy8yI0eOZKpXr84EBAQwVatWZVJTUx2OSGAmJyeHefPNN9nhf7RaLRMWFsa0bduW+fDDD616R5tHP9i9ezczZMgQpmLFikxwcDDTs2dPm9fh3LlzTJcuXZiwsDCmUqVKzOOPP85kZ2fbvcZz584xjz/+OFO5cmUmMDCQiYuLY4YPH84UFxezZf777z9m3LhxTEJCAhMQEMBERkYySUlJzBtvvMEUFhay5bgO6XXu3Dlm8uTJTKtWrZiqVasyWq2WqVSpEtO+fXvm888/tyq7YcMG5sknn2Tq1avHhIaGMgEBAUxcXBwzZMgQ5ty5c1ZlnY1+AMCmF3l5rl+/zowePZqJjY1ltFotEx8fz6Snp1u9FpakpqYyAByOusH13o2Pj2d69erltG2WOLqPzT3r33//fZtj7L33p0+fZoeoCwgIYGJiYpiHH36YWb58uVW5hQsXMgkJCYxGo7EaTsqd+8wV5ueEvYVLXe+++y5Tq1YtRqfTMQ0bNmQ++eQTts7yr8OYMWNsjo+Pj2eGDRtmtW379u1Ms2bN2M/Fu+++a7dOeyjtPTI/fxwtls9ZghsAmC1btrDrX331FQOAqVChgtWi1WqZgQMH2hw/bNgwpk+fPjbb69Wrx8yePdtqm3kYxdzcXKEvQzGoGOZeN3eCEIjExETUqVOHjdAQ98nIyMCIESNw7NgxXh2hCIIgCOmgUqmwZcsWdgSDDRs24KmnnsLZs2dt+qSEhobadFIcPnw4bt26ZTOCwkMPPYTExEQsWrSI3bZlyxYMHDgQd+/epfQDB1D6ASEYf/zxBw4ePIhffvkFTz/9tK+bQxAEQRBeJTExEUajEVevXrU7ayFXUlJSbHK/d+/ejVatWpHQOoGklhCMOXPmYMeOHRg6dCibG0oQhPxxNZWyWq0WfLpjwj3oPfIehYWFVqNPXLx4EVlZWYiMjMQDDzyAp556CkOHDsX8+fORmJiIa9euYc+ePWjatCl69uwJoCz/XK/X48aNG7h9+zaysrIAAC1atAAAjB49Gh999BEmTZqEZ599FkeOHMHKlSutxv8m7ODr/AeCIAhCupjzR50t7ubeEsJC75F32bt3r93X2JzPrdfrmbfeeoupVasWm0Pdt29f5syZM2wd8fHxduuwZN++fUxiYiITGBjI1KpVi1m2bJk3L1OWyCands6cOdi8eTN+//13BAcHIzU1FXPnznU5xt/+/fsxadIknD17FtWqVcMrr7yC0aNHe6nVBEEQ8kav1+PMmTNOy1SrVk3QsY4J96D3iCDKkI3Udu/eHU888QRat24Ng8GAN954A7/88gvOnTuHChUq2D3m4sWLaNKkCZ599lk8//zz7Iwc69evp8GLCYIgCIIgFIRspLY8//33H6KiorB//3489NBDdsu8+uqr2L59u9Xc2KNHj8bp06dx5MgRbzWVIAiCIAiCEBnZdhTLz88HUDZNqCP4zMhRUlJiNS2jyWTCjRs3ULlyZa9OgUkQBEEQBH8YhsHt27dRrVo1n3SSKy4uhl6vF6XuwMBAdpY84j6ylFqGYTBp0iS0a9eOnYXFHnxm5JgzZw47cxVBEARBEPImJyfHZlZHsSkuLkZCfCjyrhpFqT8mJgYXL14ksS2HLKV27NixOHPmDA4dOuSybPnoqjnbwlHUNT09HZMmTWLX8/PzERcXhxafvwhNiM7uMSVG1y9jiV7jsozR4LqMgUM9AMAY3firVO9G2VL3o9VqI78It0rvWWRczaOtzlCVClqdU9TOR+chRMTkxaciI/Bwk6YAz7LJmEB+x5s0PI5zt62BJk7FVBpu5bSB3GRDo3VdTsehLp3G+Yc6OMB5RC9Y6/j4EK3zh1MFbbHDfaEax8eGaYvcri9cY39fmNp+XREOtoep7dcTqi6xuz1CbXsdhYUmtGtzDWFhYXaPERO9Xo+8q0b8ebwmwsOEjRIX3Dahbqsc6PV6ktpyyE5qX3rpJWzfvh0HDhxw+ZdXTEwM8vLyrLZdvXoVWq0WlStXtnuMTqeDTmcrr5oQHbQVdCg22L5klppZorf/kmosNhscyKs60KKMA3lVl6ueMdj/sNjonDNxDS637kwG7Vye2uBCHh18cbuUViefVU7C6sL/7TwDPaqPd70enMsd1OL8CuZTTIGuy7iLpy+9SaRx0bnUy+Wr0xPxdSS9rs5r0rp5TnfayFV2tdblHB1VXnbttURbTnTt6aYu0FCujO33SpCFqJba2W8puvb2m2W2FLYfBEvR1ZfbHxpQ7Hif5v45S8o9vMPvSa7BzkM99J7o6svti9CYj7EVyzB1EfR2tlfU3IURth3Aw9XFYBBip55iGMu9PhUtHsS+TB0MD1MLLrWEY2QjtQzD4KWXXsKWLVuwb98+JCQkuDxGyBk5SoxaGMoJrSOBNeNIXq3KcI28OpBXG0SKuroUVzvwjbR6GmEVRCpFrM/puRQonmIi1uvliSy7e79wlWCu9bqqz9Xny5n0uvpMO5ReB88Ph7LrqI322ubomVdOdh0GAMrJrqNnsqXsOnq2W8quve+H8qJrL0hiKbpFpbY3oqXo3jVYv9mWIlt+n+X+wlLbiIFZdAuNtuc0i26BwToCEm4RyS00WNdpltx8o/UxZskFgNsm633maO4to7W4VtTcLTu/yfoc4feiubfLbQ9TF+OWKQCFJm5/8BDKQTZSO2bMGKxbtw7btm1DWFgYG4GNiIhAcHDZByM9PR1XrlzBmjVrAAg7I0eJXgON1vHLJZTACi6vEhRXT6RVKMEUS1SlKKXelHJvI1ZU1JP30V0hduf94RSt5VCfs3rEkl57wuvsmWNXeCUmu+Wjuva+BzwV3aByaQflRdeZ5ALORddyX3nRtYzmlhddR5IL3BddrpIL3BddoSX3jkmcfFZCushGapctWwYA6NChg9X2VatWYfjw4QCA3NxcZGdns/sSEhKwc+dOTJw4EUuWLEG1atWwePFij8eodSWwSpNXd8XVl9IqeJTWi5KqZPkUC7FeM09k2d17xh0J9lRYudbDV3r5CK8g0V1PZddOCoO9Z7QvRFcO0VzLlAVH0dzykgu4juZ6Krka3LE5J6FsZCO1XIbTzcjIsNnWvn17nDx50uPzGw0aMHYeToIJrI/l1Vvi6omECBalFVFUpSqmTvqCyBKjSBFaM568j+4KsTv3IxcBFkJ8ndUhlPCqAIQFBKCCRgt7KY/udqJj3OmsxjXHN4DDz9da12W0HOrRaCxk2E7xwACL/Xaar9M6Pl5n2ZHMTt3Blg+IcsFNq0ixIcJqX4i2xO4xAFDBfBOVVrLerrXo6FXuPgvRlO0rLdfGsHudz6y3MwhS34RRdceh5JZPSyCUj2ykVgp4VWAlIK98xNXX0iq0sHpbUpUmn2Ih5uvkqTCLlVMLcLu/hRBf5+LK71jL50klXSAG1a2FxlUioVWr7fRsFQ6Gc90cRZdDfUL0S1KpnLfH9TmcH692crzKybHO2uXsOEfnU1seU06O2XOVk9yyYxiYGCNuqS4gJ+A7lKoLbCK5RSYaRsbfIKnliEGvsRp5wBfRV6nIK1/R8zi1QCBhFVtU5SCmUo0oO0Ks3Fl78H3/+MqwUB3A2PoEEF++kVpnx5qP06hUeDmxCWpGVkRweEVAo3HsiS7Ezros96KcRZdzOWEm5nQpqi72uxJhtZP9zvY5q1ftVGTdP06jchzVtmoHAxgNDIKvR6BCSTX8qvvQJl1Bi9sO6yKUCUktRxij2rnIChh9lbO8ehSpFUBahZY1Xwmq3KRTbMR+PYSQZnfvFXclWKh8WsD5Z00s4TUfFx2iQ8UgHUIqRkIdUHYy7tHUe3gou45Ox6kdQoquh5JaVoZ/Hc6k09V+PvvUToRVw0qu7S+ijkQ3IIiBRhuGu9kRKDHEoEh9A4DjnFxfcJspARhhh/S6zdCoDo4gqeWDDwRWLHn1prh6Kq1Cio3YsqoUKdXohYlA2cMYKJ1pp72ZQ2vGnXuQqwALkk/r4nPqTHq5CK9apYIKKisbc+RHDiXT3g6HldjZ5qBee1XYnIprfVzaWL6ucocwduooL6nlu5uUl9zydVgebyq3r7yMOtvPZ5/JjtyZRddY7uI1Fi+O0eI4S8E1MSowUAFQo8gYzA6YbO54VmxUyIOY4AxJLVf0akDjQGY5SKTc5NUX4iqECIolq76WVDHl0lf46pqElmkxc2jNcLmvhRBfTiMn8Izyms+rMtxzN+be4iy30x3ZdWTAdk3V0Qk5Hi6U6LqSXDv1OJPUsv3lDvdAcgHPRdb1PnW5fc4l12gjxvcTccuPsKAt3xONUDwkte7iA4GVmrzyFVepSau3RFWJQipnPH0/PJVioXNozbj6bHCRXrGE12lKgxuCye7iIprOdngY1RVMdPlEc8vV4yqaa2/gIJWT48tLsjgiy01yLdMVnEmuCSrcMehQoAq2mhDijp1hxAhlQ1LLlVIVoLXzVywHgRUj+iq2vPpKXIWSVjGFVUqS6usIslQRs2MZn/efjwgLmUMLeC69HnUes3ieqEtxP0rrDDdl1+OorsCia+8Uxw4dxjP9+uPgH78jPCLCbl3b1n+F9998C4f+PO+4XW6kLVzJzkHPlm3x1b5daNC0yb395Q4vJ7lL352PPTu/w/8dyLy3X2yR5ZauYE9yTVDBZHE9luPk6u2M8UsoG3rH3UBIgRUr+ip1eRVCWsUQOW+LKsmouAj1+golx1zvL3flV0jxdfbZFE14eURprY4xGlHhp6PQ/ncVhqgo3GmbDGisOxpxjupyFN1rV6/i08WLcfCHH/Bvbi5Cw8IQXzsBvfr3R+/HH0dwSIjD87Zo3Qo//HIaYeHh9q/HHnba1TwqFgDw+bc70KxVEluHvqQEnZu2RP7Nm/h06/+hdbtUxFSvhh/OZqFi5UhWdl2lLAwfOxpPPjfCYj+/vFxvSu4dUwAKcf8nAcsJIQj/gaSWI2qjCij3cBZaYKUmr74SV0E7hIksq3KSUylFmN3FV53K+Ly/nogwl/dISPH1VHj5pDSoDRAkShu+8xvETHsTgbm57DZ9bCzyZryDgp69bMpbVcNTdP++fBlD+/RGWHg4xqWno16DBjAYjbj811/Y+uWXqBodg47dutk9d2lpKQIDAlG1apTVNTpMW7Bso522xVSvhq3rNqBZUiv2gn7Y+S1CKoQg/+ZNtg6NWoMq0VHWp3CRchBcoQKCK1RgZVeozmdiSK6JMYGBCncNOiujKTQGQk+z5AIA5syZg82bN+P3339HcHAwUlNTMXfuXNSvX9/hMZs3b8ayZcuQlZWFkpISNG7cGNOnT0c38/2NsgmvRowYYXNsUVERgoJ8k/oh7DgTCkalV9ks9lCXqmwW++VsF0e4VVZvf3GGvfq5fJlrSu0vXHB0TnclQqNnnC58cNY2T9rKBVfX48kiZ+T0moh9/wh5TZ62zdEzgMtzQMXYX5xyL20hfOc3qPn8MwiwEFoACMjLQ83nn0H4zm+sylstbrSnPO+kvwaNRov13+1Ct959ULtefTzQoBG6PPIIlqxdiw5du7Jlm8XE4quM1Rg3bDjaJNTGxx8sxLEfD6NZTCwK8vPZctvXb0C3lkloWysBE4ePwC2zkNq5bstr6D1oIHZt3YbioqIyM2ZU2PrFl+g9cKDVoVeyc9C8SjX8fuZXgClLgWhepRp+OnAQgzt1R9uatTGk+6O4+MdfYBgVm37wePsubB1vvDgB454aiY/nL0b7+s2RWqshls1dAIPBgPlvzURqQhM83KgVNn++gRXenw8dRpNKNXDrVgFMjAomRoVzZ86hUcWauHI5BwCwae1GtK7ZBHu+/QHdkzqgRcwDGDfkedy9cxebvvg/dGiShlY1m2LGlGkwGu8bqrm+8rJbWBpktRBl7N+/H2PGjMHRo0eRmZkJg8GArl274s4dx9MIHzhwAF26dMHOnTtx4sQJdOzYEb1798apU6esyoWHhyM3N9dq8ZXQAhSp9QihI7BiR1/5fIl6EnUVpGOYgOLhzaiq3CXSX+D7PgkVORY6bxZwfU1c2+5phJfvs8NlFNVoRMy0NwGGsQlgqhgGjEqFmOlTUdCtu00qQlll9k7qui23btzAkf37MO611xESUsFuA1VQWdW/dN48jH/9dbwyYwbUajWu5OTcbwMDnDl1Em9NnIhxr6ejU8+e+HHvXix9f57VuR11gGvUtBmqx8Xh+6+/wSOPD0DelSs4efQnvD53Dj5e8AEruo4q+HDWXEyeMQ2VqlTGO1NexbTxk7B653bry2KPVeHngz8iulosVu3YhKyfj2PauMnIOnYCSSltsS5zB77bsh1vT34NKR0eREyN6ux57XVkK8uDLRuOq6ioCGtXfIZ5K5fgbmEhxg15Di89/RzCIsLx8cYM5FzKxrgho5GU3Ao9+z/K1sFGa1Em4sUGLe4yAQixmBKYOoqV8d1331mtr1q1ClFRUThx4gQeeughu8csXLjQan327NnYtm0bduzYgcTERHa7SqVCTEyM4G3mC0ktR9SlKqg1HEY+EEFgpSyvHncMk4m0Sk1SpdYeqSF2uoK7r78n7fFkSlt7OGu7p8LLaYSE8kN6meE42kHIT0etUg5syzII/OcfVPjpKO6kpJXb6eAgDqKbfekSGIZBQp06Vu15sEkjlJQUAwCeGD4CE9+cyu7r2bcf+j7xJHsRrNTe44tPPkVqhw4YNfYlAECt2nVw+thx/Lh3r8X1OLxU9HliELau+xKPDBiAres3oF2nh1EpsrKDa7SW3JdefxWt0lIAACPHjcXYJ4egpKgYOgdRtohKFfHqnHegVqtRq249rFq8FMVFRXh20jgAwKgJL2HloiU4+dNx9KxR3frUrODa1msoLcWb8+cgLqEWAKBrn17YsWETDvxxChVCK6B2/fpo+2AKjh48aiW1JgfSfNdw/yYsNSj7OVlQUGC1rtPpoNPpXB6Xf++XgsjISM7nMplMuH37ts0xhYWFiI+Ph9FoRIsWLTBz5kwr6fU2JLUeIEeB9YW8CiVgSugg5qtz+hu+HrarPGJ1FAOElV5PhdcT2eUaQdVevcqhMkD7r51yLkYNcNkewCbBdN0334IxmfDaSy+itEQPFXM/ONq4eXOL+iyiuPfk8uKFC3i4Rw+r+polJZVJrbmsoygygEf698eiWbPx96XL2P7lBrw66x3r9jtJuajXsBHb0CrR0QCAG9euIbZGDevj7/2/Tv36UKvvZyxWrloVdRvWZ6VSo9GgYqVKuPHfNTDMfYG1J7LmNAeGAYJDglmhBYDIqlVQLa4mgiuEsg2oXLUqbvx3zSrdwDLnloEKRcYA3L13o1lGa33NbZMJEHgCsNumsgpr1qxptX3atGmYPn2602MZhsGkSZPQrl07NGnShPM558+fjzt37mCgRXpLgwYNkJGRgaZNm6KgoACLFi1CWloaTp8+jXr16nG/IAEhqeWIqhRQ2/klqzxiCazU5VUIURN8ilsvyKMSBFWt9/2Ui6ZAaaX3e2vYLnfPK7T0CiG87squeUgvTjPd2ovsRUXZbrSDISrK5U/47qQixNWqBZVKhYsX/gS63S9bMz4eAKwinObzBgeHWEmuzelNzD15VFkf6KyNFnVVjIzEQ507Y/rESdCXlODBhx/GncJC+ycz13WvPm3A/TdfZR4eywiHKQvaAK1VW1QqlVUdDKOCSqWC6d74WiqzADMMK7aleoNtvdoAq2irSqWCVlumJqzEWtRrxryvLLfWuk5ztNZg8P2zTUxycnIQbjGSBpco7dixY3HmzBkcOnSI83nWr1+P6dOnY9u2bYiy+PwlJycjOTmZXU9LS0PLli3x4YcfYvHixZzrFxKSWg+Qs8D6Sl6FElcxZVJKoioF4fQG3rhOscVZzGgs13P4ZGQED2TX7elxAdxtnYzS2Fho8/KgshMGZFQqlMbE4m6b+1+27k3WYK+hQMVKkUh5qD3WZ3yGJ0eOLMurZVwcV+785f+t/cADOHPyhFWjzpw4eb+BziT3nqA+NvgJjHnqaYwcOxYaixxil53vXHScAxzn49qto9xYt5UqVwEAXM37D+EVKwIAfv/17L399yK49s5fLq2A68gKJYYAFBnvD+kVHKD8Ib3Cw8OtpNYVL730ErZv344DBw6ghjki74INGzZg1KhR2LhxIzp37uy0rFqtRuvWrXHhwgXObRIaklo34JxuIJLAykVepSyuvhBWfxFTqcP3fRBahsUYtotr3e7U61EqAYe2OMOphGo0yJ32Dmq+8AwYlcpKbJl7qQF502a6/GmNy2QJ93eW/fPmO+9iSL/eeKJnd7w4aTIeaNgIKrUav2Zl4eJff6JRs2Zuie5TI5/BkD6PYNWSj9Cxew8c2b8PP+7ba3G8yvagcrTr+DD2//orQsPCOI/u4PQ6nUqwkxeJsf63ZkItxFSvhuXvzceY9FeQ/b+LWLNkhf1DnbSJYVT3MykYlY3klomuCiUGjZVYF5UGwlAqnQCFL2EYBi+99BK2bNmCffv2ISEhgdNx69evx8iRI7F+/Xr06tWL03mysrLQtGlTT5vMG5JajqgNAOw8I6UisHKUV6EF01vCKkdJ1ciwzUaJpCS4+34LIcFipCE4q9ebsqsutbAfRwOh2sFSxgq798Lfyz5FzIw3rYb1Ko2JRd60mbjdoxd7CutKuNVvprzD1axVCxu//R6ffLQIC9+djX9zcxEYGIg69R7A8OdfwKBhw+1U4uD/AJonJWHGvPlYMm8els2fj+QHH8Tz4yZgxaIP7DfSskFsJFOFSpUtOoe5IdWchlBzhb2IMgMEaAPw7oqlmPVKOgZ26ILGLZpj7OuvYsrI5+7n1Np5Q9i7w8GtYZ2qYH3eYosZxIK0tqkO/sqYMWOwbt06bNu2DWFhYcjLywMAREREIDi4bAa29PR0XLlyBWvWrAFQJrRDhw7FokWLkJyczB4THByMiHuz4c2YMQPJycmoV68eCgoKsHjxYmRlZWHJkiU+uMoyVAzj7G8koqCgABEREaj38mwEqLgPDyI1gVWCvIo+kYJExE+OAioXpCLKYqdCCNnRzdO6ystudMVgTOrXElGx1aDWuDBhV7JrNCLk56PQXi2bUexum2QwWg6dH6zOwb2oq1/jedXLsRync3NJVOZQj8tzudzvoh3O/rhwcWz5W8KkN+C/v69g1j8/4oY632qf8W4Jfh34PvLz8936mV4IzO6QdS4KYWHCft5v3zahRaOrnK9L5eBztGrVKgwfPhwAMHz4cFy6dAn79u0DAHTo0AH79++3OWbYsGHIyMgAAEycOBGbN29GXl4eIiIikJiYiOnTpyMlJYXXdQkBRWo5otYDcJCDrRSBlcIIB6INiO8DUSQ5lR583xOhZZjL/eiJ+AoxbJeruvgO/8VpRjEz9mIull/QGg3ulhu2y638WThoC4dxa13WzbVejuU4pUtwSFfgEr12eS5Xdbhqh5PjXc12ZnlLlHe1Ev19pdEFUqTWDJfYpVlUzZjl1hkffPABPvjAwa8KPoKk1k2kJLBylVehxdVbwip3SZVKJNoVUhsJAXDvvRdKgF29X3xfJyFSEBzV404dKpOdn5a5XJIr0bV3LnfyZwGP0hbcEmiuQiwhybV/Hhd1uCO5TtINAGvJNXc2YxgVjEbrCH2JXgujndEWCGVDUssRdSmgdvHAdUdipS6wUpNXsYVMSsIqF/kUCzGu35uizOVeEkJ8nb1OfK5XCNn1tA6VnUviJbpuSi4gkIzaqVtpksvtPI6PtznIjSgu4Dyn1mC4L7ZarRGE/0FSyxOpCayvoq9yEFdfCau/y6mUkMrIB2Zc3ZOeSq+Qwiu07GpKOY5Rew9eoit2NNeDlAXBJdeT87orub5OVbAjuOa32lBqfVMYDBqYDG7mVxOyh6SWIxoDoOHwXSBlgZWCvIohet6UVqWLqrpEvJ/rTDr5PW7ceb+FFGAxpdfRNXldds3jNFnC8dDyouuNaK5QKQuCS66Q5/UgF9beIR6nKrh5foPeIlIbKI1Ibb4pAEaTsH8cF5qU/T3kCfL7lpEYYkusXAVWSPnzhrRKWVbFFE1v4q3r8JU8i935yxJnnwm+wium7LoluuXhcKg3orlipSzwllwPUhXKn9dxZzcngsnhXB6nKnCUbHtvpUGvgUlPkVp/g6TWDZQmsP4kr76WVqWIqRzg+1p7Q4bF6vxliaPPkZCy64noqksZ7qMfAIKJrs+juUJLrtTzccVOVShfmUENxnD/TVZppRuoIMSDpJYj6lIAgY73K11ghZJCscTVF9JKoqos3Hk/xRJgoTt/WSKk7AoR1bU3+D/ncWDLHadWq2ByMWyRINFcklz7G70xqoIjwWVUdv/wYQxqMEbpjaRCiAtJLU+8IbG+EFgpy6u3xFWusqoqkUYOmdAwOun9hMjlHhFafMUSXrFl113RLY8r0dUFapFQozIu/n0dJZZDOIkRzfWW5AoxuoJInc4kEcW114ZSNaC/9wYGUpTWXyGp5YhGz0DD8TczfxJYoeVVbHGVorAqVUaFQqjXx9ty7E3xFSof1hJ7n20hRFdd6t5n3JXsRIQGQaVSITw0CP/dKLQoWL4iDufyouSKMYSYN/JxJRHFZRz834xZbvUUqfU3SGoFwFtjzvpSYIWUV6WLK0mqNHH3ffGGBDu7V4UQXqFlV7ioLmM9+oEb8z6Ud6Tw0GD2XyuptXNK64o4nMvichvViXVatk//gZg9b1G5Crh3PHNHct+YPA7b/u8rAIBWq0VMbDV06tETYya9jJCQCq7rlHEU1/41qQCDCihVAQHCjNRDyBOSWjfxVhRWCQIrlrz6QlpJVP0PLu+5mOIrpvB6Q3bdEl2encF0gVoEBJS9B4EBGgQFaFGiN3DLzXVTcvcfPc3+/7uvt+HDhe/j6z2H2G1BuiCr8qWlpQgICCh3DoEkF0C79h3xzrxFKDWU4uSxnzDtlckounsXb81+z+06GRMDo9EIrVYr+Siuq1G+UKqy/3/CL6DYPEfUpdyEVqNnbBYxjmHbpTfZLO6g0ZtsFnex1wZPhVZdYnC4CI2qxOhykRrqklK/W6SIr+4dsT4bQn6OPX2usJHce0tggAZhoUFWS2SlCuy89gzDILJSBYSFBiG8wv1Fp+X4h0e585WnatUodgkNC4dKpUJU5ShEVY5CaVEJkpvXx3dfb8fwJ/ohsX4tfL11E5YsnId+PTtbnIPBmpUr0CWtlVXqwpav1qN3pweR+EA8Hnm4HdZ/vortSGevQx0ABOp0qBIVhdhq1dGrTz/0eqwf9uz6DgCwY/P/YWCvrmjTsA7aJzXFKy+9gOvX/mPrOnb4RzStGYMf9+3FoJ5d0bJOHE7+fBQ5ly7hpRHD0L5FE7R5oDae6NkNRw4esDpvt+RWWLHoA7w+biza1KuNrm2SsGfXd7hx/RpeGjEMberVRt9OHXD2dBZ7vtycHIwdOgRpDeujTZ0E9O3wEA7+8P29Tl4Wi6v3xQ7sa2QkgSUoUusx3ozCevoFIwRCR1+9EXWVopQCkKyoSRVPXi+TLsB1IZFwdv8JHel19HnyJLIrxJBewP1nkLrUdE9E3HsOVokMQ3hYkM12xqKeiLBgRIQFW+0vKCzGP3m3rI8RIZILAAvmvoNXXp+GWXMXIjAwEF9tWOviHAw2rl+LJQvn4Y23Z6Nh4yb47eyvmPbaFAQHh+CxAYPun97Fy6ULCoLBYAAYoFSvx0uTX0WtOnVx4/o1vPf2W3hz8ngsW73Our2zZ2LKG9NQIy4eYRXD8W9uLh58uBNeevk16IJ02LbxK7w0fCh27D+E2Oo12Nfg808+xrjX0vH8+En4/NMVeH3cWLRo1Rp9nxiMyW++hQ9mv4PXx7+ErXsPQKVSYdbr6SgtLUXGpq0IDgnBXxfOI7hCBduLcCeKa+f9UBvubzRpKQ3BHyGpdRN3hdQXAlt2Xg8jpTKTVymIK0mqdHH3vfGWBHtLeO19/qQguuXFlnHy83ze1XwADMLDgsEwDFT3ypb/FwC7P/92Ef79r8DOea3XeUmuHYYOfxZduvWyOUZlctzpbPlHH+DlN6ahS7eeAIAaNeLw1x/nsXHd51ZSa9MWiwjumdMnsXPbFrRNawcA6DfoSbZozbh4vDb9HQx+tAfu3rmDEAuZHDv5FaQ+1J5dr1QxEg0aNmZfj3GvvIY9u3Zib+YuPDl8FHs9Dz7cCQOfGgqogNETJmPDmtVo0rwFuj3yKABg5Atj8XSfXrh+9T9UiY5C7j9X0KVnLzzQsGFZm+LjrV4fj9IUHLwvaoMKkED0ttCkA2MS9o/XOybff99JFZJajqhLuY1+IMcorJACK6a8+kpcSVb9Dy7vudji6+h+F0p2pSi69qK3ZtE1mRj8k5ePO3f1iK4aDoCxElm2PMOAYYC8f/ORX1jE8bzlz8mxweXEqnHT5o7PYbI9143r15D3zz9469VJmJY+hd1uNBgRFhZ2P0XBznXu35OJVo1qw2gwwmAoxcNduuON6bMAAL/9+guWLpyH38/9ivxbt8Dcm1Y19++/UeeB+vfb28y6vXfv3sGyD+Zj/w+Z+O/qvzAYDCgpLkbelStW5cxyCgaoXKUqAKCeeRuAylWrAACuX7uGKlFReGrEM3jn9VdxeP9+JD/4IDr3fAT1GzW6/9oI0NlMVQqo9GXlmECK0vorJLUewkdiSWCd4215VYy06hVwHYG+SxNwF1f3jVjSK6bsii26QkRz8wuKUFSkR624Knb7XTEMcCn7GvSldl4njrLKeZiscgQHh5QJ471j1Gq1zbBfpaVlr7HKBDDGsn0z5sxD0xYtrcppNBbvpx3Zb5OShqnvzEWAVouq0TFsp7S7d+7guSGDkPpgB8z9YAkqVq6M3H+u4PkhT6C01PqeDQ4KsRqBYv6st/Hj/n2Y8uY0xNVKQFBQECaOfgal+lKr10SrvX9vm/+w0GoC7kem721jmLL3v/+TTyGtQwcc+OF7HN6/H59+9CGmvDUNT418xua63J6+1w5muTX/S/gPJLVu4q8SK1YHLW8gC2lVgpAKgRCvg0TE2Nl9J4bwiiW7QoquWm+6P00uz2CaimEAhoFabV9Y1GqV46r5DOvlQapCZKXKuPbfVat0id9/+5UtVqVKVUTHxOLvy5fR+9H+9w/n4P7BwcGIj69lE8W9+NefuHnjBia++gZiq1UHAJw7c9pODbZtPvnzT3hswCB07l6WCnH3zh3883eOTVFz5yyXQ4ZZvHYx1apj4JBhGDhkGBbOmYVNX3yBp0ZYSC2vobzu7WAANY10QICkljMaPQMNx44NfEVSCgIrV3mVnLiSpPoOd157Hwmwo/vVW7IrGdG198jiIHRhoUGsKNr7Nyw0CDdu3nFdEYdxWMtjORqBU7ED0LptCm7cuI6VK5aga/dHcOjAXhzcvxehoaFsmTHjJmP2228iNDQMD3Z4GHq9Hr/+choF+bcwfNRotyeAiK1WHQGBgfhi9UoMemoYLpz/Hcs//IC9XkejKQBAzVoJ+P67nejQuSugUuGj+XNhMjn+XnHVec18zrnTp6Jdh4cRX7s2Cgry8fOPh1C7Xj2bcvcrdn0ue6+7pdiaaLxav4SkVgB8IbFSFFix5VUS4kqyqiy4vJ9eFF9vRXe9Ibq80xbKP9rsSF3YvVEQGIbB3SI9/vuvAFWrhiMkOBAAEB6qw80bhU47ntnAI4oLOJfEOnUfwNQZc/DxssVYtuQDdO3WCyNGjcbGDWvZ8w0Y9BSCgoPx2SfLMO+9dxAcHIIH6jfA0OHPltXv5ixnkZGVMev9hVj0/hx8sWolGjVpiimvT8PYZ4Y6bTujAl6dOgNTX56Ip/v1RsXISIwaPQaFhbfvFXDxOsBa9i0xGo2Y9WY6/s3LRWhoKNI6dMQr0952XBmHPzbYdjuoQl2qonFq/RAVw7g5roqfUVBQgIiICCT3mgltQBBFYe8hpsD6VF5JWgl38WG6g9id1YQcgcEsuVWjKuD5F9siOroa1Gr3268NVKNO7WgwDINr125bRWQjK1VAlSphUKlU+Ot//8JgsH5uuiW5lvA8zJ18XD7n4ZKmcL9u7pVzbjfHcpzq41nGZCjF1StXMOvwKfxzpwgmi1vKVFyM/739OvLz8xEeHs6tsQJhdodvztRGhTCBRz+4bUSvZv/zyXVJHYrUckStN0HNcBdLJUVhxRtA3gcCqSRpLdH7ugXioAv0dQvcw9k9JbLw2vsMiRnR9URyzc8ktd5gt+MTZ0xAYWExrl8rRHG51/7GzTu4W6RH5chQu4e6M4SYdcHyFXE7zO0OZ26exzKK61aagovr5txujukb3HJjOdTFoYzlJEniTshOSBGSWoFQisQqQmDlJK5KFVNP8OQ1kZoQ+0B4xRRdISUX5SZg4CqYBoMJV/6+aX+nGiguLsWVfxzsLwef85cVtqyE2yG8RlRw4zx+K7hO/j5SyeirgBAGklqe+FJiSWDvIVV5JVH1HVxfeynIr6P7VwTZFUt0hZRc3lFUSzjk4wp+fi+PqMDlHG7l4ZaPmju5bqkKrgqAylAWpTVJY/ATwkeQ1HJEU2qCxo30A0uUKrF+Ka8krMqAy/voK/G1d797QXQVL7luDpEr6Sium+cQI4orVcG1TD9gxJ+F3SW3TUEwCjyj2F2aUcwhJLUiIAWJFSMKK7rESkFeSVoJM87uBW8LrxdEV4xorj3Jve9M7uXV8pZMMwJFcUlw7dTpS8E1lTWTurwTAEmtIChRYkUVWF/KK0lrGcUlwtYXpBO2PqkjBeH1gegKIbmF/91Fqd4Ik7EUGh6jHwC+jeIKkqZAgiuY4DIGPUqNRhTc1kN976vUJIHsIsI3kNTyQGkSq0iBVYq8Ci2fYiFWO+Uoy/buPV+JrgQlt6TYgBOHLiO4SwAqVQLUai1Yq+E75JYlfKqwfKR70ARecs3jEFGGCrP4SnFrqDCOFyD4MGEoE9pbN67jwF//oNhw/wLU9z6C9CO9/0FSyxG13gS1k5lVnB7rLxLrC4GVk7zKRVClhLuvmVQl2NF9KrbsihzN5Su5+3b+AQBIaleKgACNRyLpEk9E2SPB9dJJ3R3bVuSxc4Ws01lGAQPAYDTiwJ//YMd526l8gftyS/gPspLaAwcO4P3338eJEyeQm5uLLVu24LHHHnNYft++fejYsaPN9t9++w0NGjQQrZ0ksSIgdXklYZUGrt4HqUmvL6K6IkZzLZ81zgSXYYC93/yBH7//C2EVg6B2IZ5MoJu9vBxgCuT/lWcK4G93pgB+7Xf3OHfbaHTzrTe58fJxbQvX0QrKn9vEAPlFehQbjGWjH7hZH6FMZCW1d+7cQfPmzTFixAj079+f83Hnz5+3mnWjatWqgrZLShIre4GVqryStCoDOUivt0VXJMnlEsXVlxhx/d87NttdIcRMZ7yn8TUfz1O0jTyPc/d8xkA3BdfN8u7II9e6udbpTMYtRz+g9AP/Q1ZS26NHD/To0cPt46KiolCxYkVOZUtKSlBScv+Lr6CgwKYMSaxASElgSVoJwPF94GvZLf9Z8Zbk+iCKywXL5ydfwbV8jvMRXMu+Fe4Ip+UY5+4Irvl8XM+l0d//8Z6LVLpb3mrmLhdvJ9e6udapsRRXjoJL+Aeyklq+JCYmori4GI0aNcKbb75pNyXBzJw5czBjxgyb7eoSA9RG/jIraYn1J4EleSX4IDXZ9ZbkeiGKqyTBdTeaykdw+ci0twSXS6TVXLereoUWXMI/ULTUxsbG4uOPP0ZSUhJKSkrw+eefo1OnTti3bx8eeughu8ekp6dj0qRJ7HpBQQFq1qzp9rlJYn0ssSSvLEyJ+K+FSieBn+19gVRk1xeS68U0Ba74WnD5Rm8BZQiuGNFbd+olwSUULbX169dH/fr12fWUlBTk5ORg3rx5DqVWp9NBx/MLWrIiK7bE+lJgFSqv3hBRIRGqvYqRY3v3pTdF1xuSK/EoLgkul/MoXHAlMKNYvikYeqOwqlVkksCFSRRFS609kpOTsXbtWkHqIon1MjIXWLmJqi/g+hrJUn7L379KllwSXOtjBRBcPvm37pyPa1oAn/JiCy6NeECY8TupPXXqFGJjY3kdSxLrRWQmsCSs3sXV6y0L6ZWK5JLg8miL4V47qIOZnDqYEcpHVlJbWFiIP//8k12/ePEisrKyEBkZibi4OKSnp+PKlStYs2YNAGDhwoWoVasWGjduDL1ej7Vr12LTpk3YtGmT2+dW6U0Az1FkZCWxJLAOIXGVD87eK8kKr68kV8aC6+tOZtTBzLPyYncwoyG9/A9ZSe3x48etRi4wd+gaNmwYMjIykJubi+zsbHa/Xq/HlClTcOXKFQQHB6Nx48b45ptv0LNnT9HbKhuR9bbESlxgSVyVj6P3WHKy6wvJFTtNQWDBFWMUBUpPcHQe+aYnEP6DimEYZzPR+T0FBQWIiIjAw01fhVbj+EtFNhILeFdkJSqxJK8EFyQnumZ8MZSYmGPjCjgeLuC54JrxdJIHOU3wwOdcUp/gwagvxplVryM/P99qAiZvYHaHFSeTEBwqcEexQgOeb3nCJ9cldYSZe9BPUZeUsovH6EutFyEp0VsvYlJcYr1IAKakxGYhCC5I9r7xxedMzGeIwM8+oZ7NqhIju/Brh4FdeB2vN1lFVbmi0ZvYxd1zuXM+jZ5hFzHKq0u5R1vdqdffmDNnDlq3bo2wsDBERUXhsccew/nz510et3//fiQlJSEoKAi1a9fG8uXLbcps2rQJjRo1gk6nQ6NGjbBlyxYxLoEzJLVuIprICokfSywJLCEmkr2/fCW4YiDwM1Go57UnclvWDv6Cy0c4zbgrt3zP565U8pFbLoLrrjj7A/v378eYMWNw9OhRZGZmwmAwoGvXrrhzx/EU1RcvXkTPnj3x4IMP4tSpU3j99dcxbtw4qz5JR44cwaBBgzBkyBCcPn0aQ4YMwcCBA/HTTz9547LsQukHLjD/hND5gUlO0w84I/fcWAmIqxnJCAVBWCCplAVvpilQegKPdvD/WZpvagLgP+kJRn0xTn3xhmLTD3Jycqyui+s4+//99x+ioqKwf/9+h2P2v/rqq9i+fTt+++03dtvo0aNx+vRpHDlyBAAwaNAgFBQU4Ntvv2XLdO/eHZUqVcL69ev5Xp5HyKqjmCwhiRUEEtj7mIqKfN0Ep6iDg33dBJ9iea/6XHAtP79iC66YoyiYn6MSHT1BTkODAdLvXMb1GDmMU3vbFAyDSZzJF8rPdjpt2jRMnz7d5fH5+fkAgMjISIdljhw5gq5du1pt69atG1auXInS0lIEBATgyJEjmDhxok2ZhQsXcrgKcSCpFQM5iyxJrNeRuqS6izvXo3QBLn8f+1RyfSG4fjJ6gi9HTgC8OzQYn/O5K6uWxwg9eoKSsBepdQXDMJg0aRLatWuHJk2aOCyXl5eH6Ohoq23R0dEwGAy4du0aYmNjHZbJy8tz80qEg6RWKMQQWT+KxipVYpUmrELj7PVRovBKJopr/sxT9JZFiOitXCd2ACh6K0fCw8PdTqsYO3Yszpw5g0OHDrksq1JZv97mbFXL7fbKlN/mTUhq+SLnaCzgc5FVksSSuIqDo9dVKbIrCcFVUvRWQnILyD96627erRSjtwyNU2vFSy+9hO3bt+PAgQOoUaOG07IxMTE2EderV69Cq9WicuXKTsuUj956Exr9wB28MVqBWPh4lAJJ9hh3E1NRkd2F8C5KfA8k8dnw1rNBZsOCeYqnoyaUtUUew4LxPR/fkRNohANuMAyDsWPHYvPmzdizZw8SEhJcHpOSkoLMzEyrbbt370arVq0QEBDgtExqaqpwjXcTitRypbQUUAsYyVB4fqxcxdWMEkTJn7D3fsk1omv+7Pg8eivXyC0gaPRW6LxbgFITHJ9H3OitvzJmzBisW7cO27ZtQ1hYGBtdjYiIQPC952R6ejquXLmCNWvWACgb6eCjjz7CpEmT8Oyzz+LIkSNYuXKl1agG48ePx0MPPYS5c+eiT58+2LZtG77//ntOqQ1iQVLrTUhkJQkJrDIp/77KTXJ9np7grdQEP8u7BXw7agIg/dSEsnOJm3vrTyxbtgwA0KFDB6vtq1atwvDhwwEAubm5yM7OZvclJCRg586dmDhxIpYsWYJq1aph8eLF6N+/P1smNTUVX375Jd58801MnToVderUwYYNG9C2bVvRr8kRJLVio2CRlaPEksD6L5bvvVwFl6K3PFG43Ja1RT6jJrhzTo+it5xbpmy4TEeQkZFhs619+/Y4efKk0+MGDBiAAQMG8G2a4JDUioE3ZvHyEXITWZJYwh5yFVySWw9RqNyWtcXz1AQ+EyxIOXqrLqWcW3+DpFYoKCIrCUhiCXeRo+D6ldxKPOcWUJ7cAspKTSD8B5JaTyCRlQQksoRQmO8lkluOFJfIN2oLkNw6O16hqQnepsAYBL1R2EFzi400VpkjSGrdhUTW55DEEmJDcusGck9JAEhunR2v0NQEQpmQ1HKlRA+oRfywkMg6hUSW8AVylFu/SEkAxJNbgcQWILk14wu5BQAD+a3fQVLrS0hknUIiS0gFOcmtX6QkALLJtwXK5NZTsQVIbt1PhaCOYv4GSa23oZELnEIiS0gZucktRW09QKIpCQDJLd/zEsqHpNZbUFTWISSyhNwwFRXJRmwBP4naAn6VkgD4r9x6el5CuZDUiglFZR1CIkvIHYracsRbUVtAdikJAMktwH/EBE/PSygPugvEoLjEJ0LLlJSwi1QxFRWR0BKKQi73s8+fC956Joo5Qo2+9L7gCoRZboXALLeeoC4xWM1U5vbxepNVmoC7aPQmK8n11nkJZUCRWqGgqKxT5PLFTxB8kFM6gs8itoAy0hEAxacklLXJd5FbwPudyghlQFLrKZQr6xASWcKfILHliLfEFhAvHQHwi5SEsjbJV27VpRS59TdIavlAUVmnkMwShLQhsRUQgaO2gHBDgAEkt77mjiEIBoOw90eJwbP3UsnI6+7wNT7KlQUg+VxZgPJlCUJO97/PnyfefJaW6MXPtRUYdUmp5PJtAXiUbwv4LueW8A9IarlS7IXpce1AMksQhGLxdpBAZp3IAOE7kkmhMxlAckuIA0mtBJHDKAYAySxByB1JPGOUJLaAbKK2JLeEEiGplRByEFkzJLMEQQgGiS0nhBRbQNiUBJJbQgqQ1EoAucksCS1BKAfJPHuUKLZ+FLUFSG4J30NS60NIZgmCIHyI2GIL+F3UFvC8MxkgkNzSkF5+B0mtD5CTzAKUakAQXJHDOLWSxhejy5DYskgtagvQTGGEe5DUehk5ySxAQksQXCGhJZwiotiS3BJEGSS1XkKO0VkSWoLgBgmtgCg1WguIJraA8FFbQHopCQDJLeEcmlFMZOQksmZIZgmCOyS0CkHsmcfMiDADmRkhZyIzI9SMZIDns5JZ1aU38Z6dzJvcNgSjROAZxfQGUjdHSP+OkClyi8yaIaElCO6Q0CoMhURs5RC1pZQEQgxIakVAjjILkNASBFfUwcGKEVqVTufrJvgnIootIP10BIBSEgjhIakVELlGZwmC4I5SZBaQsND6Iq/WjLeitYBsxVaKUVuA5JagnFpBUILIUpSWIJyjJJklJISIObaAOHm2QJncCpFna0bofNuy/3hcFSEz6C33EBJaglA2Sko1sESyUVoACPJx27wZrfUCYkRsAeGjtoBwKQkAKGrrh1CklidKkFmCIOyjRIm1RNJC64+IHK0FxIvYAtKO2hL+BUVqeUBCSxDKRKlRWUtIaCWKyPm1gHgRW0D4TmSAsPm2hH9AUusmJLQEoSzMIqt0mQVkIrS+Tj0w44sUBAWIrVhySxBcoNg+Rxh9CRgV4+tmEAQhAP4gsJbIQmYJryFmKgIgfDoCQCkJBDfo7iCgDg6mzmKEovE3ibVEVkIrlSitL/FCfi0gT7EFyuRWTmJbaAxAoFHYmer0wgfDFYN87gyCIAiO+LPEmpGVzBI+wRtiCwgzxa4lFLUlHEF3BAGAorWEvCGJvY9sZZaitPfxUrQWEF9sAYraEt5DVh3FDhw4gN69e6NatWpQqVTYunWry2P279+PpKQkBAUFoXbt2li+fLn4DZUp/tJZhpA3lh276J69j0qnI6EVEp2wPxlLGTE7j5kRowMZQCMkENbISmrv3LmD5s2b46OPPuJU/uLFi+jZsycefPBBnDp1Cq+//jrGjRuHTZs2idxSeUOSQEgFEljXyFpmAWkKrRTwwkgIlshZbAEaIYEoQ1Zx+x49eqBHjx6cyy9fvhxxcXFYuHAhAKBhw4Y4fvw45s2bh/79+4vUSmVglgdKSSC8Acmqe8haYi0hoZUU3kpFAITPswUo15aQWaTWXY4cOYKuXbtabevWrRuOHz+O0lL7f5WWlJSgoKDAavFnKEJGCIm9yCvdW9yRfVTWTJBO2kLrR6kHvkLsqC1Fbq1xN31z+PDhUKlUNkvjxo3ZMhkZGXbLFBcXi3w1jlG01Obl5SE6OtpqW3R0NAwGA65du2b3mDlz5iAiIoJdatas6Y2mygKSEIILjsSV7ht+mEVWETILSFtmCa+kIZgRU2wBSkmwxN30zUWLFiE3N5ddcnJyEBkZiccff9yqXHh4uFW53NxcBAUFiXEJnFB8jF6lUlmtMwxjd7uZ9PR0TJo0iV0vKCggsbVDeUGhNAX/gMTUOyhGYC2Ri8xKKUrrxVEQLPFGGoIZsUZGIKxxN33THNgzs3XrVty8eRMjRoywKqdSqRATEyNYOz1F0VIbExODvLw8q21Xr16FVqtF5cqV7R6j0+mgU+IXisjYkx0SXXlBwuo7FCmxlshFaAkWElvpUz49Ukx/WblyJTp37oz4+Hir7YWFhYiPj4fRaESLFi0wc+ZMJCYmitIGLihaalNSUrBjxw6rbbt370arVq0QEOD9v379DWeSRMIrPiSp0kXxEmtGbjIrpSitn6FUsb1jCIK+VNj7qtRQljla/lfkadOmYfr06YKeCwByc3Px7bffYt26dVbbGzRogIyMDDRt2hQFBQVYtGgR0tLScPr0adSrV0/wdnBBVlJbWFiIP//8k12/ePEisrKyEBkZibi4OKSnp+PKlStYs2YNAGD06NH46KOPMGnSJDz77LM4cuQIVq5cifXr1/vqEoh7cBUuf5ZfklLl4DcSa0ZuMguQ0NrBm9FaQLliKxY5OTkIDw9n18WK0mZkZKBixYp47LHHrLYnJycjOTmZXU9LS0PLli3x4YcfYvHixaK0xRWyktrjx4+jY8eO7Lo593XYsGHIyMhAbm4usrOz2f0JCQnYuXMnJk6ciCVLlqBatWpYvHgxDeclI0jsCDnidxILyFNkzZDQSgYSW+6Eh4dbSa0YMAyDzz77DEOGDEFgoPPPiVqtRuvWrXHhwgVR2+QMWUlthw4d2I5e9sjIyLDZ1r59e5w8eVLEVhEE4c/4pcBaImeZBaQttD7oJFYeb0drAXHHsiXcY//+/fjzzz8xatQol2UZhkFWVhaaNm3qhZbZR1ZSSxAE4Uv8XmDNyF1kzUhZaAmK2gqIu+mbZlauXIm2bduiSZMmNnXOmDEDycnJqFevHgoKCrB48WJkZWVhyZIlol+PI0hqCYIg7EACaweSWb/EF9FaMyS2wuBu+iYA5OfnY9OmTVi0aJHdOm/duoXnnnsOeXl5iIiIQGJiIg4cOIA2bdqIdyEuIKklCMKvIXl1gVJE1oxchFYCqQdSgcTWc/ikb0ZERODu3bsOj/nggw/wwQcfCNE8wSCpJQhC8ZC4uoHSJNaMXGRWovgyWguQ2BLcIKklCEIxkLzyRKkiC8hTZilKaxcSW8IVJLUEQcgGklaBULLEmpGjzBIukZvY3jUEIMAg7B8ppQbHaQT+DkktQRCSgaRVJPxBYs3IXWYlHKX1dQqCGbmJLeE9SGoJgvAKJKxewp8E1hK5yywgaaGVGiS2hD1IagmC8AiSVR/irwJrRgkia4aE1m1IbInykNQSBGEDiaoE8XeBtURJMguQ0HoAiS1hCUktQfgBJKkygwTWFqWJrBkSWo8hsSXMkNRyRBWog0p9/6HKlJT4sDWEv0JyqjBIXp2jVJEFSGYJQgRIanliTy5IdAkukJj6GSSu3FGyxFpCQis4FK2VF+fPn8f69etx8OBBXLp0CXfv3kXVqlWRmJiIbt26oX///tDx+K4kqRUQZ7JCwit/SEYJh5C48sNfJNYMyayokNhKn1OnTuGVV17BwYMHkZqaijZt2uCxxx5DcHAwbty4gV9//RVvvPEGXnrpJbzyyiuYMGGCW3JLUusl3BEiEmD+kHgSokHi6jn+JrFmSGa9BomttHnsscfw8ssvY8OGDYiMjHRY7siRI/jggw8wf/58vP7665zrJ6mVICRmBOEDSFqFx18l1ozCZFYKEy9wQUpiW2TQolTgGcUMBpOg9XmTCxcuIDDQ9XMhJSUFKSkp0Ov1btVPUksQhH9A0iou/i6wlihMZuWIqsQISMNrCQu4CK0n5dVulSYIgpAaQTpuCyEcukDbhSiTWYUKrVyitJao9PKNaCqZnj17Ij8/n12fNWsWbt26xa5fv34djRo14lU3SS1BENKFZNX3kMA6xyyyCpVZghCaXbt2ocSi79DcuXNx48YNdt1gMOD8+fO86qb0A4IgvAuJqDQhWeWOnwmsHKO0hHRhGMbpuieQ1BIE4TkkqvKB5JUffiayZkhoCTlBUksQhH1IVOULiasw+KnIEoSYqFQqqFQqm21CQFJLEP4CSaqyIHEVHpJYKyhKS4gBwzAYPnw4O6lCcXExRo8ejQoVKgCAVb6tu5DUEoQcIUFVPiSt4kMSaxeSWUJMhg0bZrX+9NNP25QZOnQor7pJagnC15Cg+ickrd6HJNYlJLSE2KxatUq0uklqCUJISFAJgIRVCpDAug0JrfAUlQZCWyrs88BQKtxoAVLi8uXLuHPnDho0aAC1mt+IsyS1BGEPklPCHiSr0oQE1iNIZglvsnr1aty8eRMTJkxgtz333HNYuXIlAKB+/frYtWsXatas6XbdNPkCoWy4zjZFA/r7N/YmGKBJB6SJ5WQHNOmBR5h0ASS0hNdZvnw5IiIi2PXvvvsOq1atwpo1a3Ds2DFUrFgRM2bM4FU3RWoJeUCiSfCBRFSekKiKCoks4Uv++OMPtGrVil3ftm0bHn30UTz11FMAgNmzZ2PEiBG86iapJbwLySnhKSSqyoHk1auQzBJSoKioCOHh4ez64cOHMXLkSHa9du3ayMvL41U3SS3BD5JTQkhIVJULiavPIZklpER8fDxOnDiB+Ph4XLt2DWfPnkW7du3Y/Xl5eVbpCe5AUkuQoBLiQKLqH5C0ShISWUKqDB06FGPGjMHZs2exZ88eNGjQAElJSez+w4cPo0mTJrzqJqlVGiSohFiQpPofJKyygkSWkAOvvvoq7t69i82bNyMmJgYbN2602v/jjz9i8ODBvOomqZUyJKiEmJCk+jckrIqARJaQG2q1GjNnzsTMmTPt7i8vue5AUutNSFIJMSFJJQCSVYVDEksokeLiYmzYsAF37txB165dUbduXV71kNR6AkkqISYkqYQlJKt+Bwms/CkxamEwCKtaRqNR0Pq8zcsvvwy9Xo9FixYBAPR6PVJSUnD27FmEhITglVdeQWZmJlJSUtyumyZf4EpQIA3QT3gO10H+SWj9A3sTCThaCEVjngjBciEIJfLtt9+iU6dO7PoXX3yBy5cv48KFC7h58yYef/xxvPPOO7zqpkgtQXgKCShhCQko4QISVsKfyc7ORqNGjdj13bt3Y8CAAYiPjwcAjB8/Hj179uRVN0ktQdiDRJUwQ5JK8IDElSDso1arwTAMu3706FFMnTqVXa9YsSJu3rzJr26PW0cQcoF++icA937yJ6ElnGAvZYBSBwjCOQ0aNMCOHTsAAGfPnkV2djY6duzI7r98+TKio6N51U2RWkL+kIT6NySehEiQnBKE8Lz88ssYPHgwvvnmG5w9exY9e/ZEQkICu3/nzp1o06YNr7opUktIF4qq+icUSSW8gLMoK0VbCaVx4MAB9O7dG9WqVYNKpcLWrVudlt+3bx9UKpXN8vvvv1uV27RpExo1agSdTodGjRphy5YtLtvSv39/7Ny5E82aNcPEiROxYcMGq/0hISF48cUX3b5GgCK1hC8gEfUvSDwJL0IyShC23LlzB82bN8eIESPQv39/zsedP38e4eHh7HrVqlXZ/x85cgSDBg3CzJkz0bdvX2zZsgUDBw7EoUOH0LZtW6f1du7cGZ07d7a7b9q0aZzbVx6SWkJYSFiVD0kq4UVIUgnCc3r06IEePXq4fVxUVBQqVqxod9/ChQvRpUsXpKenAwDS09Oxf/9+LFy4EOvXr7d7THZ2NuLi4jif/8qVK6hevTrn8pR+QLgHpQIoE/q5n/ASXH72pxQAgnBNQUGB1VJSUiL4ORITExEbG4tOnTph7969VvuOHDmCrl27Wm3r1q0bDh8+7LC+1q1b49lnn8XPP//ssEx+fj4++eQTNGnSBJs3b3arvRSpJe5DUqosSEAJESHhJAjXlOg10GgFnlFMbwAA1KxZ02r7tGnTMH36dEHOERsbi48//hhJSUkoKSnB559/jk6dOmHfvn146KGHAAB5eXk2oxRER0cjLy/PYb2//fYbZs+eje7duyMgIACtWrVCtWrVEBQUhJs3b+LcuXM4e/YsWrVqhffff9/t6DJJrb9B4ipvSFQJgSE5JQh5kpOTY5XvqtMJN9Np/fr1Ub9+fXY9JSUFOTk5mDdvHiu1AKBSqayOYxjGZpslkZGRmDdvHt555x3s3LkTBw8exKVLl1BUVIQqVargqaeeQrdu3dCkSRNe7Zad1C5duhTvv/8+cnNz0bhxYyxcuBAPPvig3bL79u2zGvvMzG+//YYGDRqI3VTfQNIqT0hWCQ8hOSUI/yI8PNxKasUmOTkZa9euZddjYmJsorJXr17lNMZsUFAQ+vXrh379+gnaRllJ7YYNGzBhwgQsXboUaWlpWLFiBXr06IFz5845TTx21ntPlpC4ygeSVcINSEwJgpAqp06dQmxsLLuekpKCzMxMTJw4kd22e/dupKam+qJ5AGQmtQsWLMCoUaPwzDPPACjrebdr1y4sW7YMc+bMcXics957koXEVdqQrBIOIDElCEJqFBYW4s8//2TXL168iKysLERGRiIuLg7p6em4cuUK1qxZA6DMr2rVqoXGjRtDr9dj7dq12LRpEzZt2sTWMX78eDz00EOYO3cu+vTpg23btuH777/HoUOHvH59ZmQjtXq9HidOnMBrr71mtb1r165Oe9oBZb33iouL0ahRI7z55pt2UxLMlJSUWPUgLCgo8KzhriB5lR4krARITgmCUA7Hjx+3cp9JkyYBAIYNG4aMjAzk5uYiOzub3a/X6zFlyhRcuXIFwcHBaNy4Mb755hv07NmTLZOamoovv/wSb775JqZOnYo6depgw4YNLseoFRPZSO21a9dgNBrd6mnHpfdeeebMmYMZM2YI3n6SV4lAwup3kJwSBOHvdOjQAQzDONyfkZFhtf7KK6/glVdecVnvgAEDMGDAAE+bJxiykVoz7vS049p7z5L09HT2LxigLFJbftgMp5C8+haSVkVDgkoQBEE4QjaTL1SpUgUajYZ3TzszycnJuHDhgsP9Op2O7VHosmchTT7gfWhiAEVBA/ETBEH4J6tXr8Y333zDrr/yyiuoWLEiUlNTcfnyZV51ykZqAwMDkZSUhMzMTKvtmZmZbvW0K997jzMksN6DpFW2kKQSBEEQXJg9ezaCg4MBlM1O9tFHH+G9995DlSpVrEZUcAdZpR9MmjQJQ4YMQatWrZCSkoKPP/4Y2dnZGD16NADw6r1H+ACSU1lB8kkQBMEPo0EDxqARtE6TwPX5ipycHNStWxcAsHXrVgwYMADPPfcc0tLS0KFDB151ykpqBw0ahOvXr+Ptt99Gbm4umjRpgp07dyI+Ph4AePXeI0SE5FWykKgSBEEQviQ0NBTXr19HXFwcdu/ezUZng4KCUFRUxKtOWUktALz44ot48cUX7e7j23uP8AASV0lBskoQBEHIgS5duuCZZ55BYmIi/vjjD/Tq1QsAcPbsWdSqVYtXnbLJqSV8DOW4+hTKUSUIgiCUxJIlS5Camor//vsPmzZtQuXKlQEAJ06cwODBg3nVKbtILSEyJKpeh2SUIGxhdMrIG/QmqhKjr5tAEJwwGAxYtGgRXnnlFZthUz2ZK4Ck1l8hefUaJK2ElCF5VA7eeC9JnAkh0Gq1eP/99zFs2DBh6xW0NkKakMCKCkkrIRYknITU4HNPkggT9ujcuTP27duH4cOHC1YnSa2SIHkVBZJWggskoARhH1efDZJe/6RHjx5IT0/Hr7/+iqSkJFSoUMFq/6OPPup2nSS1coUEVlBIXP0bElKC8B2OPn8ku8rmhRdeAAAsWLDAZp9KpYLR6P77T1IrF0hiPYbEVfmQnBKEcij/eZaj5Br0Gqi1Ak++oFfGc85kMgleJ0mtFCGB5Q2JqzIgOSUIojxKkFxCXGicWilAY7+6DY3TKi8YncathSAIwhX0vJA/+/fvR+/evVG3bl3Uq1cPjz76KA4ePMi7PpJab0MTGLgFyat0IUklCEIK0LNGnqxduxadO3dGSEgIxo0bh7FjxyI4OBidOnXCunXreNVJ6QdiQ9LKCRJV6UBfDARByBVGp6G0BJkwa9YsvPfee5g4cSK7bfz48ViwYAFmzpyJJ5980u06KVIrNBSFdQlFXn0HRVUJglA69CyTB//73//Qu3dvm+2PPvooLl68yKtOitR6ComrQ0hWvQs9xAmCIO7DBFLcTsrUrFkTP/zwA+rWrWu1/YcffrCZOpcrJLV8IJG1gQRWfEhaCYIgCKUwefJkjBs3DllZWUhNTYVKpcKhQ4eQkZGBRYsW8aqTpJYrAQGAhsQNIIEVC5JWgiAIwl944YUXEBMTg/nz5+Orr74CADRs2BAbNmxAnz59eNVJUku4hCRWOEhcCYIgCH/HYDBg1qxZGDlyJA4dOiRYvZRwQlhBnbg8hzphEQRBEADAGNVgDAIvRvmrm1arxfvvv89rKlxnyP+VITyCBJY/JK4EQRAEwY/OnTtj3759gtZJ6Qd+Bomre5CkEgRBEITw9OjRA+np6fj111+RlJSEChUqWO1/9NFH3a6TpFbhkMRyhwSW4INJR49RLqhLDL5uAkEQEuKFF14AACxYsMBmn0ql4pWaQE9jhUES6xqSV/+GJNQ3CPG6kxgThHIwmUyC10lPd5lDEuscElhlQmLqn7jzvpMAE4R0MRgMCAoKQlZWFpo0aSJYvfTNIENIZO1DAitPSFAJMeByX5H4EoRv0Gq1iI+Pp9EP/BEaocAWGnVA+ph0Wk4LQfgKui8Jwne8+eabSE9Px40bNwSrkz65EoXk9T4krNKCvvAJf8DRfU7RXYIQhsWLF+PPP/9EtWrVEB8fbzP6wcmTJ92uk76dJASJbBkksb6FpJUgHGPv80GiSxDu89hjjwleJ317+RgSWZJYb0PSShDCUv4zRZJLsOjVgEbgTE+9MjJHp02bJnidynhlZIY/58dSLqz4UA4rQfgW+swRhGN+/vlnqw5iDMNY7S8pKcFXX33Fq25Bpfbff//F22+/LWSVioFElgRWaEhaCUIe0GeUIO6TkpKC69evs+sRERH43//+x67funULgwcP5lW3oFKbl5eHGTNmCFmlrPFXkaVIrHBQxJUglAd9jgl/pnxktvy6o21ccOsTdebMGaf7z58/z6sRSsLfBBagnFihoC84gvA/LD/3lItLEGWoVCpex7n1LdqiRQuoVCq7Bm3ezrchcoZElnAHkleCIOxhfjaQ3BIEP9xKP6hcuTI++eQTXLx40Wb53//+h6+//lqsdkoSf0otoJQCflDaAEEQ7kLPCkJoDhw4gN69e6NatWpQqVTYunWr0/KbN29Gly5dULVqVYSHhyMlJQW7du2yKpORkQGVSmWzFBcXu2zPuXPncObMGZw5cwYMw+D3339n18+ePcv7Ot361CQlJeGff/5BfHy83f23bt3inQchF/xFYgGKxroLfQkRBCEkFLklhOLOnTto3rw5RowYgf79+7ssf+DAAXTp0gWzZ89GxYoVsWrVKvTu3Rs//fQTEhMT2XLh4eE2qadBQUEu6+/UqZOVLz7yyCMAPP/V361v4eeffx537txxuD8uLg6rVq3i1RCpY9IFwKRRvtCSyHKDBJYgCG9Bckt4So8ePdCjRw/O5RcuXGi1Pnv2bGzbtg07duywklqVSoWYmBi32nLx4kW3yruDW9/Mffv2dbq/UqVKGDZsmEcNIrwPiaxrSGIJgvA1JLeEJQUFBVbrOp0OOp1OlHOZTCbcvn0bkZGRVtsLCwsRHx8Po9GIFi1aYObMmVbSaw9Hv/YLgaiTL4SHh1uNPUZIB8qPdQ7lwRIEIVXomSQjSlXiLABq1qyJiIgIdpkzZ45olzF//nzcuXMHAwcOZLc1aNAAGRkZ2L59O9avX4+goCCkpaXhwoULorXDFaJ+MpSeXys3SGAdQ18SBEHICYraEjk5OQgPD2fXxYrSrl+/HtOnT8e2bdsQFRXFbk9OTkZycjK7npaWhpYtW+LDDz/E4sWLRWmLK+ib3A8gmbWFJJYgCCVg0mlJbP2U8PBwK6kVgw0bNmDUqFHYuHEjOnfu7LSsWq1G69atfRqpFTX9gPAdlF5gDaUTEAShVOiZRojB+vXrMXz4cKxbtw69evVyWZ5hGGRlZSE2NtYLrbMPfRIUBAmsNfSgJ9zBFOjbv/HVepNPz0/IG4rYEs4oLCzEn3/+ya5fvHgRWVlZiIyMRFxcHNLT03HlyhWsWbMGQJnQDh06FIsWLUJycjLy8vIAAMHBwYiIiAAAzJgxA8nJyahXrx4KCgqwePFiZGVlYcmSJd6/wHuI+q3vj7OL+QKS2TJIYv0TX8uoUHh6HSTFBIkt4Yjjx4+jY8eO7PqkSZMAAMOGDUNGRgZyc3ORnZ3N7l+xYgUMBgPGjBmDMWPGsNvN5YGyuQmee+455OXlISIiAomJiThw4ADatGnjsB2JiYmc3fDkyZPuXCIA6igma0hmSWSVilJE1Ztwec1IfJUPiS1hjw4dOjh1MrOomtm3b5/LOj/44AN88MEHbrXjscceY/9fXFyMpUuXolGjRkhJSQEAHD16FGfPnsWLL77oVr1mRDWCb7/9FtWrVxfzFH4HiSyJrNwhYfUdzl57El6CIMRm2rRp7P+feeYZjBs3DjNnzrQpk5OTw6t+3nbw999/Y/v27cjOzoZer7fat2DBAgBAu3bt+FZPlMPfZZZEVj6QtMoTe+8bia48oWgtIQc2btyI48eP22x/+umn0apVK3z22Wdu18nLFH744Qc8+uijSEhIwPnz59GkSRNcunQJDMOgZcuWfKokHODPMksiK21IXpUPiS5BEGIRHByMQ4cOoV69elbbDx06hKCgIF518rKG9PR0TJ48GW+//TbCwsKwadMmREVF4amnnkL37t15NYSwhmSWkAIkrkR5LO8JElzpQtFaaaA2qqA2CNxp3qiMTvgTJkzACy+8gBMnTrCTOBw9ehSfffYZ3nrrLV518vrG+u233zBs2DAAgFarRVFREUJDQ/H2229j7ty5vBrClaVLlyIhIQFBQUFISkrCwYMHnZbfv38/kpKSEBQUhNq1a2P58uWits9T/HVsWRpD1veYAtU2C0E4g+4XgiD48tprr2HNmjU4deoUxo0bh3HjxuHUqVPIyMjAa6+9xqtOXgZRoUIFlJSUAACqVauGv/76C40bNwYAXLt2jVdDuLBhwwZMmDABS5cuRVpaGlasWIEePXrg3LlziIuLsyl/8eJF9OzZE88++yzWrl2LH3/8ES+++CKqVq2K/v37i9ZOPviryBK+gQSEEAPzfUURXIIguDBw4EAMHDhQsPp4fbMlJyfjxx9/BAD06tULkydPxqxZszBy5EireYCFZsGCBRg1ahSeeeYZNGzYEAsXLkTNmjWxbNkyu+WXL1+OuLg4LFy4EA0bNsQzzzyDkSNHYt68eaK10V38MTJLEVnvQxE1wpvQvUYQBBdu3bqFTz/9FK+//jpu3LgBoGx82itXrvCqj5dZLFiwAIWFhQCA6dOno7CwEBs2bEDdunXdHrOMK3q9HidOnLAJSXft2hWHDx+2e8yRI0fQtWtXq23dunXDypUrUVpaioCAAJtjSkpK2Cg0ABQUFAjQelv8TWQBisx6E5IJQipQ9JYgCHucOXMGnTt3RkREBC5duoRnnnkGkZGR2LJlCy5fvszObuYOvCyjdu3a7P9DQkKwdOlSPtW4xbVr12A0GhEdHW21PTo6mp2+rTx5eXl2yxsMBly7ds3u/MRz5szBjBkzhGt4OUhmCTEgiSWkDskt4W1UdK9JmkmTJmH48OF47733EBYWxm7v0aMHnnzySV518vomrF27Nq5fv26z/datW1bCKwblp1djGMbplGv2ytvbbiY9PR35+fnswncAYHv4m9BSmoG40E+8hByh+5XwBqoSo6+bQLjg2LFjeP755222V69e3WGw0hW8jOPSpUswGm1vmJKSEt55EK6oUqUKNBqNzYVevXrVJhprJiYmxm55rVaLypUr2z1Gp9NBp9MJ0+h7+KPMEsJDMkAoBVOgmiK2BOHnBAUF2U3xPH/+PKpWrcqrTrfsY/v27ez/d+3ahYiICHbdaDTihx9+QK1atXg1xBWBgYFISkpCZmYm+vbty27PzMxEnz597B6TkpKCHTt2WG3bvXs3WrVqZTefVgz8SWhJZoWHRJZQKiS2hFhQlFYe9OnTB2+//Ta++uorAGW/oGdnZ+O1117jPUKVWxby2GOPsSc2j1NrJiAgALVq1cL8+fN5NYQLkyZNwpAhQ9CqVSukpKTg448/RnZ2NkaPHg2gLHXgypUrbHLx6NGj8dFHH2HSpEl49tlnceTIEaxcuRLr168XrY1mSGYJTyCZJfwBEltCaKQmtCq9Ciq1sJMlqPTKmHxh3rx56NmzJ6KiolBUVIT27dsjLy8PKSkpmDVrFq863bIRk6ns4ZOQkIBjx46hSpUqvE7Kl0GDBuH69et4++23kZubiyZNmmDnzp2Ij48HAOTm5iI7O5stn5CQgJ07d2LixIlYsmQJqlWrhsWLF4s6Ri3JLMEXElnCHyGxJQj/JDw8HIcOHcKePXtw8uRJmEwmtGzZEp07d+Zdp4ox95ziSXFxMe85euVAQUEBIiIi8HDTV6HVOM+19RehJZkVFpJZwt8hqRUef5wit3yU1mAswZ5f5iI/Px/h4eFebYvZHWrNnAW1wI5kKi7Gpalv+OS6hMJgMCAoKAhZWVlo0qSJYPXy+jY1mUyYOXMmqlevjtDQUPzvf/8DAEydOhUrV64UrHFygoSWcAcauYAg7kOfA8JTpJZ2QDhHq9UiPj7e7qADnsDrSfLOO+8gIyMD7733HgIDA9ntTZs2xaeffipY4+SAv8wIRsNzCQOJLEEQYuNvUVoSWnny5ptvIj09nZ1JTAh4WcqaNWvw8ccfo1OnTmwnLQBo1qwZfv/9d8EaJ3X8QWYBis4KAYksQRCE8JDQypfFixfjzz//RLVq1RAfH48KFSpY7T958qTbdfKylStXrqBu3bo2200mE0pLS/lUKTtIaAkukMwSBOFN/C1KS8gX84haQsLLWBo3boyDBw+yow6Y2bhxIxITEwVpmFQhmSW4QkJLEAQhHhSllTfTpk0TvE5e5jJt2jQMGTIEV65cgclkwubNm3H+/HmsWbMGX3/9tdBtlAwktAQXSGYJgvAF/hSlJaEl7MHr27d3797YsGEDdu7cCZVKhbfeegu//fYbduzYgS5dugjdRknA+ImokNB6BgktQbgPDenlOSS0hNwwGo2YN28e2rRpg5iYGERGRlotfOBtMN26dUO3bt34Hk5IEBJazyChJQjCF5DQShd1qQpqjcAzgJUqY0axGTNm4NNPP8WkSZMwdepUvPHGG7h06RK2bt2Kt956i1edHlmMXq/H1atX2ZnGzMTFxXlSLeEDSGg9g4SWIPhBUVrPIKEl5MoXX3yBTz75BL169cKMGTMwePBg1KlTB82aNcPRo0cxbtw4t+vkZTIXLlzAyJEjcfjwYavtDMNApVIJPpguIS4ktJ5BQksQ/CCh9QwSWkLO5OXloWnTpgCA0NBQ5OfnAwAeeeQRTJ06lVedvGxm+PDh0Gq1+PrrrxEbGwuVShmhcH+EhNYzSGgJgh8ktJ7hL0JLMqtcatSogdzcXMTFxaFu3brYvXs3WrZsiWPHjkGn0/Gqk5fRZGVl4cSJE2jQoAGvkxLSgITWM0hoCYIfJLSeQUJLKIG+ffvihx9+QNu2bTF+/HgMHjwYK1euRHZ2NiZOnMirTl5W06hRI1y7do3XCQlpQEJLEIQvIKH1DBJaQim8++677P8HDBiAGjVq4PDhw6hbty4effRRXnVyNpuCggL2/3PnzsUrr7yC2bNno2nTpggICLAqGx4ezqsxhHcgofUcitIShHuQzHoOCS2hZJKTk5GcnOxRHZztpmLFila5swzDoFOnTlZlqKMYQRAEUR4SWs8hoSWUxpo1a5zuHzp0qNt1cpbavXv3sv+/dOkSatasCY3GeoYtk8mE7OxstxtBeA+K0hIE4S1IZj2HZJZQKuPHj7daLy0txd27dxEYGIiQkBBxpbZ9+/bs/x9++GHk5uYiKirKqsz169fRuXNnDBs2zO2GEARBEMqAZFYYSGgJJXPz5k2bbRcuXMALL7yAl19+mVedvMJ25jSD8hQWFiIoKIhXQwjxoSgtQRBiQ0LrOf4is4DyhVZVCqg1rsu5A1MqbH1Sol69enj33Xfx9NNP4/fff3f7eLcsZ9KkSQAAlUqFqVOnIiQkhN1nNBrx008/oUWLFm43giAIgpAvJLLC4S9Cq3SZJfij0Wjwzz//8DrWLak9deoUgLJI7S+//ILAwEB2X2BgIJo3b44pU6bwagghLhSlFRa13kQjIBB+D8mscPiLzAIktEQZ27dvt1pnGAa5ubn46KOPkJaWxqtOt0zH3FlsxIgRWLRoEQ3dRRAE4YeQzAoHySzhrzz22GNW6yqVClWrVsXDDz+M+fPn86qTV/hu1apVvE5GEEqCorWEP0EiKzwktIQ/YzIJ/0yh36QJwgNIbAklQyIrDiSzBCEOJLUE4SEktoSSIJEVD5JZwlccOHAA77//Pk6cOIHc3Fxs2bLF5uf/8uzfvx+TJk3C2bNnUa1aNbzyyisYPXq0VZlNmzZh6tSp+Ouvv1CnTh3MmjULffv25dQm8+ADXFiwYAGnciS1BCEAJLaEnCGRFRd/klmAhFaK3LlzB82bN8eIESPQv39/l+UvXryInj174tlnn8XatWvx448/4sUXX0TVqlXZ448cOYJBgwZh5syZ6Nu3L7Zs2YKBAwfi0KFDaNu2rctznDp1CidPnoTBYED9+vUBAH/88Qc0Gg1atmzJlrM3hKwjSGoJQiDMYkByS0gdkljvQDJLSIUePXqgR48enMsvX74ccXFxWLhwIQCgYcOGOH78OObNm8dK7cKFC9GlSxekp6cDANLT07F//34sXLgQ69evd3mO3r17IywsDKtXr0alSpUAlE3IMGLECDz44IOYPHmym1cJ0LcvQQiMWm8iaSAkh/m+pHtTfNQlBr8SWlWJkYTWBxQUFFgtJSUlgtV95MgRdO3a1Wpbt27dcPz4cZSWljotc/jwYU7nmD9/PubMmcMKLQBUqlQJ77zzDu/RD0hqCUIkSCAIX2EpsHQfegezyJLMEpaoDYC6VODl3i1Ws2ZNREREsMucOXMEa3deXh6io6OttkVHR8NgMODatWtOy+Tl5XE6R0FBAf7991+b7VevXsXt27d5tZvSDwhCZCgtgRAbklbf4U8Sa4ZEVhrk5ORYzReg0+kErb98LivDMDbb7ZXhmgPbt29fjBgxAvPnz0dycjIA4OjRo3j55ZfRr18/Xm0mqSUIL2EpHiS4BF9IYH2PP4osQDIrNcLDw0WbBCsmJsYm4nr16lVotVpUrlzZaZny0VtHLF++HFOmTMHTTz/NpjRotVqMGjUK77//Pq920zern+CvD2GpQj8LE1ygNAJp4W/pBWYozcD/SElJQWZmptW23bt3o1WrVggICHBaJjU1ldM5QkJCsHTpUly/fp0dCeHGjRtYunQpKlSowKvdJLUE4WNIWgh78kr3gjTwx1xZMySzyqGwsBBZWVnIysoCUDZkV1ZWFrKzswGUjVwwdOhQtvzo0aNx+fJlTJo0Cb/99hs+++wzrFy5ElOmTGHLjB8/Hrt378bcuXPx+++/Y+7cufj+++8xYcIEt9pWoUIFNGvWDBUrVsTly5c9mmmMpNaP8MeHshwhuVEmjsSV3l/p4c8iC5DMKpHjx48jMTERiYmJAMomPkhMTMRbb70FAMjNzWUFFwASEhKwc+dO7Nu3Dy1atMDMmTOxePFiqzFuU1NT8eWXX2LVqlVo1qwZMjIysGHDBpdj1K5evZodKszMc889h9q1a6Np06Zo0qQJcnJyeF2nijFn/hJ2KSgoQEREBDompUOrDfJ1czzGpKM0aqVAebnSgwRVvvirwFqiNJE1GEuw55e5yM/PFy331BFmd6j38mxodMK6g7GkGBfef90n1yUEKSkpeO655zBixAgAwHfffYfevXsjIyMDDRs2xNixY9GoUSN8+umnbtdNhuNnqEsMJLYKwZlAkfAKC8mqMiGRVZ7ImlGXlEJtLPV1Mwg7/PHHH2jVqhW7vm3bNjz66KN46qmnAACzZ89mhdddyG78EBJb5cNVwvxZfklU/RMS2TKULLOEtCkqKrKKMB8+fBgjR45k12vXrs15rNvykNn4KSS2BCCM2HlLjElCCb6QyN6HZJbwNfHx8Thx4gTi4+Nx7do1nD17Fu3atWP35+XlISIiglfdZDV+DIktIQQkm4TUIIm1RqkiC0hfZtV6QM1tLgLOMHph6/M2Q4cOxZgxY3D27Fns2bMHDRo0QFJSErv/8OHDaNKkCa+6yWj8HBJbgiDkDkmsLUoWWUD6Mks45tVXX8Xdu3exefNmxMTEYOPGjVb7f/zxRwwePJhX3WQzBIktQRCyg0TWPiSzhNRRq9WYOXMmZs6caXd/ecl1BzIZAsD9LwiSW4IgpAhJrGNIZAmiDDIYwgqK2hIE4WtIYF2jdJEFSGYJ9yF7IWygqC1BEN6EJJYb/iCyAMkswR+yFsIhJLcEQQgNCax7kMgSSqKgoEDUWdD8d+R1gjP+PAc6QRD8MT87LBfCOaoSo9WidNQlpSS0fkSlSpVw9epVAMDDDz+MW7duCVo/heAIzlh+IVH0liAIS0hY+eMP8moJSaz/EhoaiuvXryMqKgr79u1Daamw9wKZCcELSk0gCP+E5FUYSGQJf6Rz587o2LEjGjZsCADo27cvAgMD7Zbds2eP2/XLxkhu3ryJcePGYfv27QCARx99FB9++CEqVqzo8Jjhw4dj9erVVtvatm2Lo0ePitlUv4KitwShXEhghcPfJNaMv8usuhRQC5zoycj4JV27di1Wr16Nv/76C/v370fjxo0REhIiWP2ysZAnn3wSf//9N7777jsAwHPPPYchQ4Zgx44dTo/r3r07Vq1axa47+ouA8JzyX4AkuQQhfUhcxcFfJRYgkSUcExwcjNGjRwMAjh8/jrlz5zoNTrqLLKzjt99+w3fffYejR4+ibdu2AIBPPvkEKSkpOH/+POrXr+/wWJ1Oh5iYGM7nKikpQUlJCbteUFDAv+F+DkVxCUIakLiKjz9LLEAiS7jP3r172f8zDAMAUKlUHtUpi9EPjhw5goiICFZoASA5ORkRERE4fPiw02P37duHqKgoPPDAA3j22WfZXneOmDNnDiIiItilZs2aglyDv0M9oAlCXOyNNECfN/Hwt1EK7GEeuYCEluDLmjVr0LRpUwQHByM4OBjNmjXD559/zrs+WYTP8vLyEBUVZbM9KioKeXl5Do/r0aMHHn/8ccTHx+PixYuYOnUqHn74YZw4cQI6nc7uMenp6Zg0aRK7XlBQQGIrAva+aCmaSxCOITn1Lf4qruUhgSWEYsGCBZg6dSrGjh2LtLQ0MAyDH3/8EaNHj8a1a9cwceJEt+v0qUVMnz4dM2bMcFrm2LFjAOyHpBmGcRqqHjRoEPv/Jk2aoFWrVoiPj8c333yDfv362T1Gp9M5FF5CXEh0CX+GpFVakMSWQRJLiMWHH36IZcuWYejQoey2Pn36oHHjxpg+fbr8pHbs2LF44oknnJapVasWzpw5g3///ddm33///Yfo6GjO54uNjUV8fDwuXLjgdlsJ3+Dsi56El5ADJKvShwTWGhJZwhvk5uYiNTXVZntqaipyc3N51elTK6hSpQqqVKnislxKSgry8/Px888/o02bNgCAn376Cfn5+XZfEEdcv34dOTk5iI2N5d1mQjqQ8BK+gkRVvpDA2odElvA2devWxVdffYXXX3/davuGDRtQr149XnXK4pu/YcOG6N69O5599lmsWLECQNmQXo888ojVyAcNGjTAnDlz0LdvXxQWFmL69Ono378/YmNjcenSJbz++uuoUqUK+vbt66tLIbwEF+kg8SXMkKQqExJYx5DEEr5mxowZGDRoEA4cOIC0tDSoVCocOnQIP/zwA7766itedcrmW/2LL77AuHHj0LVrVwBlky989NFHVmXOnz+P/Px8AIBGo8Evv/yCNWvW4NatW4iNjUXHjh2xYcMGhIWFeb39hPRwV2RIgqULSSlBAuscv5RYgadg5YPGAGiEHmdKIY+7/v3746effsIHH3yArVu3gmEYNGrUCD///DMSExN51Smbb+nIyEisXbvWaRnzOGdA2QC/u3btErtZhB8hpDj5qyCTfBKeQvLKDb+UWADQ++l1y5SkpCSXbucO/vnNShA+huSOIJxD8uoefiuxAIkswUJSSxAEQfgMkld++LXEAiSyhF1IagmCIAjRIXn1DL+XWIBElnAJSS1BEAQhCCSuwkESew8SWcINSGoJgiAIzpC4Cg8JrAUksYQHkNQSBEEQLCSt4kICawcSWb+kuLgYH374Ifbu3YurV6/CZDJZ7T958qTbdZLUEgRB+BEkrd6DBNYBJLEEgJEjRyIzMxMDBgxAmzZtoFKpPK6TpJYgCEIhkLD6DhJYJ5DEEnb45ptvsHPnTqSlpQlWJ0ktQRCEDCBhlQYkrxwgiWVRlwJqzwOQVjAKeXmrV68u+AyvQk/eplhUehNUJUb6YiEIQjDMzxQuC+Fd1CWldhfCDvpS64UgODB//ny8+uqruHz5smB1UqSWB+W/YBidxkctIQhCapCAygsSVR6QuBIC0KpVKxQXF6N27doICQlBQECA1f4bN264XSdJrQCQ5BKEciFJVQYkrzwhgSVEYvDgwbhy5Qpmz56N6Oho6igmVRx9CZLsEoTvIDlVPiSuAkASS3iJw4cP48iRI2jevLlgdZLUehFnX6okvAThGhJTgsRVQEhgCR/SoEEDFBUVCVondRSTCNRRhFAy7nSIos8A4aiTFgmtB5TvzEVC63csXboUCQkJCAoKQlJSEg4ePOiw7PDhw6FSqWyWxo0bs2UyMjLslikuLubUnnfffReTJ0/Gvn37cP36dRQUFFgtfKBIrYzg86VOEWDCHUgcCW9AcioyJKxEOTZs2IAJEyZg6dKlSEtLw4oVK9CjRw+cO3cOcXFxNuUXLVqEd999l103GAxo3rw5Hn/8caty4eHhOH/+vNW2oKAgTm3q3r07AKBTp05W2xmGgUqlgtHo/vcRSa3CEUtSSJbFgaSSUAIkrV6C5JXgyIIFCzBq1Cg888wzAICFCxdi165dWLZsGebMmWNTPiIiAhEREez61q1bcfPmTYwYMcKqnEqlQkxMDK827d27l9dxziCpJXhB8kUQ/gtJqw8ggSXKUf4nep1OB51OZ1NOr9fjxIkTeO2116y2d+3aFYcPH+Z0rpUrV6Jz586Ij4+32l5YWIj4+HgYjUa0aNECM2fORGJiIqc627dvz6mcO5DUEgRBECwkrD6G5NVzSvRl/5r0vm0HAI2egQaMsJXqy+qrWbOm1eZp06Zh+vTpNsWvXbsGo9GI6Ohoq+3R0dHIy8tzebrc3Fx8++23WLdundX2Bg0aICMjA02bNkVBQQEWLVqEtLQ0nD59GvXq1XNZ74EDB5zuf+ihh1zWUR6SWoIgCD+AZFVCkLgKR4nvxdVX5OTkIDw8nF23F6W1pPw4sObcVVdkZGSgYsWKeOyxx6y2JycnIzk5mV1PS0tDy5Yt8eGHH2Lx4sUu6+3QoYPTNlJOLUEQhJ9BsiphSF6Fx48ltjzh4eFWUuuIKlWqQKPR2ERlr169ahO9LQ/DMPjss88wZMgQBAYGOi2rVqvRunVrXLhwwXXjAdy8edNqvbS0FKdOncLUqVMxa9YsTnWUh6SWIAhCYpCoyggSV/EggRWEwMBAJCUlITMzE3379mW3Z2Zmok+fPk6P3b9/P/7880+MGjXK5XkYhkFWVhaaNm3KqV2WHdHMdOnSBTqdDhMnTsSJEyc41WMJSS1BEIQXIFGVMSSu4kMCKyqTJk3CkCFD0KpVK6SkpODjjz9GdnY2Ro8eDQBIT0/HlStXsGbNGqvjVq5cibZt26JJkyY2dc6YMQPJycmoV68eCgoKsHjxYmRlZWHJkiUetbVq1ao2w4RxhaSWIAiCBySpCoKk1buQwHqdQYMG4fr163j77beRm5uLJk2aYOfOnexoBrm5ucjOzrY6Jj8/H5s2bcKiRYvs1nnr1i0899xzyMvLQ0REBBITE3HgwAG0adOGU5vOnDljtc4wDHJzc/Huu+/ynjpXxTCMwN3ylEVBQQEiIiLwcNNXodU4T8ImCEKekKD6ASSu3sfH8mowleD7f1YgPz+fU+6pkJjdIfGpWdAEcpuMgCtGfTFOffGGT65LSNRqNVQqFcpraHJyMj777DM0aNDA7TopUksQhKIgQfVDSFh9D0VfCTe5ePGi1bparUbVqlU5z0hmD5JagiAkCckpwULSKh1IXgmBKD+RgxCoBa+RIAjiHuqSUt4L4SfoS10vhHcp0TteCMJDfvrpJ3z77bdW29asWYOEhARERUXhueeeQ0lJCa+6KVJLEIRTSDAJXpCMSh+SVNFRlwJq1/MbuAUj84/W9OnT0aFDB/To0QMA8Msvv2DUqFEYPnw4GjZsiPfffx/VqlWzOzuaK0hqCULhkJQSgkKyKi9IXAmJkZWVhZkzZ7LrX375Jdq2bYtPPvkEQNn0v46m/HUFSS1BSBySUsIrkKzKE5JWQmbcvHnTaiaz/fv3o3v37ux669atkZOTw6tuklqC8AIkpoRPIFFVBiSuhIKIjo7GxYsXUbNmTej1epw8eRIzZsxg99++fRsBAQG86iapJQg3IDklfA6JqrIgYSX8jO7du+O1117D3LlzsXXrVoSEhODBBx9k9585cwZ16tThVTdJLeGXkJwSkoJEVbmQtBKEFe+88w769euH9u3bIzQ0FKtXr0ZgYCC7/7PPPkPXrl151U1SSygCklRCUpCk+gckrAThNlWrVsXBgweRn5+P0NBQaDQaq/0bN25EaGgor7pJagnJQqJKSAaSVP+DhJUgRCUiIsLu9sjISN51ktQSPoGElfAZJKj+DckqQSgWklpCFEhaCa9BkkoAJKsEQZDUEvwgaSVEgySVMEOiSnCl2M60qibf3z/qUgYaMILWyZQKW5+SIKklnELySngMSSphCYkq4S72hJUg7EBSS7CQwBKcIEklykOiSvCBZJUQGJJaP4UElrCCRJUoD4kqwReSVcJHkNT6CSSxfgiJKmEJSSrhKSSrhMQhqVUoJLEKhmSVAEhSCc8hSSUUBkmtgiCRlTkkq/4NSSohBCSqhB9DUitzSGRlBEmr/0GiSngCCSpBuIXa1w3gyqxZs5CamoqQkBBUrFiR0zEMw2D69OmoVq0agoOD0aFDB5w9e1bchnoBdUkpuxASQV/qeiHkT4nevYUgLCkucW8hCMItZCO1er0ejz/+OF544QXOx7z33ntYsGABPvroIxw7dgwxMTHo0qULbt++LWJLxYNE1seQsCoTklSCD+4KKkkqQYiObNIPZsyYAQDIyMjgVJ5hGCxcuBBvvPEG+vXrBwBYvXo1oqOjsW7dOjz//PNiNVVwSGS9CAmqMiABJbhCskmIiEbPQMPQjGLeQjZS6y4XL15EXl4eunbtym7T6XRo3749Dh8+7FBqS0pKUFJy/yFXUFAgelsdQTIrIiSv8oNElXAFCSpB+DWKldq8vDwAQHR0tNX26OhoXL582eFxc+bMYaPCvoJkVkBIXqUPySphDxJUgiDcxKc5tdOnT4dKpXK6HD9+3KNzqFQqq3WGYWy2WZKeno78/Hx2ycnJ8ej87kJC6wGU6yotKE+VAPjlnpLQEgTBA59GaseOHYsnnnjCaZlatWrxqjsmJgZAWcQ2NjaW3X716lWb6K0lOp0OOp2O1zk9hYTWDUhYfQvJqH9CskkQhITxqdRWqVIFVapUEaXuhIQExMTEIDMzE4mJiQDKRlDYv38/5s6dK8o5PYGE1gUksd6FpFX5kKASBKEwZJNTm52djRs3biA7OxtGoxFZWVkAgLp16yI0NBQA0KBBA8yZMwd9+/aFSqXChAkTMHv2bNSrVw/16tXD7NmzERISgieffNKHV2ILCa0dSGLFhaRVeZCkEgTh58hGat966y2sXr2aXTdHX/fu3YsOHToAAM6fP4/8/Hy2zCuvvIKioiK8+OKLuHnzJtq2bYvdu3cjLCzMq213BgntPUhihYWkVf6QpBIEQbiFimEEHkBNYRQUFCAiIgIPN30VWo2wubZ+L7Qksp5D8iofSFIJwqsYTHp8f2MV8vPzER4e7tVzm90huddMaAOCBK3bUFqMo99M9cl1SR3ZRGoJhUAi6z4krtKFRJUgCCeo9SaoGZOwdZYKW5+SIKn1EX4VpSWR5QbJq3QgWSUIgpAdJLWEeJDMOoYE1jeQrBIEQSgWklofoOgoLYmsLSSw3oGElSAIwq8hqSWEgWS2DBJYcSBhJXwIU0L3HxdUPpq4iCDMkNQSnuHPMksCKxwkrYQHkHRKA3feBxJgQgxIagl++KPMksR6BokrYQGJqH/j7P0n4SX4ovZ1AwgZ4k9CW6K/vxCuKS5xvBCKgSkp8XghCEfQ/SIOS5cuRUJCAoKCgpCUlISDBw86LLtv3z6oVCqb5ffff7cqt2nTJjRq1Ag6nQ6NGjXCli1bxL4Mp1CkluCOP8gsyatrSFAVA8kCIRfK36sUzXWPDRs2YMKECVi6dCnS0tKwYsUK9OjRA+fOnUNcXJzD486fP281wUPVqlXZ/x85cgSDBg3CzJkz0bdvX2zZsgUDBw7EoUOH0LZtW1GvxxE0o5gLxJhRTJajHyhZaElk7UPyKitIUAl/xZHgSmFGsdQuM0SZUexw5jTk5ORYXZdOp4POwWvRtm1btGzZEsuWLWO3NWzYEI899hjmzJljU37fvn3o2LEjbt68iYoVK9qtc9CgQSgoKMC3337LbuvevTsqVaqE9evX87w6z6D0A8I1ShRaSiuwhtIFJAf9nE8Q3JDyZ0BTaoJGL/Byb0axmjVrIiIigl3sySkA6PV6nDhxAl27drXa3rVrVxw+fNhp+xMTExEbG4tOnTph7969VvuOHDliU2e3bt1c1ikmlH5AOEdJQksCS7LqY6T6xUsQSoApKfGrtAR7kVp7XLt2DUajEdHR0Vbbo6OjkZeXZ/eY2NhYfPzxx0hKSkJJSQk+//xzdOrUCfv27cNDDz0EAMjLy3OrTm9AUks4RilC688ySxLrFUhWCUIamD+L/iC34eHhbqVVqFQqq3WGYWy2malfvz7q16/PrqekpCAnJwfz5s1jpdbdOr0BSa0PMOkCpJ9XK3eh9UeRJYEVBRJWgiDkTJUqVaDRaGwiqFevXrWJtDojOTkZa9euZddjYmI8rlNoKKeWsEXOQutPebKUAysIlKtKEMqDPrv3CQwMRFJSEjIzM622Z2ZmIjU1lXM9p06dQmxsLLuekpJiU+fu3bvdqlNoKFJLKAN/EFkSV97QFxxB+B+Mnj73ZiZNmoQhQ4agVatWSElJwccff4zs7GyMHj0aAJCeno4rV65gzZo1AICFCxeiVq1aaNy4MfR6PdauXYtNmzZh06ZNbJ3jx4/HQw89hLlz56JPnz7Ytm0bvv/+exw6dMgn1wiQ1PoMyaYgyDFKq1ShJYl1CxJXgiAI+wwaNAjXr1/H22+/jdzcXDRp0gQ7d+5EfHw8ACA3NxfZ2dlseb1ejylTpuDKlSsIDg5G48aN8c0336Bnz55smdTUVHz55Zd48803MXXqVNSpUwcbNmzw2Ri1AI1T6xIxxqk1Q1LrIUqUWRJZp5C4EgTBFQOjxw+F63w6Tu2DHaZBqxV4nFpDMQ7um+GT65I6FKn1IZKL1spFaJUmsySyNpC8EgRBEO5CUkvIC6UILYksCwksQRAEIQQktT5GctFaKSN3ofVzkSV5JQjCm6gCfT9WrVpvgtpkErZOg7D1KQmSWkIeyFlo/VRmSWL9C1NRka+bIDjq4GBfN4EgCDcgqZUAFK11gVyF1o9klgRWnihRRIWEz+tDIiwNVDodYJLpdwfBG5JaiUBi6wA5Cq0fyCxJrPQgQZUGrt4Hkl7x8Ycpcgn7kNQS0kVuQqtwmSWR9T4kqsrD3ntKoksQwkBSKyEoWmuBnIRWoTJLEisuJKyEGRJd4aAorX9DUisxSGxlhAJllkRWOEhaCU+wvH9IcLlBQkuQ1HKECVQDRu+cy+/FVg5RWgUJLYksf0hcCW9AgusaEloCIKl1C0angarEO2brE7ENDJDPrGK+RCFCSzLLHZJXQiqY70WS2/uQ0BJmSGrdhNFpAMBrcktIDJkLLYmsc0heCblAclsGCS1hCUktT7wRtfX7NAQpQTKrOEhgCSVgKiryW7GVg9CqSwxQGw3C1mkQtj4lQVLrAYoUW1+nIMghn1ZGkMyWQQJLKBl/FFs5CC3hfUhqPcQb6QgUsfUxMozS+rvMksQS/oa/iC3JLOEMklqBEDtq61Wx9XW0VkrITGj9VWZJYglC+ZDQEq5Q+7oBSsIctRULky5A1PqtCPTiuaSKjISWKSnxO6E1FRWxC0EQyv7jjoSW4AJFagVG7HQExaci6AIpr9ZN/ElmlfylTRCELSSzhDuQ1IqEmOkIXhNbSkOQPP4gtCSyBOGfkNAS7kJSKyIktjJGBqkHShZaElmCcB+ldBQjmSX4Qjm1IsPoNKLl2notx9bb+bW6QO+eT4YoVWgpR5Yg/BsSWsITKFLrJcSK2prFVtF5toQVShRaElmC8Ay5R2lJZgkhIKn1ImJ2IhM9HcHbaQjUYcwuShNaklmC8BwSWumi0pugMgr7na8ymgStT0lQ+oEPkG06Ag3zRQgEpRkQBKHS6RQttIT3IanliEmnhUknXGBbrFxbRYkt5dZaoZQoLcksQQiHHKO0JLOEWJDUuomQYguIE7U16QLElVuK2BI8oOistAgODfJ1EwgPkZvQkswSYkNSywOK2sJ7YuuraG0QPXiFhGRWWtRpUQv/9++nqNOilq+bQvBAHRwsS6ElCLEhqfUAMeRWaEhslYGcUw9IaKVHx0GpCAjUosPAVF83hXATOcosCS3hLUhqBUBIuRUjaitqOoKSxVZC0Vr6UiCEpP09me0ysA06mC6jmekq1Az1qJY6chJan8tskA4IooCIvyEbqZ01axZSU1MREhKCihUrcjpm+PDhUKlUVktycrJobfTbqG1ggHfkliK2soOitNKjTotaiI6rAgCIjI/B602LMM+0F58bv0aa6W8ft46wh5zSDaQhsxQE8FdkM06tXq/H448/jpSUFKxcuZLzcd27d8eqVavY9cBAccXILLbqEoPHdYkxrq2o49l6Yyxbb49fG6STxZS5BGGPmvWroXazeKttvR9pCMZggEqrBQwGYMoUYMcOVAbwFoBN6gfw7S+FyDn/j0/aTFgjJ5n1KSSyBGQktTNmzAAAZGRkuHWcTqdDTEyMCC1yjpTlVtRZyEhsiXtQlNb3DJ8xCA/2b2uznWGYsv9oNMDTTwNPP83+bDcAQPT/HcHMwYu81k7CFpJZDpDIEuWQjdTyZd++fYiKikLFihXRvn17zJo1C1FRUQ7Ll5SUoMSiU05BQYFH5xdabmURtVWi2EoAlU4n6w5jhPeZ/9xyGI1GdBiYCpOJgVqtAgCoVGX/wvwvAJhMgFoNrF+PXS+uA1DB+w0mSGa5ICOZVZeUQq0RNtNTbfTi7J4yQzY5tXzo0aMHvvjiC+zZswfz58/HsWPH8PDDD1tJa3nmzJmDiIgIdqlZsyYAwBTo2UslVGcyoTuSidaJzBt5tt7MsZXRQ5QgzNwtKMLspxZj3jPLYCgphbHUwR/XpaVASQkwfDjw5JMIKbjm1XYS8sibNefL+kxoKV+WcIFPpXb69Ok2HbnKL8ePH+dd/6BBg9CrVy80adIEvXv3xrfffos//vgD33zzjcNj0tPTkZ+fzy45OTnsPk/FFhCuM5kYcisKJLaC4vO8NTeQ+he0P7F79X6MbvWqc6lt0QJYvRoAcAP03nkLOcmsTzCLrASev4T08Wn6wdixY/HEE084LVOrVi3BzhcbG4v4+HhcuHDBYRmdTgedkw+vpdiq9fyGwJFqSoJoubZipyN4MxVBAvm1lIZA8MFkNCEwxMEsYiEhgMEAE4BrCMavqipebZs/InWRBSjFgJAfPpXaKlWqoEoV7z08r1+/jpycHMTGxgpSn1lwfS23suhIZo7YiiW3JLaSRB0cTB3GJEK7vm1hMpqg1qjBlJZCFRBQFqENCACMRpj694fq/fexTN0SJpWiM9N8CsmsE0hkCQ+RzZMrOzsbWVlZyM7OhtFoRFZWFrKyslBYWMiWadCgAbZs2QIAKCwsxJQpU3DkyBFcunQJ+/btQ+/evVGlShX07dtX0LaZAtUepSZIOd9WcMRMR9AFei8dgX4O44wcvsT9gfYDU6BSq8AwDC4ePIObLVOBgwcBhgHUahgHPYG31Wn4UV3D101VJFJPM/Bpviw9TwmBkI3UvvXWW0hMTMS0adNQWFiIxMREJCYmWuXcnj9/Hvn5+QAAjUaDX375BX369MEDDzyAYcOG4YEHHsCRI0cQFhYmShuVKLeidCQTuxOZn+TZ+nyQc0I2VK1ZGfUSE2AymvBp+jq80H0eBp+pgZe7zsKe9KUwGU0ISGqJP+Ka+7qpisIssnKQWa/jjVxZmrDHiqVLlyIhIQFBQUFISkrCwYMHHZbdvHkzunTpgqpVqyI8PBwpKSnYtWuXVZmMjAy7faGKi4vFvhSHyGZIr4yMDJdj1LJjLwIIDg62eQO8hRLTEkRLSRAzHQHwTkqCj9MR5JCKQGkIvoUxMfhp50msfWcTzh/7q2ybSo3TqIrT8w9i68FcPPVGP6tnKMEfKUss4Ae5subnv0naz0VvsmHDBkyYMAFLly5FWloaVqxYgR49euDcuXOIi4uzKX/gwAF06dIFs2fPRsWKFbFq1Sr07t0bP/30ExITE9ly4eHhOH/+vNWxQUEOcve9gIqhp5hTCgoKEBERgQc7TINWy++N4iu37PECdCgDhMu5FWVsWzE7knkr19bHebZSF1szJLeEEpG6yAIKz5W1E5U1mErw/cUPkZ+fj/DwcPHbYIHZHTo/MAlajbDXbzCW4Ps/FiAnJ8fqupx1dG/bti1atmyJZcuWsdsaNmyIxx57DHPmzOF03saNG2PQoEF46623AJQFGydMmIBbt27xvxiBkU36ga8xBqhh5JlaIKW0BCGQXUqCt3JtfZwXJpdUBDl8+RMEV+SSXuD154O30gu82ZeCD6WlZUEbIZfSsiBQzZo1rcbVdySner0eJ06cQNeuXa22d+3aFYcPH+Z0GSaTCbdv30ZkZKTV9sLCQsTHx6NGjRp45JFHcOrUKR4vknDIJv1AKpjFVsMj+iqFtAS/T0lQeDqC+YtL6lFbswRQ1JaQI1KWWMD/IrL+ir1IrT2uXbsGo9GI6Ohoq+3R0dHIy8vjdK758+fjzp07GDhwILutQYMGyMjIQNOmTVFQUIBFixYhLS0Np0+fRr169XhckeeQ1PKE5NayPQLLrZjDf3kr19b8cPeh3EpdbAHKtSXkg9RFFiCZ9TfCw8PdSqtQWU6LjbJ+SOW32WP9+vWYPn06tm3bhqioKHZ7cnIykpOT2fW0tDS0bNkSH374IRYvXsy5XUJCUushJLeW7SG5tcGHcktRW4LgjxwkFiCRJVxTpUoVaDQam6js1atXbaK35dmwYQNGjRqFjRs3onPnzk7LqtVqtG7d2ukEV2JDObUCYQxUs4u7SCHnVtLDgImdb+sNaOgvl8hh+CNC2cjlHqQ8WcIdAgMDkZSUhMzMTKvtmZmZSE1NdXjc+vXrMXz4cKxbtw69evVyeR6GYZCVlSXYBFd8oEitCPCN3no6BS9FbnniB1FbQD4pCQBFbwnvIHV5tYQism4SGAAYPRt5SElMmjQJQ4YMQatWrZCSkoKPP/4Y2dnZGD16NAAgPT0dV65cwZo1awCUCe3QoUOxaNEiJCcns1He4OBgREREAABmzJiB5ORk1KtXDwUFBVi8eDGysrKwZMkS31wkSGo5YwpUA24Ofuar1ATLqC1fwSW5FREJpCQA0k9LAKylgwSXEAISWQ7IWWQJuwwaNAjXr1/H22+/jdzcXDRp0gQ7d+5EfHw8ACA3NxfZ2dls+RUrVsBgMGDMmDEYM2YMu33YsGHsnAG3bt3Cc889h7y8PERERCAxMREHDhxAmzZtvHptltA4tS4wjzWX3GsmtAH3x6nlI5t85NaT81kdL8BYt5Id51as0RJofFtJQnJLuANJLEcUKLLm8Vx9Ok5twkvQqgUep9aH4+9KHYrU8oRPJNUy35ZvaoISOpVR5LYcEkhLAOQjt+UlhSSXMCMngTVDIssDisgSDiCp9RC+skl5tyS3NpDc8oJSFPwXOUosQCLLCxJZggMktQLBVzZ9Fb2VUt4tyW05LL90KO/WLSiKq0zkKq9mSGJ5ogSRLdEDatdjwbqFyUupcTKEpFYEfBW99VVqgt/KLeA30VtAfoIL2JchEl1pI3eBBSQwZTWJLAAIP507IXlIajliDFRBFVD215ZGz61vnafRW7mlJliOc+uJ4Fo+iAQRXMuHpNyjtz7sVCbX9ITykOhKAyXIqxm/kFhAfiJrlPezinAfkloeGAPdk1tAXh3LKDWBB96K3vo4NQGw/QKXu+QCjgWLZJc/SpLW8vhcYgF5iyxFYwmRIKn1ALPcAhS9dYTko7dKmIIXkEQEF1CG4FriSsz8WXqVLK3lIYkVABJZwguQ1AoERW9dI8norTdSEwASXIXijthJWYD9SVBdIQmBNUMiy0IiS3CBpJYjpgBuvRcpeusaoeW2rC0UvWWRQP4toMw0BU8gcZQmJLECIvBoBSSyhLuQ1LqBu9FYTwTXH6K3QqUmlLWForc2lP+CJMkl/BxJCSwgf4kFJBmNNX+3MAa1i5KE0iCp5QEfWXVXiP+/vTuPjqo6wAD+PWCSCQIja5YaQoAKpWglAUkiSpBj2KRAK0u1CKfKESzaQC2glgZaLYayaFG2U4x/0KOcUxIPIiqhJoGWaFli2YNiACFJEQ4EypZlbv+IM5193nuzvGW+3zlzTF7uve9ebmbel+udN9G+761eVm8Bne69BYwdcAHdbFFw8BUwGHQpHHQXXh2iFWKBmFqNdb1+UGxjqA2R0oBrpO0J3HvrgQE34hh0SSkG2O/E6GoskSuG2jCKZsDl6q2SfoR59RYwb8AFdBVyAQZd0nFwdcUQ61dMr8beagTCvQuCnyjmF0OtTHYLIH33vGwjI8Oo3X9rxu0J4Vy9BRhwI07nIRfwH3IYdo3LEMHVIdoBFoi5EAuEHmRdrz0UGzjjKthdnq/BAq6ZtycoPR/AgBsSz4saQ66XQMGIgVdbhgqtrrQIsABDrKp+eESaZnXXGDIuhtoQ2VWs3gL6CrhGvXsCEMMBF9BmFRcwVMh1JSdUMfiqY9jA6smMARYIe4gFdBxkKabxtyFMlKzeAtx/61WfATc0Wq3iAr6DgEGCriel4cyMIdg0ATUQrcKrA0NsCP1gbCH/+NshU0ucBMjc96o24Ebq/rdG2n8LxFjABcwXcgFTBd1AYiIAGp3ZAyxg6hALMMiSfPxNUUFJqIzk/lvXOmZ8gxmg34Db2h+DrOIC2odcwH+4MGHYpSjTOrg6RCPAAgyxgeq6XHPsbfjhC7GGoTZEegm4WrzBrPVcsRlwW/tj0FVcQB8h14Fhl+TQS3B1MHCABcwZYokYamWyW4BgT10GXAbc1v6EMYhqFXIBbYMuEDjEMPCak96Cq0O0AizAECunPoMs+cFQq0CkgigDrr4CLqDTbQpA9EIuoM+g6xAs/DD06o9eA6uraIZXQPcBFjB2iG2xMPzGGoZalRhwg50r9ICr9JxAeAIuELlVXMDAIRfQd9B1JTdAMfyGxghB1Zdoh1cgYgEWYIh1aPGsF+GXQzlE420ISd71VnabQoevuTrBUBsGDLjBzhX6fXCVnhPwfmHVwyouEOWQC2gTdAF9hl1PakOZmcKwUYOpHFqEV8AwARYwWYilmMdQK1NLOwDfvZa0DZARjB5w5dYxWsAF9LmKC0Q45ALRX811MHLYDcbMQdBotAquDhEMsABDrCcGWQqEoVaFFpfXGLMFXDV11G4Z8Hxx0mqbAqCfVVxAg5ALRC/oAoFDiBkCL4WX1qHVIcLhFdBngAWMF2Id57NLDMCxhqE2REYNuHLaVtoXZ58MvooLxFjIBfxfsKMZdoHgAYah13z0ElodohBegfAHWIAhloihVia7BZAsgYOjkQKu0rbVlAe0D7hKz+tsIwJbFYDIhFwgQkEX0H5V15OcAMTgqw96C6ueDBxeAf0EWIAhlvSDoVYhu8frk7/wqCbgBmoP0G/AlVtHi20KnufVchUX8H0hMkXQBbQNu66UhimG4OD0HlD9iVJwddB7gAW0v0dstEJsS5yEFkkKXpBMhaE2RHLCY4vH61w4VnHVBtxg7SptO5Q64VjFbT1f9PbiAuENuUBkVnOBKAddIHCA0Evg9SWcgU1PAdmoQVSpKAdXIHLhFdBXgAW0eVOX2hBLxFArkz0OkL67RrTxc92SHUjDvE1B6aqp3vfhAsbdquBsR4eruYD/i3FEwy4QPHjoOfQqEStBMlo0CKyuIhleAf0FWIAhloyNoVYFu8t1S28BFzD+NgVAm1Vcz/MqPbdbOwZZzXWI+qqup1gJvfR/GgdWV0YKrw5ar8IC+g+xdguDb6xhqA2R0oALaLcPFwgcKkPZphCs7VDqhGsVt/V85gy5DuEMu4Eu9FENvID8AMTwqy0dBVVPkQ6uDuEOsHpYgQVCuz9stEKsVx0d7Aay37wFuxTmRQjB1zl/GGplsrdrvfsB4D/syQm4gHb7cAF9reKqrWPGkAuEHnSByG5dcKWrwOtKbahiGPam44DqS7RCq4NeV1+dbRloFbb1fMpCLLcfkC8MtSrICXt2j611WmxTCNpmlN5sJqd9tXUAfYVcped3ayuKQReITNgFdBx4A4lWgAs1PBssaIZTtEMrEJng6mCGABvKuSMdYu0WwC7vEkImwlArk/Dzeio37EVqmwKg/ZvNgrWtpn21dQBtQ66v8yvtg1tbEQq6QPTDLiAvmOg2+IZDDIfSQLQIrK5iJbwCxgiwaup4XpMoNjHUKiBrhVbhKm6o2xQA7ffieratdKuCnPbV1gHCG3Jbzxt60FUbcgH/F0kjh11XcgOOqcOvCWgdVD1FMrgC4Q2vgPYBNpQ+MMSSVhhqVZITEGWVieA2BUD7vbhy2lbavq86SuqFGjD1tprrbDOCq7pA8FAQrdDroCY0MQiro7eA6kukQ6tDuMMrwAArh5IQ62i/Bdx3G2sM8Vlzp0+fxlNPPYX09HQkJCSgT58+KCgoQGNj4Lc2CiGwZMkSpKSkICEhAbm5uTh69KiqPtgtAnZL4DsHOB4hlYn7/0NOO8Ge6C2W/z8CUdRmnOT2CEZJ22raD7WePa6N10OJlrg2Xg81Qu2Hzzbj2/l8RIKIbxvwoQf2eEtYH3oU7jHqYZzBfrcicceBSDx3fD3H1d0ZILTXnFD6oOZ1NtRrRrivSbFq7dq1SE9Ph9VqRWZmJvbs2ROwfEVFBTIzM2G1WtG7d2+sX7/eq8zWrVsxYMAAxMfHY8CAASgpKYlU92UxxErtiRMnYLfbsWHDBvTt2xdHjhzBrFmzcP36daxYscJvveXLl2PVqlV45513cPfdd+OVV17BI488gurqanTs2FFVXzyDbZsm7ydQJLYpAPrciwvocz9uKPWA8K/mtp4/PCu6avrj1W6Ai3M4V3ddyQke0V7tDZUeAp/RafUHT6T+uAPCs/LqoNUKbOu59bcKq/YcsW7Lli3Iz8/H2rVr8cADD2DDhg0YM2YMjh07hp49e3qVr6mpwdixYzFr1ixs3rwZ//znP/Hss8+ie/fu+OlPfwoAqKysxNSpU/GHP/wBkyZNQklJCaZMmYJ//OMfGDp0aLSHCACQhBCGfH/gn/70J6xbtw5ff/21z58LIZCSkoL8/HwsXLgQAHD79m0kJiaisLAQzzzzjKzzXL16FTabDWmFr6Btm4SAZX0FXO8ywc8pq4zM++/JaQsIHHDVtulsW0GYVNO+mnOEWg8IPVi2nj/0NlyFo08B249Q4FXDaAE4VuhlVR6IbHAF9BVegegHWDX1IhlgPdtuabyFIxtfQkNDAzp16qTsxCFyZIcRbX+CdlJ4//htFk0oaylWNK6hQ4ciIyMD69atcx77wQ9+gIkTJ2LZsmVe5RcuXIht27bh+PHjzmOzZ8/Gv//9b1RWVgIApk6diqtXr+Kjjz5ylhk9ejQ6d+6Md999V+3wQmKIlVpfGhoa0KVLF78/r6mpQX19PfLy8pzH4uPjMXz4cOzdu9dvqL19+zZu377tdh4AsN+6BVhbj0mNvp9kjjgRKNw6ykiB9sU62gmQH5xlgoRbZ7lgq6EuX7cNklvktulZXvYKq8L23esoC6nN302VqnDrMs1qw2SzSxttm8IQSF2uaREJuB55RdOQGyA7SREO97FMBAtOzdrsW/YZYJvD+/vpFRpDbL7F4tKeyn82tz4pbMMtMCqo6/ZJXTIXWJyBU2Z5Z99klPfXdkvjLQCti1xaaUYTEObTN383WVevXnU7Hh8fj/j4eK/yjY2NOHDgABYtWuR2PC8vD3v37vV5jsrKSrf8BACjRo3Cpk2b0NTUBIvFgsrKSsybN8+rzOuvv650SGFjyFB76tQprFmzBitXrvRbpr6+HgCQmJjodjwxMRFnzpzxW2/ZsmVYunSp1/FvCl5R2VsiIiLSyqVLl2Cz2aJ6zri4OCQlJWFP/QcRab9Dhw5ITU11O1ZQUIAlS5Z4lb148SJaWlp85iFHVvJUX1/vs3xzczMuXryI5ORkv2X8tRkNmobaJUuW+AyQrvbt24fBgwc7v6+trcXo0aMxefJkPP3000HPIUnuq6ZCCK9jrl588UXMnz/f+f2VK1eQlpaGs2fPRv1JoaWrV68iNTUV33zzTdT/t42WOG6OOxZw3Bx3LGhoaEDPnj0D/l/dSLFaraipqQn6hna1fGUZX6u0rpTmIV/lPY8rbTPSNA21c+fOxbRp0wKW6dWrl/Pr2tpajBgxAtnZ2di4cWPAeklJSQBa/9pITk52Hr9w4YLXXxau/C3f22y2mHoxcOjUqRPHHUM47tjCcceWWB13mzba3OjJarXCarVqcm5X3bp1Q9u2bb1WUAPloaSkJJ/l27Vrh65duwYsEyhjRZqmt/Tq1q0b+vfvH/Dh+IU4f/48cnNzkZGRgaKioqC/pOnp6UhKSkJpaanzWGNjIyoqKpCTkxPRcRERERHpQVxcHDIzM93yEACUlpb6zUPZ2dle5Xfu3InBgwfDYrEELKNlxjLEfWpra2uRm5uL1NRUrFixAt9++y3q6+u9/kLo37+/8x5pkiQhPz8ff/zjH1FSUoIjR45g5syZaN++PR5//HEthkFEREQUdfPnz8df/vIXvP322zh+/DjmzZuHs2fPYvbs2QBat14++eSTzvKzZ8/GmTNnMH/+fBw/fhxvv/02Nm3ahBdeeMFZ5le/+hV27tyJwsJCnDhxAoWFhdi1axfy8/OjPTwnQ7xRbOfOnfjqq6/w1Vdf4a677nL7meu7Gqurq513KwCABQsW4ObNm3j22Wdx+fJlDB06FDt37lR0j9r4+HgUFBQE3atiNhw3xx0LOG6OOxZw3LE1bl+mTp2KS5cu4fe//z3q6uowcOBA7NixA2lpaQCAuro6nD171lk+PT0dO3bswLx58/DWW28hJSUFf/7zn533qAWAnJwcvPfee/jtb3+LxYsXo0+fPtiyZYtm96gFDHyfWiIiIiIiB0NsPyAiIiIiCoShloiIiIgMj6GWiIiIiAyPoZaIiIiIDI+h1sPp06fx1FNPIT09HQkJCejTpw8KCgqCfiqIEAJLlixBSkoKEhISkJubi6NHj0ap1+Hx6quvIicnB+3bt8edd94pq87MmTMhSZLbIysrK7IdDTM14zbDfF++fBnTp0+HzWaDzWbD9OnTceXKlYB1jDjfa9euRXp6OqxWKzIzM7Fnz56A5SsqKpCZmQmr1YrevXtj/fr1UeppeCkZd3l5ude8SpKEEydORLHHodu9ezfGjx+PlJQUSJKE999/P2gdM8y30nGbYb6XLVuGIUOGoGPHjujRowcmTpyI6urqoPXMMN/kH0OthxMnTsBut2PDhg04evQoVq9ejfXr1+Oll14KWG/58uVYtWoV3nzzTezbtw9JSUl45JFHcO3atSj1PHSNjY2YPHky5syZo6je6NGjUVdX53zs2LEjQj2MDDXjNsN8P/744/jiiy/w8ccf4+OPP8YXX3yB6dOnB61npPnesmUL8vPz8fLLL6OqqgoPPvggxowZ43brGlc1NTUYO3YsHnzwQVRVVeGll17C888/j61bt0a556FROm6H6upqt7n9/ve/H6Ueh8f169fxox/9CG+++aas8maZb6XjdjDyfFdUVOCXv/wlPvvsM5SWlqK5uRl5eXm4fv263zpmmW8KQFBQy5cvF+np6X5/brfbRVJSknjttdecx27duiVsNptYv359NLoYVkVFRcJms8kqO2PGDDFhwoSI9ida5I7bDPN97NgxAUB89tlnzmOVlZUCgDhx4oTfekab7/vvv1/Mnj3b7Vj//v3FokWLfJZfsGCB6N+/v9uxZ555RmRlZUWsj5GgdNxlZWUCgLh8+XIUehcdAERJSUnAMmaZb1dyxm3G+b5w4YIAICoqKvyWMeN8kzuu1MrQ0NCALl26+P15TU0N6uvrkZeX5zwWHx+P4cOHY+/evdHooqbKy8vRo0cP3H333Zg1axYuXLigdZciygzzXVlZCZvN5naT7KysLNhstqBjMMp8NzY24sCBA27zBAB5eXl+x1hZWelVftSoUdi/fz+ampoi1tdwUjNuh0GDBiE5ORkjR45EWVlZJLupC2aY71CYab4dH7wU6Fod6/MdCxhqgzh16hTWrFnj/Cg5Xxwf15uYmOh2PDEx0eujfM1mzJgx+Otf/4pPP/0UK1euxL59+/Dwww/j9u3bWnctYsww3/X19ejRo4fX8R49egQcg5Hm++LFi2hpaVE0T/X19T7LNzc34+LFixHrazipGXdycjI2btyIrVu3ori4GP369cPIkSOxe/fuaHRZM2aYbzXMNt9CCMyfPx/Dhg3DwIED/ZaL1fmOJTETapcsWeJzY7zrY//+/W51amtrMXr0aEyePBlPP/100HNIkuT2vRDC61i0qRm3ElOnTsW4ceMwcOBAjB8/Hh999BFOnjyJDz/8MIyjUC7S4waMP9+++hpsDHqd70CUzpOv8r6O652Scffr1w+zZs1CRkYGsrOzsXbtWowbNw4rVqyIRlc1ZZb5VsJs8z137lwcOnQI7777btCysTjfsaSd1h2Ilrlz52LatGkBy/Tq1cv5dW1tLUaMGIHs7Gxs3LgxYL2kpCQArX8FJicnO49fuHDB66/CaFM67lAlJycjLS0NX375ZdjaVCOS4zbDfB86dAj/+c9/vH727bffKhqDXubbl27duqFt27Zeq5OB5ikpKcln+Xbt2qFr164R62s4qRm3L1lZWdi8eXO4u6crZpjvcDHqfD/33HPYtm0bdu/ejbvuuitgWc63+cVMqO3WrRu6desmq+z58+cxYsQIZGZmoqioCG3aBF7QTk9PR1JSEkpLSzFo0CAArfvaKioqUFhYGHLfQ6Fk3OFw6dIlfPPNN25hTwuRHLcZ5js7OxsNDQ3417/+hfvvvx8A8Pnnn6OhoQE5OTmyz6eX+fYlLi4OmZmZKC0txaRJk5zHS0tLMWHCBJ91srOz8cEHH7gd27lzJwYPHgyLxRLR/oaLmnH7UlVVpct5DSczzHe4GG2+hRB47rnnUFJSgvLycqSnpwetw/mOAVq9Q02vzp8/L/r27Ssefvhhce7cOVFXV+d8uOrXr58oLi52fv/aa68Jm80miouLxeHDh8XPfvYzkZycLK5evRrtIah25swZUVVVJZYuXSo6dOggqqqqRFVVlbh27ZqzjOu4r127Jn7961+LvXv3ipqaGlFWViays7PF9773PVOPWwhzzPfo0aPFvffeKyorK0VlZaW45557xKOPPupWxujz/d577wmLxSI2bdokjh07JvLz88Udd9whTp8+LYQQYtGiRWL69OnO8l9//bVo3769mDdvnjh27JjYtGmTsFgs4m9/+5tWQ1BF6bhXr14tSkpKxMmTJ8WRI0fEokWLBACxdetWrYagyrVr15zPXwBi1apVoqqqSpw5c0YIYd75VjpuM8z3nDlzhM1mE+Xl5W7X6Rs3bjjLmHW+yT+GWg9FRUUCgM+HKwCiqKjI+b3dbhcFBQUiKSlJxMfHi4ceekgcPnw4yr0PzYwZM3yOu6yszFnGddw3btwQeXl5onv37sJisYiePXuKGTNmiLNnz2ozAJWUjlsIc8z3pUuXxBNPPCE6duwoOnbsKJ544gmvW/yYYb7feustkZaWJuLi4kRGRobbLX9mzJghhg8f7la+vLxcDBo0SMTFxYlevXqJdevWRbnH4aFk3IWFhaJPnz7CarWKzp07i2HDhokPP/xQg16HxnGrKs/HjBkzhBDmnW+l4zbDfPu7Tru+Tpt1vsk/SYjvdkkTERERERlUzNz9gIiIiIjMi6GWiIiIiAyPoZaIiIiIDI+hloiIiIgMj6GWiIiIiAyPoZaIiIiIDI+hloiIiIgMj6GWiIiIiAyPoZaIDKO8vBySJOHKlStad4WIiHSGoZaIdCs3Nxf5+flhb1eSJLz//vtha6+pqQkLFy7EPffcgzvuuAMpKSl48sknUVtbG7ZzEBFRYAy1REQhunHjBg4ePIjFixfj4MGDKC4uxsmTJ/HjH/9Y664REcUMhloi0qWZM2eioqICb7zxBiRJgiRJOH36NADgwIEDGDx4MNq3b4+cnBxUV1e71f3ggw+QmZkJq9WK3r17Y+nSpWhubgYA9OrVCwAwadIkSJLk/P7UqVOYMGECEhMT0aFDBwwZMgS7du2S1VebzYbS0lJMmTIF/fr1Q1ZWFtasWYMDBw7g7NmzYfn3ICKiwBhqiUiX3njjDWRnZ2PWrFmoq6tDXV0dUlNTAQAvv/wyVq5cif3796Ndu3b4xS9+4az3ySef4Oc//zmef/55HDt2DBs2bMA777yDV199FQCwb98+AEBRURHq6uqc3//3v//F2LFjsWvXLlRVVWHUqFEYP3686lDa0NAASZJw5513hvCvQEREcklCCKF1J4iIfMnNzcV9992H119/HUDrG8VGjBiBXbt2YeTIkQCAHTt2YNy4cbh58yasViseeughjBkzBi+++KKznc2bN2PBggXOPa6SJKGkpAQTJ04MeP4f/vCHmDNnDubOnauo37du3cKwYcPQv39/bN68WVFdIiJSp53WHSAiUuree+91fp2cnAwAuHDhAnr27IkDBw5g3759zpVZAGhpacGtW7dw48YNtG/f3meb169fx9KlS7F9+3bU1taiubkZN2/eVLxS29TUhGnTpsFut2Pt2rUqRkdERGow1BKR4VgsFufXkiQBAOx2u/O/S5cuxU9+8hOvelar1W+bv/nNb/DJJ59gxYoV6Nu3LxISEvDYY4+hsbFRdr+ampowZcoU1NTU4NNPP0WnTp1k1yUiotAw1BKRbsXFxaGlpUVRnYyMDFRXV6Nv375+y1gsFq929+zZg5kzZ2LSpEkAWvfYOt6YJocj0H755ZcoKytD165dFfWbiIhCw1BLRLrVq1cvfP755zh9+jQ6dOjgXI0N5He/+x0effRRpKamYvLkyWjTpg0OHTqEw4cP45VXXnG2+/e//x0PPPAA4uPj0blzZ/Tt2xfFxcUYP348JEnC4sWLZZ0PAJqbm/HYY4/h4MGD2L59O1paWlBfXw8A6NKlC+Li4tT/IxARkSy8+wER6dYLL7yAtm3bYsCAAejevbus/a2jRo3C9u3bUVpaiiFDhiArKwurVq1CWlqas8zKlStRWlqK1NRUDBo0CACwevVqdO7cGTk5ORg/fjxGjRqFjIwMWf08d+4ctm3bhnPnzuG+++5DcnKy87F37151gyciIkV49wMiIiIiMjyu1BIRERGR4THUEhEFsWfPHnTo0MHvg4iItMftB0REQdy8eRPnz5/3+/NAd1ogIqLoYKglIiIiIsPj9gMiIiIiMjyGWiIiIiIyPIZaIiIiIjI8hloiIiIiMjyGWiIiIiIyPIZaIiIiIjI8hloiIiIiMrz/Adu/tIK90B4XAAAAAElFTkSuQmCC", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Analyze the objective space by evaluating the sum of squared errors (SSE)\n", - "# over a grid of theta_1 and theta_2 values\n", - "theta1_range = np.linspace(-2, 2, 200)\n", - "theta2_range = np.linspace(-2, 2, 200)\n", - "sse_grid = np.zeros((len(theta1_range), len(theta2_range)))\n", - "\n", - "# Use the model function from cell 5\n", - "for i, t1 in enumerate(theta1_range):\n", - " for j, t2 in enumerate(theta2_range):\n", - " y_sim = np.array([model(x, t1, t2) for x in conc])\n", - " sse = np.sum((vel - y_sim) ** 2)\n", - " sse_grid[i, j] = sse\n", - "\n", - "# Plot the objective space\n", - "plt.figure(figsize=(8, 6))\n", - "X, Y = np.meshgrid(theta2_range, theta1_range)\n", - "cp = plt.contourf(X, Y, sse_grid, levels=50, cmap='viridis')\n", - "plt.colorbar(cp, label='Sum of Squared Errors (SSE)')\n", - "plt.xlabel('theta_2')\n", - "plt.ylabel('theta_1')\n", - "plt.title('Objective Space: SSE over theta_1 and theta_2')\n", - "# Optionally, mark the minimum SSE location on the grid\n", - "min_idx = np.unravel_index(np.argmin(sse_grid), sse_grid.shape)\n", - "plt.scatter([theta2_range[min_idx[1]]], [theta1_range[min_idx[0]]], color='red', label='Grid Minimum')\n", - "plt.scatter([true_params['theta2']], [true_params['theta1']], color='white', marker='*', s=100, label='True Params')\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "5eb58543", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Plot the effect of varying each parameter (theta_1 and theta_2) one at a time\n", - "\n", - "fig, axes = plt.subplots(1, 2, figsize=(14, 5), sharey=True)\n", - "\n", - "# Vary theta_1, fix theta_2 at true value\n", - "theta1_vals = theta1_range\n", - "theta2_fixed = true_params[\"theta2\"]\n", - "sse_theta1 = []\n", - "for t1 in theta1_vals:\n", - " y_sim = np.array([model(x, t1, theta2_fixed) for x in conc])\n", - " sse = np.sum((vel - y_sim) ** 2)\n", - " sse_theta1.append(sse)\n", - "axes[0].plot(theta1_vals, sse_theta1, label=f'Fixed theta_2={theta2_fixed:.3f}')\n", - "axes[0].axvline(true_params['theta1'], color='green', linestyle='--', label='True theta_1')\n", - "axes[0].set_xlabel('theta_1')\n", - "axes[0].set_ylabel('Sum of Squared Errors (SSE)')\n", - "axes[0].set_title('SSE vs theta_1 (theta_2 fixed)')\n", - "axes[0].legend()\n", - "\n", - "# Vary theta_2, fix theta_1 at true value\n", - "theta2_vals = theta2_range\n", - "theta1_fixed = true_params[\"theta1\"]\n", - "sse_theta2 = []\n", - "for t2 in theta2_vals:\n", - " y_sim = np.array([model(x, theta1_fixed, t2) for x in conc])\n", - " sse = np.sum((vel - y_sim) ** 2)\n", - " sse_theta2.append(sse)\n", - "axes[1].plot(theta2_vals, sse_theta2, label=f'Fixed theta_1={theta1_fixed:.3f}')\n", - "axes[1].axvline(true_params['theta2'], color='green', linestyle='--', label='True theta_2')\n", - "axes[1].set_xlabel('theta_2')\n", - "axes[1].set_title('SSE vs theta_2 (theta_1 fixed)')\n", - "axes[1].legend()\n", - "\n", - "plt.tight_layout()\n", - "plt.show()\n" - ] - }, - { - "cell_type": "markdown", - "id": "be805271-1f41-4066-ad50-f912ba61fa53", - "metadata": {}, - "source": [ - "# PARMEST" - ] - }, - { - "cell_type": "markdown", - "id": "2b95ecb7-a1c7-4b09-b32e-dafe3fca3944", - "metadata": {}, - "source": [ - "## Creating an experiment class for both DOE and ParmEst\n", - "We will use this class for both parmest (to estimate the parameters) and DOE (To calculate the FIM and factorial design)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "a5e84eb1-94a6-40ec-a940-2306778a4f71", - "metadata": {}, - "outputs": [], - "source": [ - "class Simple_Multimodal(experiment.Experiment):\n", - "\n", - " # Defining the constructor for our model\n", - " def __init__(self, data, theta_initial=None):\n", - " \"\"\"\n", - " Arguments:\n", - " data: data from our experiment. type: 'dict'\n", - " theta_initial: initial guess of the parameter values, dtype: dict. pass the values as theta_initial = {1 : , 2 : }\n", - " default: {1: 100, 2: 0.05}\n", - " \n", - " \"\"\"\n", - " self.conc = data[\"x\"]\n", - " self.vel = data[\"y\"]\n", - " self.model = None \n", - " self.theta_initial = theta_initial\n", - " if self.theta_initial is None:\n", - " self.theta_initial = {1: -1.5, 2: 0.5} # default initial guess of theta[1] & theta[2]\n", - " else:\n", - " self.theta_initial = theta_initial\n", - "\n", - " # Creating the get_labeled_model which is a must for ``DOE`` and ``ParmEst``\n", - " def get_labeled_model(self):\n", - " if self.model is None:\n", - " self.create_model()\n", - " self.label_model()\n", - " self.finalize_model()\n", - " return self.model\n", - "\n", - " \n", - " def create_model(self):\n", - " \"\"\"\n", - " Here, we will create different variables, parameters, and constraints.\n", - " The index set for data points will be the actual substrate concentrations (x values).\n", - " \"\"\"\n", - " m = self.model = pyo.ConcreteModel()\n", - "\n", - " # theta_1 and theta_2 as parameters to be estimated\n", - " m.theta_1 = pyo.Var(initialize=self.theta_initial[1], bounds=(-2, 2))\n", - " m.theta_2 = pyo.Var(initialize=self.theta_initial[2], bounds=(-2, 2))\n", - "\n", - " # Use the actual conc values as the index set\n", - " m.x_set = pyo.Set(initialize=[float(val) for val in self.conc], ordered=True)\n", - "\n", - " # Substrate concentration (x) as a parameter, indexed by x_set\n", - " m.x = pyo.Param(m.x_set, initialize={float(val): float(val) for val in self.conc}, mutable=False)\n", - "\n", - " # Measured variable for each data point (indexed by x_set)\n", - " m.measured_var = pyo.Var(m.x_set)\n", - "\n", - " # Constraint for each data point\n", - " def meas_con(m, xval):\n", - " return m.measured_var[xval] == (m.theta_1 * m.x[xval]**3 - m.theta_2 * m.x[xval]**2 + 2 * m.x[xval] - 1)**2 + (m.theta_1 - m.theta_2)**2 + (m.x[xval]**2 - 1)**2\n", - " m.meas_con = pyo.Constraint(m.x_set, rule=meas_con)\n", - "\n", - " # Objective function: sum of squared errors over all data points\n", - " m.FirstStageCost = pyo.Expression(initialize=0)\n", - " m.SecondStageCost = pyo.Expression(expr=sum((self.vel[i] - m.measured_var[float(self.conc[i])]) ** 2 for i in range(len(self.conc))))\n", - " m.Total_Cost_Obj = pyo.Objective(expr=(m.FirstStageCost + m.SecondStageCost), sense=pyo.minimize)\n", - "\n", - " return m\n", - "\n", - " \n", - " def finalize_model(self):\n", - " \"\"\"\n", - " Finalizing the model. Here, we will set the experimental conditions (e.g, initial conditions),\n", - " fixing the parameter values (if needed), update `t` values, and discretize the model (if model is dynamic). \n", - " It makes a solvable model.\n", - " \"\"\" \n", - " m=self.model\n", - "\n", - " # fixing the parameters\n", - " m.theta_1.fix(self.theta_initial[1]) \n", - " m.theta_2.fix(self.theta_initial[2]) \n", - "\n", - "\n", - " return m\n", - "\n", - " \n", - " def label_model(self):\n", - " \"\"\"\n", - " The model is updated with outputs, and unknown parameters. This makes the model labeled with full experiment.\n", - " In `ParmEst` output (given data) is the most important. For `DOE` input is most important.\n", - " \"\"\"\n", - " m = self.model\n", - "\n", - " m.experiment_outputs = pyo.Suffix(direction = pyo.Suffix.LOCAL) \n", - " m.experiment_outputs.update([(m.measured_var, self.vel)]) # Pass the data as a list of `tuple`\n", - " # If we only use ``DOE``, we could use ``m.experiment_ouputs.update([(m.x, None)])``.\n", - " # Output is not important for ``DOE``\n", - "\n", - " # m.experiment_inputs = pyo.Suffix(direction = pyo.Suffix.LOCAL) \n", - " # # m.experiment_inputs[m.x] = self.conc\n", - " # m.experiment_inputs.update([(m.x, self.conc)])\n", - " # # If we only use ``DOE``, we could use ``m.experiment_inputs.update([(m.x, None)])``\n", - "\n", - " m.unknown_parameters = pyo.Suffix(direction = pyo.Suffix.LOCAL)\n", - " m.unknown_parameters.update((p, pyo.value(p)) for p in [m.theta_1, m.theta_2])\n", - " # m.unknown_parameters[m.theta_1]= self.theta_initial[1]\n", - " # m.unknown_parameters[m.theta_2]= self.theta_initial[2]\n", - "\n", - " m.measurement_error = pyo.Suffix(direction = pyo.Suffix.LOCAL)\n", - " # m.measurement_error[m.measured_var] = 0.03\n", - " m.measurement_error.update([(m.measured_var, 0.03**2)]) # variance of the measurement error, b/c in doe, the measurement error is passed as variance, not std\n", - " # this will be fixed in later update \n", - "\n", - " return m" - ] - }, - { - "cell_type": "markdown", - "id": "c303c406-ac5a-4ff6-9535-48967393acc4", - "metadata": {}, - "source": [ - "## Parmest Example\n", - "We will evaluate diffent parameters, visualize pairwise plots and show bootstrap table" - ] - }, - { - "cell_type": "markdown", - "id": "32d11b62-0040-4a65-b4f7-035730272094", - "metadata": {}, - "source": [ - "### Treated velocity\n", - "Here, we will calculate objective value at optimum parameter value, paramater values, $\\theta$, and covariance matrix for treated velocity." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "09b0eae6-e1d1-404c-be2a-98a1a8e776bf", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ipopt 3.13.2: bound_relax_factor=0\n", - "honor_original_bounds=no\n", - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", - " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", - " 3 4.2816330e+05 4.18e-12 2.10e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", - " 4 4.2816330e+05 4.18e-12 1.51e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", - " 5 4.2816330e+05 5.09e-12 2.77e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", - " 6 4.2816330e+05 3.50e-12 1.50e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 6\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", - "Dual infeasibility......: 1.4985094187954244e-12 2.8984606156203206e-10\n", - "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", - "Complementarity.........: 2.5059035597864302e-09 4.8469917395796542e-07\n", - "Overall NLP error.......: 2.5059035597864302e-09 4.8469917395796542e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 7\n", - "Number of objective gradient evaluations = 7\n", - "Number of equality constraint evaluations = 7\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 7\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 6\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", - "\n", - "theta values:\n", - "theta_1 -1.500300\n", - "theta_2 0.501315\n", - "dtype: float64\n", - "\n", - "SSE value: 428163.2965187811\n", - "\n", - "Covariance matrix:\n", - " theta_1 theta_2\n", - "theta_1 4.667923e-08 3.978519e-08\n", - "theta_2 3.978519e-08 6.376757e-07\n" - ] - } - ], - "source": [ - "exp_list = [] \n", - "conc = data_df[\"x\"].values # substrate concentration (control variable)\n", - "vel = data_df[\"y\"].values # reaction velocity (output variable)\n", - "n_exp = 1\n", - "\n", - "# exp_list to separate each experiment\n", - "# for i in range(n_exp):\n", - "exp_list.append(Simple_Multimodal(data_df))\n", - "\n", - "# Creating an Estimator object\n", - "pest = parmest.Estimator(exp_list, tee = True) \n", - "\n", - "# Estimating d\n", - "obj, theta, cov = pest.theta_est(calc_cov=True, cov_n=len(conc),)\n", - "\n", - "# ``parmest`` can mess up the order of the theta estimates and also the covariance. So we need to fix the order so that we can manipulate it properly.\n", - "t_order = [\"theta_1\", \"theta_2\"]\n", - "theta_perm = theta.loc[t_order] # ``theta`` in the order we want\n", - "cov_perm = cov.loc[t_order, t_order] # ``covariance`` matrix in the order we want\n", - "\n", - "print(\"\\ntheta values:\")\n", - "print(theta_perm)\n", - "\n", - "print(\"\\nSSE value: \", obj)\n", - "\n", - "print(\"\\nCovariance matrix:\")\n", - "print(cov_perm)" - ] - }, - { - "cell_type": "markdown", - "id": "fe378b08", - "metadata": {}, - "source": [ - "### Manual multistart test\n", - "\n", - "Made a for loop version of multistart, to see expected results from solver being accessed each time. Sobol sampling, n = 50, seed = 12345" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "5a2cc375", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ipopt 3.13.2: bound_relax_factor=0\n", - "honor_original_bounds=no\n", - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 8.33e+03 9.73e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 1.9834030e+07 2.32e+03 1.15e+05 -1.0 9.25e+03 - 2.54e-01 1.00e+00f 1\n", - " 2 2.6443991e+07 1.71e+02 1.35e+04 -1.0 9.35e+01 - 8.83e-01 1.00e+00h 1\n", - " 3 2.7180925e+07 1.23e+00 3.23e+02 -1.0 1.06e+01 - 1.00e+00 1.00e+00h 1\n", - " 4 2.7186293e+07 5.91e-05 4.09e-02 -1.0 7.54e-02 - 1.00e+00 1.00e+00h 1\n", - " 5 2.7186294e+07 3.89e-12 1.48e-10 -2.5 1.72e-05 - 1.00e+00 1.00e+00h 1\n", - " 6 2.7186294e+07 5.57e-12 6.31e-10 -3.8 5.65e-07 - 1.00e+00 1.00e+00h 1\n", - " 7 2.7186294e+07 4.69e-12 1.10e-09 -5.7 3.14e-08 - 1.00e+00 1.00e+00h 1\n", - " 8 2.7186294e+07 4.12e-12 7.61e-10 -8.6 3.90e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 8\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 1.4055363392027403e+05 2.7186293738994990e+07\n", - "Dual infeasibility......: 7.6089379903002177e-10 1.4717429743825483e-07\n", - "Constraint violation....: 7.7375360819269268e-14 4.1211478674085811e-12\n", - "Complementarity.........: 2.5059035597865472e-09 4.8469917395798797e-07\n", - "Overall NLP error.......: 2.5059035597865472e-09 4.8469917395798797e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 9\n", - "Number of objective gradient evaluations = 9\n", - "Number of equality constraint evaluations = 9\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 9\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 8\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.005\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Applications/anaconda3/envs/parmest-dev-mac2/lib/python3.13/site-packages/scipy/stats/_qmc.py:993: UserWarning: The balance properties of Sobol' points require n to be a power of 2.\n", - " sample = self._random(n, workers=workers)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ipopt 3.13.2: bound_relax_factor=0\n", - "honor_original_bounds=no\n", - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 2.15e+04 9.85e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 9.7588988e+06 2.78e+03 5.57e+04 -1.0 9.41e+03 - 1.46e-02 1.00e+00f 1\n", - " 2 2.6386162e+07 2.49e+02 1.19e+04 -1.0 2.49e+02 - 9.29e-01 1.00e+00h 1\n", - " 3 2.7175624e+07 2.55e+00 5.81e+02 -1.0 1.51e+01 - 1.00e+00 1.00e+00h 1\n", - " 4 2.7186293e+07 2.61e-04 1.56e-01 -1.0 1.51e-01 - 1.00e+00 1.00e+00h 1\n", - " 5 2.7186294e+07 5.40e-12 3.64e-09 -2.5 3.02e-05 - 1.00e+00 1.00e+00h 1\n", - " 6 2.7186294e+07 4.41e-12 1.03e-09 -3.8 5.65e-07 - 1.00e+00 1.00e+00h 1\n", - " 7 2.7186294e+07 4.69e-12 1.23e-09 -5.7 3.14e-08 - 1.00e+00 1.00e+00h 1\n", - " 8 2.7186294e+07 4.12e-12 5.64e-10 -8.6 3.90e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 8\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 1.4055363392027401e+05 2.7186293738994986e+07\n", - "Dual infeasibility......: 5.6444294344696339e-10 1.0917620008427717e-07\n", - "Constraint violation....: 6.8047436072523522e-14 4.1211478674085811e-12\n", - "Complementarity.........: 2.5059035597865472e-09 4.8469917395798797e-07\n", - "Overall NLP error.......: 2.5059035597865472e-09 4.8469917395798797e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 9\n", - "Number of objective gradient evaluations = 9\n", - "Number of equality constraint evaluations = 9\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 9\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 8\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.005\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", - "Ipopt 3.13.2: bound_relax_factor=0\n", - "honor_original_bounds=no\n", - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 1.10e+03 1.06e+02 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 2.2271200e+09 1.06e+04 5.22e+06 -1.0 1.41e+04 - 1.55e-01 2.75e-01f 1\n", - " 2 4.9620454e+06 1.15e+03 1.42e+06 -1.0 6.05e+03 - 1.49e-02 1.00e+00f 1\n", - " 3 4.3909756e+05 6.85e+01 8.18e+04 -1.0 3.03e+02 - 9.74e-01 1.00e+00f 1\n", - " 4 4.2816904e+05 2.93e-01 4.24e+02 -1.0 1.76e+01 - 1.00e+00 1.00e+00f 1\n", - " 5 4.2816330e+05 6.80e-06 1.39e-02 -1.0 1.19e-01 - 1.00e+00 1.00e+00h 1\n", - " 6 4.2816330e+05 5.03e-12 1.55e-10 -2.5 2.53e-05 - 1.00e+00 1.00e+00h 1\n", - " 7 4.2816330e+05 4.18e-12 4.43e-12 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", - " 8 4.2816330e+05 5.09e-12 2.61e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", - " 9 4.2816330e+05 3.50e-12 6.22e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 9\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 2.2136120434349154e+03 4.2816329651878146e+05\n", - "Dual infeasibility......: 6.2153811273360234e-12 1.2021971422198111e-09\n", - "Constraint violation....: 3.4631683699249438e-13 3.4958702599396929e-12\n", - "Complementarity.........: 2.5059035597864302e-09 4.8469917395796542e-07\n", - "Overall NLP error.......: 2.5059035597864302e-09 4.8469917395796542e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 10\n", - "Number of objective gradient evaluations = 10\n", - "Number of equality constraint evaluations = 10\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 10\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 9\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.005\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", - "Ipopt 3.13.2: bound_relax_factor=0\n", - "honor_original_bounds=no\n", - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 1.07e+03 8.68e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 4.4414889e+09 1.02e+03 2.34e+05 -1.0 1.25e+04 - 2.26e-01 5.25e-02f 1\n", - " 2 4.4306624e+09 1.02e+03 2.34e+05 -1.0 7.54e+03 - 9.36e-01 1.31e-03f 1\n", - " 3 4.4294973e+09 1.02e+03 6.00e+06 -1.0 3.07e+03 - 1.00e+00 6.02e-04f 1\n", - " 4 3.0552690e+09 9.12e+02 1.82e+07 -1.0 2.98e+03 - 1.00e+00 9.96e-01f 1\n", - " 5 1.5613347e+09 1.89e+03 3.20e+07 -1.0 6.63e+03 -2.0 4.47e-01 1.00e+00f 1\n", - " 6 1.0074442e+09 2.86e+01 4.84e+06 -1.0 1.43e+03 - 1.37e-02 1.00e+00f 1\n", - " 7 9.7365090e+08 1.54e+00 4.43e+06 -1.0 2.29e+02 - 1.36e-04 1.00e+00f 1\n", - " 8 1.4779113e+08 2.15e+03 2.83e+07 -1.0 3.47e+03 -1.6 1.90e-02 1.00e+00f 1\n", - " 9 2.9134338e+06 2.92e+01 1.03e+06 -1.0 1.61e+03 - 1.00e+00 1.00e+00f 1\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 10 4.2930909e+05 2.52e+00 2.09e+04 -1.0 2.46e+02 - 1.00e+00 1.00e+00f 1\n", - " 11 4.2816335e+05 2.53e-03 1.42e+01 -1.0 5.43e+00 - 1.00e+00 1.00e+00f 1\n", - " 12 4.2816330e+05 1.59e-09 7.66e-06 -1.0 3.76e-03 - 1.00e+00 1.00e+00h 1\n", - " 13 4.2816330e+05 3.21e-12 2.17e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", - " 14 4.2816330e+05 4.18e-12 2.30e-03 -3.8 7.13e-07 - 1.00e+00 5.00e-01h 2\n", - " 15 4.2816330e+05 4.01e-12 1.68e-11 -3.8 3.56e-07 - 1.00e+00 1.00e+00h 1\n", - " 16 4.2816330e+05 5.09e-12 2.77e-12 -5.7 3.95e-08 - 1.00e+00 1.00e+00h 1\n", - " 17 4.2816330e+05 3.50e-12 9.85e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 17\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", - "Dual infeasibility......: 9.8533599348512529e-12 1.9058656118194543e-09\n", - "Constraint violation....: 2.9162136132700857e-13 3.4958702599396929e-12\n", - "Complementarity.........: 2.5059035597866419e-09 4.8469917395800629e-07\n", - "Overall NLP error.......: 2.5059035597866419e-09 4.8469917395800629e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 20\n", - "Number of objective gradient evaluations = 18\n", - "Number of equality constraint evaluations = 20\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 18\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 17\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.010\n", - "Total CPU secs in NLP function evaluations = 0.002\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", - "Ipopt 3.13.2: bound_relax_factor=0\n", - "honor_original_bounds=no\n", - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 9.72e+03 9.63e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 2.6711997e+07 4.94e+01 4.67e+03 -1.0 9.15e+03 - 8.25e-01 1.00e+00f 1\n", - " 2 2.7185876e+07 1.30e-01 3.12e+01 -1.0 5.38e+00 - 1.00e+00 1.00e+00h 1\n", - " 3 2.7186294e+07 8.26e-07 2.97e-04 -1.0 7.99e-03 - 1.00e+00 1.00e+00h 1\n", - " 4 2.7186294e+07 3.41e-12 2.29e-10 -2.5 2.05e-05 - 1.00e+00 1.00e+00h 1\n", - " 5 2.7186294e+07 4.41e-12 4.78e-10 -3.8 5.65e-07 - 1.00e+00 1.00e+00h 1\n", - " 6 2.7186294e+07 3.67e-12 9.05e-10 -5.7 3.14e-08 - 1.00e+00 1.00e+00h 1\n", - " 7 2.7186294e+07 4.12e-12 2.65e-10 -8.6 3.89e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 7\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 1.4055363392027403e+05 2.7186293738994990e+07\n", - "Dual infeasibility......: 2.6501622456984170e-10 5.1260210965743845e-08\n", - "Constraint violation....: 4.6417226757976225e-14 4.1211478674085811e-12\n", - "Complementarity.........: 2.5059035597868338e-09 4.8469917395804345e-07\n", - "Overall NLP error.......: 2.5059035597868338e-09 4.8469917395804345e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 8\n", - "Number of objective gradient evaluations = 8\n", - "Number of equality constraint evaluations = 8\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 8\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 7\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", - "Ipopt 3.13.2: bound_relax_factor=0\n", - "honor_original_bounds=no\n", - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 2.37e+03 9.51e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 5.2544727e+08 1.26e+04 4.35e+05 -1.0 9.02e+03 - 2.75e-01 6.86e-01f 1\n", - " 2 2.2794582e+07 1.25e+03 9.75e+04 -1.0 2.97e+03 - 2.39e-02 1.00e+00f 1\n", - " 3 2.7074814e+07 3.89e+01 6.86e+03 -1.0 5.61e+01 - 1.00e+00 1.00e+00h 1\n", - " 4 2.7186179e+07 4.02e-02 1.85e+01 -1.0 1.74e+00 - 1.00e+00 1.00e+00h 1\n", - " 5 2.7186294e+07 3.87e-08 4.24e-05 -1.0 3.08e-03 - 1.00e+00 1.00e+00h 1\n", - " 6 2.7186294e+07 4.58e-12 3.65e-10 -2.5 2.05e-05 - 1.00e+00 1.00e+00h 1\n", - " 7 2.7186294e+07 4.41e-12 3.77e-10 -3.8 5.65e-07 - 1.00e+00 1.00e+00h 1\n", - " 8 2.7186294e+07 4.21e-12 9.78e-10 -5.7 3.14e-08 - 1.00e+00 1.00e+00h 1\n", - " 9 2.7186294e+07 4.12e-12 7.89e-10 -8.6 3.89e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 9\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 1.4055363392027401e+05 2.7186293738994986e+07\n", - "Dual infeasibility......: 7.8853941762349164e-10 1.5252159360353654e-07\n", - "Constraint violation....: 1.8412683274148882e-13 4.1211478674085811e-12\n", - "Complementarity.........: 2.5059035597868338e-09 4.8469917395804345e-07\n", - "Overall NLP error.......: 2.5059035597868338e-09 4.8469917395804345e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 10\n", - "Number of objective gradient evaluations = 10\n", - "Number of equality constraint evaluations = 10\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 10\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 9\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.005\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", - "Ipopt 3.13.2: bound_relax_factor=0\n", - "honor_original_bounds=no\n", - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 8.36e+03 1.01e+02 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 3.6567139e+06 2.70e+03 6.69e+04 -1.0 9.84e+03 - 3.42e-01 1.00e+00f 1\n", - " 2 4.3352737e+05 1.69e+02 6.65e+03 -1.0 1.86e+02 - 8.09e-01 1.00e+00f 1\n", - " 3 4.2816873e+05 8.05e-01 4.30e+01 -1.0 7.01e+00 - 1.00e+00 1.00e+00f 1\n", - " 4 4.2816330e+05 1.82e-05 3.52e-03 -1.0 3.07e-02 - 1.00e+00 1.00e+00h 1\n", - " 5 4.2816330e+05 3.69e-12 7.46e-11 -2.5 2.58e-05 - 1.00e+00 1.00e+00h 1\n", - " 6 4.2816330e+05 4.18e-12 7.82e-12 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", - " 7 4.2816330e+05 5.09e-12 1.10e-11 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", - " 8 4.2816330e+05 3.50e-12 2.88e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 8\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 2.2136120434349154e+03 4.2816329651878146e+05\n", - "Dual infeasibility......: 2.8795658903932589e-12 5.5697403157450258e-10\n", - "Constraint violation....: 9.8520816457861569e-14 3.4958702599396929e-12\n", - "Complementarity.........: 2.5059035597864302e-09 4.8469917395796542e-07\n", - "Overall NLP error.......: 2.5059035597864302e-09 4.8469917395796542e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 9\n", - "Number of objective gradient evaluations = 9\n", - "Number of equality constraint evaluations = 9\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 9\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 8\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.005\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", - "Ipopt 3.13.2: bound_relax_factor=0\n", - "honor_original_bounds=no\n", - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 8.43e+03 1.01e+02 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 1.1035343e+07 5.64e+03 1.01e+06 -1.0 9.86e+03 - 1.52e-01 1.00e+00f 1\n", - " 2 7.7062599e+05 7.40e+02 1.98e+05 -1.0 4.05e+02 - 6.58e-01 1.00e+00f 1\n", - " 3 4.2910167e+05 2.42e+01 8.77e+03 -1.0 8.67e+01 - 1.00e+00 1.00e+00f 1\n", - " 4 4.2816393e+05 3.08e-02 1.65e+01 -1.0 3.60e+00 - 1.00e+00 1.00e+00h 1\n", - " 5 4.2816330e+05 5.42e-08 4.48e-05 -1.0 5.94e-03 - 1.00e+00 1.00e+00h 1\n", - " 6 4.2816330e+05 3.89e-12 2.24e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", - " 7 4.2816330e+05 5.31e-12 1.87e-12 -3.8 7.13e-07 - 1.00e+00 1.00e+00H 1\n", - " 8 4.2816330e+05 3.04e-12 1.45e-11 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", - " 9 4.2816330e+05 3.50e-12 2.62e-11 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 9\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", - "Dual infeasibility......: 2.6224264566340445e-11 5.0723737245673013e-09\n", - "Constraint violation....: 1.6351447811689176e-13 3.4958702599396925e-12\n", - "Complementarity.........: 2.5059035597864302e-09 4.8469917395796542e-07\n", - "Overall NLP error.......: 2.5059035597864302e-09 4.8469917395796542e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 11\n", - "Number of objective gradient evaluations = 10\n", - "Number of equality constraint evaluations = 11\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 10\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 9\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.005\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", - "Ipopt 3.13.2: bound_relax_factor=0\n", - "honor_original_bounds=no\n", - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 8.08e+02 9.23e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 3.5436370e+08 1.58e+04 3.23e+06 -1.0 9.13e+03 - 4.82e-01 7.24e-01f 1\n", - " 2 3.0476400e+07 1.98e+03 6.74e+05 -1.0 2.56e+03 - 3.12e-02 1.00e+00f 1\n", - " 3 2.7331790e+07 9.12e+01 5.74e+04 -1.0 5.85e+01 - 1.00e+00 1.00e+00f 1\n", - " 4 2.7186712e+07 2.48e-01 3.36e+02 -1.0 7.25e+00 - 1.00e+00 1.00e+00f 1\n", - " 5 2.7186294e+07 3.56e-06 7.20e-03 -1.0 4.95e-02 - 1.00e+00 1.00e+00f 1\n", - " 6 2.7186294e+07 3.58e-12 1.45e-10 -2.5 2.01e-05 - 1.00e+00 1.00e+00h 1\n", - " 7 2.7186294e+07 4.41e-12 2.03e-10 -3.8 5.65e-07 - 1.00e+00 1.00e+00h 1\n", - " 8 2.7186294e+07 4.69e-12 2.62e-10 -5.7 3.14e-08 - 1.00e+00 1.00e+00h 1\n", - " 9 2.7186294e+07 4.12e-12 7.32e-10 -8.6 3.90e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 9\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 1.4055363392027401e+05 2.7186293738994986e+07\n", - "Dual infeasibility......: 7.3178996857371158e-10 1.4154494968219492e-07\n", - "Constraint violation....: 1.6008713042790100e-13 4.1211478674085811e-12\n", - "Complementarity.........: 2.5059035597865472e-09 4.8469917395798797e-07\n", - "Overall NLP error.......: 2.5059035597865472e-09 4.8469917395798797e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 10\n", - "Number of objective gradient evaluations = 10\n", - "Number of equality constraint evaluations = 10\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 10\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 9\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.005\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", - "Ipopt 3.13.2: bound_relax_factor=0\n", - "honor_original_bounds=no\n", - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 1.21e+04 9.67e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 2.3904826e+07 7.92e+02 1.65e+04 -1.0 9.20e+03 - 6.85e-01 1.00e+00f 1\n", - " 2 2.7078421e+07 2.47e+01 2.40e+03 -1.0 4.93e+01 - 1.00e+00 1.00e+00h 1\n", - " 3 2.7186180e+07 2.61e-02 8.33e+00 -1.0 1.57e+00 - 1.00e+00 1.00e+00h 1\n", - " 4 2.7186294e+07 2.61e-08 2.15e-05 -1.0 1.65e-03 - 1.00e+00 1.00e+00h 1\n", - " 5 2.7186294e+07 3.58e-12 2.84e-10 -2.5 2.05e-05 - 1.00e+00 1.00e+00h 1\n", - " 6 2.7186294e+07 4.41e-12 8.12e-10 -3.8 5.65e-07 - 1.00e+00 1.00e+00h 1\n", - " 7 2.7186294e+07 4.69e-12 1.17e-10 -5.7 3.14e-08 - 1.00e+00 1.00e+00h 1\n", - " 8 2.7186294e+07 4.12e-12 6.44e-10 -8.6 3.90e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 8\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 1.4055363392027401e+05 2.7186293738994986e+07\n", - "Dual infeasibility......: 6.4447847720351047e-10 1.2465690641376951e-07\n", - "Constraint violation....: 5.4622972811557702e-14 4.1211478674085811e-12\n", - "Complementarity.........: 2.5059035597865472e-09 4.8469917395798797e-07\n", - "Overall NLP error.......: 2.5059035597865472e-09 4.8469917395798797e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 9\n", - "Number of objective gradient evaluations = 9\n", - "Number of equality constraint evaluations = 9\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 9\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 8\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.005\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", - "Ipopt 3.13.2: bound_relax_factor=0\n", - "honor_original_bounds=no\n", - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 2.11e+03 1.00e+02 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 4.8316028e+09 2.06e+03 8.87e+03 -1.0 6.09e+03 - 3.06e-01 2.48e-02f 1\n", - " 2 4.8286802e+09 2.06e+03 2.41e+04 -1.0 6.72e+03 - 6.43e-01 3.26e-04f 1\n", - " 3 4.8221153e+09 2.05e+03 5.20e+06 -1.0 1.77e+03 - 1.00e+00 4.84e-03f 1\n", - " 4 3.6291264e+09 1.18e+01 2.25e+06 -1.0 1.75e+03 - 1.00e+00 1.00e+00f 1\n", - " 5 3.6164199e+09 1.93e-02 1.33e+04 -1.0 1.77e+01 0.0 1.00e+00 1.00e+00f 1\n", - " 6 3.6142797e+09 9.44e-02 3.42e+04 -1.0 2.49e+01 -0.5 1.00e+00 1.00e+00f 1\n", - " 7 3.6063319e+09 1.26e+00 6.91e+03 -1.0 9.04e+01 -1.0 1.00e+00 1.00e+00f 1\n", - " 8 3.5609956e+09 4.91e+01 2.62e+05 -1.0 5.47e+02 -1.4 1.00e+00 1.00e+00f 1\n", - " 9 3.4536087e+09 1.18e+01 3.09e+05 -1.0 2.54e+02 -1.0 1.00e+00 1.00e+00f 1\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 10 3.1279033e+09 4.24e+02 2.06e+06 -1.0 1.38e+03 -1.5 1.00e+00 1.00e+00f 1\n", - " 11 3.4934385e+09 1.21e+02 2.69e+07 -1.0 1.18e+04 -2.0 3.42e-01 5.36e-02H 1\n", - " 12 2.9025111e+09 9.96e+02 5.96e+06 -1.0 2.40e+03 -1.5 1.00e+00 1.00e+00f 1\n", - " 13 3.0988933e+09 8.23e+02 3.70e+07 -1.0 3.37e+04 - 3.57e-01 1.95e-02H 1\n", - " 14 1.4289790e+09 1.38e+03 1.82e+07 -1.0 5.41e+03 -2.0 7.49e-01 1.00e+00f 1\n", - " 15 9.1847716e+08 2.03e+00 4.74e+06 -1.0 1.66e+03 - 6.80e-02 1.00e+00f 1\n", - " 16 9.1753645e+08 3.84e-02 4.80e+06 -1.0 3.84e+01 - 7.33e-04 1.00e+00f 1\n", - " 17 2.6734760e+08 8.34e+02 1.25e+07 -1.0 2.22e+03 -1.6 4.98e-04 1.00e+00f 1\n", - " 18 3.1392273e+07 4.23e+01 1.36e+06 -1.0 1.71e+03 - 9.07e-01 1.00e+00f 1\n", - " 19 2.7168841e+07 4.01e+00 3.90e+04 -1.0 3.38e+02 - 1.00e+00 1.00e+00f 1\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 20 2.7186259e+07 7.87e-03 4.66e+01 -1.0 9.43e+00 - 1.00e+00 1.00e+00h 1\n", - " 21 2.7186294e+07 1.60e-08 7.92e-05 -1.0 1.13e-02 - 1.00e+00 1.00e+00h 1\n", - " 22 2.7186294e+07 4.09e-12 4.46e-10 -2.5 2.05e-05 - 1.00e+00 1.00e+00h 1\n", - " 23 2.7186294e+07 4.41e-12 1.77e-10 -3.8 5.65e-07 - 1.00e+00 1.00e+00h 1\n", - " 24 2.7186294e+07 4.69e-12 4.44e-10 -5.7 3.14e-08 - 1.00e+00 1.00e+00h 1\n", - " 25 2.7186294e+07 4.12e-12 9.32e-10 -8.6 3.90e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 25\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 1.4055363392027403e+05 2.7186293738994990e+07\n", - "Dual infeasibility......: 9.3170592537955412e-10 1.8021327701920809e-07\n", - "Constraint violation....: 2.8906604310374570e-13 4.1211478674085811e-12\n", - "Complementarity.........: 2.5059035597865472e-09 4.8469917395798797e-07\n", - "Overall NLP error.......: 2.5059035597865472e-09 4.8469917395798797e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 28\n", - "Number of objective gradient evaluations = 26\n", - "Number of equality constraint evaluations = 28\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 26\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 25\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.018\n", - "Total CPU secs in NLP function evaluations = 0.003\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", - "Ipopt 3.13.2: bound_relax_factor=0\n", - "honor_original_bounds=no\n", - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 3.71e+03 1.00e+02 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 3.6253900e+06 7.03e+03 4.94e+05 -1.0 9.73e+03 - 5.66e-01 1.00e+00f 1\n", - " 2 6.3155920e+05 9.68e+02 1.03e+05 -1.0 2.11e+02 - 3.06e-01 1.00e+00f 1\n", - " 3 4.2943835e+05 3.66e+01 6.18e+03 -1.0 6.27e+01 - 1.00e+00 1.00e+00f 1\n", - " 4 4.2816462e+05 6.43e-02 1.50e+01 -1.0 3.60e+00 - 1.00e+00 1.00e+00h 1\n", - " 5 4.2816330e+05 2.08e-07 6.53e-05 -1.0 7.59e-03 - 1.00e+00 1.00e+00h 1\n", - " 6 4.2816330e+05 3.92e-12 2.10e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", - " 7 4.2816330e+05 4.18e-12 3.09e-12 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", - " 8 4.2816330e+05 5.09e-12 6.41e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", - " 9 4.2816330e+05 3.50e-12 4.40e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 9\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", - "Dual infeasibility......: 4.3995938736571737e-12 8.5098227662596132e-10\n", - "Constraint violation....: 1.2311475077887451e-13 3.4958702599396929e-12\n", - "Complementarity.........: 2.5059035597864302e-09 4.8469917395796542e-07\n", - "Overall NLP error.......: 2.5059035597864302e-09 4.8469917395796542e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 10\n", - "Number of objective gradient evaluations = 10\n", - "Number of equality constraint evaluations = 10\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 10\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 9\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.005\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", - "Ipopt 3.13.2: bound_relax_factor=0\n", - "honor_original_bounds=no\n", - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 2.36e+04 9.68e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 2.9411233e+07 2.24e+03 2.14e+04 -1.0 9.12e+03 - 9.31e-02 1.00e+00f 1\n", - " 2 2.7249209e+07 1.10e+02 1.53e+03 -1.0 2.59e+01 - 9.65e-01 1.00e+00f 1\n", - " 3 2.7186190e+07 3.26e-01 3.14e+00 -1.0 1.24e+00 - 1.00e+00 1.00e+00f 1\n", - " 4 2.7186294e+07 2.93e-06 8.94e-05 -1.0 5.33e-03 - 1.00e+00 1.00e+00h 1\n", - " 5 2.7186294e+07 4.32e-12 6.52e-10 -2.5 2.05e-05 - 1.00e+00 1.00e+00h 1\n", - " 6 2.7186294e+07 4.41e-12 6.15e-11 -3.8 5.65e-07 - 1.00e+00 1.00e+00h 1\n", - " 7 2.7186294e+07 4.69e-12 2.62e-10 -5.7 3.14e-08 - 1.00e+00 1.00e+00h 1\n", - " 8 2.7186294e+07 4.12e-12 1.03e-09 -8.6 3.90e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 8\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 1.4055363392027398e+05 2.7186293738994978e+07\n", - "Dual infeasibility......: 1.0262933743643622e-09 1.9850865722675085e-07\n", - "Constraint violation....: 4.3964831775156199e-14 4.1211478674085811e-12\n", - "Complementarity.........: 2.5059035597865472e-09 4.8469917395798797e-07\n", - "Overall NLP error.......: 2.5059035597865472e-09 4.8469917395798797e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 9\n", - "Number of objective gradient evaluations = 9\n", - "Number of equality constraint evaluations = 9\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 9\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 8\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.005\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", - "Ipopt 3.13.2: bound_relax_factor=0\n", - "honor_original_bounds=no\n", - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 5.50e+03 9.77e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 1.5759216e+08 1.17e+04 2.18e+05 -1.0 9.43e+03 - 1.70e-01 8.24e-01f 1\n", - " 2 2.3277921e+07 1.37e+03 9.11e+04 -1.0 1.41e+03 - 3.04e-02 1.00e+00f 1\n", - " 3 2.7051971e+07 4.76e+01 1.10e+03 -1.0 6.63e+01 - 1.00e+00 1.00e+00h 1\n", - " 4 2.7186107e+07 6.48e-02 2.18e+00 -1.0 2.12e+00 - 1.00e+00 1.00e+00h 1\n", - " 5 2.7186294e+07 1.19e-07 1.67e-05 -1.0 2.83e-03 - 1.00e+00 1.00e+00h 1\n", - " 6 2.7186294e+07 4.69e-12 1.19e-09 -2.5 2.05e-05 - 1.00e+00 1.00e+00h 1\n", - " 7 2.7186294e+07 4.41e-12 3.60e-10 -3.8 5.65e-07 - 1.00e+00 1.00e+00h 1\n", - " 8 2.7186294e+07 4.69e-12 2.91e-10 -5.7 3.14e-08 - 1.00e+00 1.00e+00h 1\n", - " 9 2.7186294e+07 4.12e-12 5.86e-10 -8.6 3.90e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 9\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 1.4055363392027401e+05 2.7186293738994986e+07\n", - "Dual infeasibility......: 5.8627081629004306e-10 1.1339821090148592e-07\n", - "Constraint violation....: 1.7015480447604767e-13 4.1211478674085811e-12\n", - "Complementarity.........: 2.5059035597865472e-09 4.8469917395798797e-07\n", - "Overall NLP error.......: 2.5059035597865472e-09 4.8469917395798797e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 10\n", - "Number of objective gradient evaluations = 10\n", - "Number of equality constraint evaluations = 10\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 10\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 9\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.005\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", - "Ipopt 3.13.2: bound_relax_factor=0\n", - "honor_original_bounds=no\n", - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 1.08e+04 9.98e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 9.8648968e+05 4.31e+02 1.25e+04 -1.0 9.71e+03 - 5.64e-01 1.00e+00f 1\n", - " 2 4.2829757e+05 9.37e+00 2.57e+02 -1.0 7.16e+01 - 1.00e+00 1.00e+00f 1\n", - " 3 4.2816336e+05 3.72e-03 4.88e-01 -1.0 2.97e-01 - 1.00e+00 1.00e+00h 1\n", - " 4 4.2816330e+05 5.74e-10 2.35e-07 -1.7 1.82e-04 - 1.00e+00 1.00e+00h 1\n", - " 5 4.2816330e+05 5.51e-12 6.63e-12 -3.8 5.28e-06 - 1.00e+00 1.00e+00h 1\n", - " 6 4.2816330e+05 1.88e-12 2.17e-11 -5.7 3.96e-08 - 1.00e+00 1.00e+00H 1\n", - " 7 4.2816330e+05 3.50e-12 3.72e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 7\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", - "Dual infeasibility......: 3.7174728473274775e-12 7.1904443859138779e-10\n", - "Constraint violation....: 6.7272184213622484e-14 3.4958702599396929e-12\n", - "Complementarity.........: 2.5059035597866419e-09 4.8469917395800629e-07\n", - "Overall NLP error.......: 2.5059035597866419e-09 4.8469917395800629e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 9\n", - "Number of objective gradient evaluations = 8\n", - "Number of equality constraint evaluations = 9\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 8\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 7\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", - "Ipopt 3.13.2: bound_relax_factor=0\n", - "honor_original_bounds=no\n", - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 1.51e+04 9.87e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 4.4591296e+05 4.94e+02 3.70e+03 -1.0 9.66e+03 - 6.72e-01 1.00e+00f 1\n", - " 2 4.2820756e+05 6.55e+00 1.50e+02 -1.0 1.56e+01 - 1.00e+00 1.00e+00f 1\n", - " 3 4.2816331e+05 1.24e-03 2.54e-02 -1.0 2.82e-01 - 1.00e+00 1.00e+00h 1\n", - " 4 4.2816330e+05 4.29e-11 1.22e-08 -2.5 4.22e-05 - 1.00e+00 1.00e+00h 1\n", - " 5 4.2816330e+05 4.49e-12 2.73e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", - " 6 4.2816330e+05 5.09e-12 1.09e-11 -5.7 3.95e-08 - 1.00e+00 1.00e+00h 1\n", - " 7 4.2816330e+05 3.50e-12 1.67e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 7\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 2.2136120434349154e+03 4.2816329651878146e+05\n", - "Dual infeasibility......: 1.6711097683383890e-12 3.2323092448766719e-10\n", - "Constraint violation....: 4.4162799930954544e-14 3.4958702599396929e-12\n", - "Complementarity.........: 2.5059035597866419e-09 4.8469917395800629e-07\n", - "Overall NLP error.......: 2.5059035597866419e-09 4.8469917395800629e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 8\n", - "Number of objective gradient evaluations = 8\n", - "Number of equality constraint evaluations = 8\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 8\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 7\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", - "Ipopt 3.13.2: bound_relax_factor=0\n", - "honor_original_bounds=no\n", - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 3.14e+03 9.52e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 3.2143856e+07 3.08e+03 8.73e+04 -1.0 9.13e+03 - 7.71e-01 1.00e+00f 1\n", - " 2 2.7516639e+07 2.13e+02 1.12e+04 -1.0 4.02e+01 - 8.52e-01 1.00e+00f 1\n", - " 3 2.7187562e+07 1.86e+00 1.48e+02 -1.0 3.04e+00 - 1.00e+00 1.00e+00f 1\n", - " 4 2.7186294e+07 1.48e-04 1.42e-02 -1.0 2.39e-02 - 1.00e+00 1.00e+00f 1\n", - " 5 2.7186294e+07 5.83e-12 4.05e-10 -2.5 1.58e-05 - 1.00e+00 1.00e+00h 1\n", - " 6 2.7186294e+07 4.60e-12 7.46e-10 -3.8 5.65e-07 - 1.00e+00 1.00e+00h 1\n", - " 7 2.7186294e+07 4.69e-12 1.22e-10 -5.7 3.14e-08 - 1.00e+00 1.00e+00h 1\n", - " 8 2.7186294e+07 4.12e-12 6.01e-10 -8.6 3.90e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 8\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 1.4055363392027401e+05 2.7186293738994986e+07\n", - "Dual infeasibility......: 6.0082273151840992e-10 1.1621288477955682e-07\n", - "Constraint violation....: 7.9483276450609387e-14 4.1211478674085811e-12\n", - "Complementarity.........: 2.5059035597865472e-09 4.8469917395798797e-07\n", - "Overall NLP error.......: 2.5059035597865472e-09 4.8469917395798797e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 9\n", - "Number of objective gradient evaluations = 9\n", - "Number of equality constraint evaluations = 9\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 9\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 8\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.005\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", - "Ipopt 3.13.2: bound_relax_factor=0\n", - "honor_original_bounds=no\n", - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 2.03e+04 9.73e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 2.2603904e+07 2.57e+03 2.37e+04 -1.0 9.24e+03 - 2.68e-01 1.00e+00f 1\n", - " 2 2.6435802e+07 1.99e+02 7.37e+03 -1.0 8.27e+01 - 9.14e-01 1.00e+00h 1\n", - " 3 2.7178955e+07 1.68e+00 2.58e+02 -1.0 1.10e+01 - 1.00e+00 1.00e+00h 1\n", - " 4 2.7186293e+07 1.18e-04 5.10e-02 -1.0 1.04e-01 - 1.00e+00 1.00e+00h 1\n", - " 5 2.7186294e+07 4.18e-12 4.94e-10 -2.5 1.35e-05 - 1.00e+00 1.00e+00h 1\n", - " 6 2.7186294e+07 5.57e-12 3.19e-10 -3.8 5.65e-07 - 1.00e+00 1.00e+00h 1\n", - " 7 2.7186294e+07 4.69e-12 3.46e-10 -5.7 3.14e-08 - 1.00e+00 1.00e+00h 1\n", - " 8 2.7186294e+07 4.12e-12 7.83e-10 -8.6 3.90e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 8\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 1.4055363392027401e+05 2.7186293738994986e+07\n", - "Dual infeasibility......: 7.8272167187299556e-10 1.5139630825544308e-07\n", - "Constraint violation....: 5.4622972811557702e-14 4.1211478674085811e-12\n", - "Complementarity.........: 2.5059035597865472e-09 4.8469917395798797e-07\n", - "Overall NLP error.......: 2.5059035597865472e-09 4.8469917395798797e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 9\n", - "Number of objective gradient evaluations = 9\n", - "Number of equality constraint evaluations = 9\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 9\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 8\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.005\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", - "Ipopt 3.13.2: bound_relax_factor=0\n", - "honor_original_bounds=no\n", - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 6.27e+03 1.05e+02 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 2.8071968e+08 1.32e+04 5.04e+06 -1.0 1.14e+04 - 1.01e-01 7.28e-01f 1\n", - " 2 2.1061322e+06 1.58e+03 1.08e+06 -1.0 1.60e+03 - 3.02e-02 1.00e+00f 1\n", - " 3 4.3722926e+05 7.27e+01 7.33e+04 -1.0 2.26e+02 - 1.00e+00 1.00e+00f 1\n", - " 4 4.2816494e+05 2.11e-01 3.44e+02 -1.0 1.68e+01 - 1.00e+00 1.00e+00f 1\n", - " 5 4.2816330e+05 2.17e-06 5.34e-03 -1.0 7.53e-02 - 1.00e+00 1.00e+00h 1\n", - " 6 4.2816330e+05 3.81e-12 1.86e-10 -2.5 2.47e-05 - 1.00e+00 1.00e+00h 1\n", - " 7 4.2816330e+05 5.31e-12 3.37e-12 -3.8 7.13e-07 - 1.00e+00 1.00e+00H 1\n", - " 8 4.2816330e+05 3.04e-12 7.53e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", - " 9 4.2816330e+05 3.50e-12 5.59e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 9\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", - "Dual infeasibility......: 5.5912355767736014e-12 1.0814730897694733e-09\n", - "Constraint violation....: 2.6290081223123707e-13 3.4958702599396925e-12\n", - "Complementarity.........: 2.5059035597864302e-09 4.8469917395796542e-07\n", - "Overall NLP error.......: 2.5059035597864302e-09 4.8469917395796542e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 11\n", - "Number of objective gradient evaluations = 10\n", - "Number of equality constraint evaluations = 11\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 10\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 9\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.005\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", - "Ipopt 3.13.2: bound_relax_factor=0\n", - "honor_original_bounds=no\n", - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 1.08e+03 1.04e+02 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 4.9442460e+09 2.90e+02 1.62e+06 -1.0 8.40e+01 0.0 9.06e-01 1.00e+00f 1\n", - " 2 3.8682267e+09 4.36e+03 3.75e+06 -1.0 7.36e+03 - 2.57e-01 2.22e-01f 1\n", - " 3 1.0445680e+09 2.36e+03 1.30e+07 -1.0 8.06e+03 - 5.91e-02 1.00e+00f 1\n", - " 4 1.8847235e+09 1.86e+03 8.68e+06 -1.0 1.76e+04 - 1.00e+00 1.41e-01H 1\n", - " 5 1.8845015e+09 1.84e+03 8.61e+06 -1.0 2.22e+02 -0.5 1.00e+00 8.89e-03f 1\n", - " 6 1.8845004e+09 1.84e+03 8.61e+06 -1.0 2.48e+02 -1.0 1.00e+00 8.80e-05f 1\n", - " 7 1.0031971e+09 1.86e+01 4.64e+06 -1.0 2.13e+03 - 6.41e-02 1.00e+00f 1\n", - " 8 9.8333743e+08 5.43e-01 4.40e+06 -1.0 1.34e+02 - 7.30e-04 1.00e+00f 1\n", - " 9 3.6753899e+08 8.32e+02 1.39e+07 -1.0 2.13e+03 -1.4 1.80e-02 1.00e+00f 1\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 10 1.6993277e+07 9.18e+01 2.49e+06 -1.0 2.09e+03 - 8.49e-01 1.00e+00f 1\n", - " 11 4.7911844e+05 1.48e+01 1.42e+05 -1.0 6.73e+02 - 1.00e+00 1.00e+00f 1\n", - " 12 4.2816647e+05 1.06e-01 6.23e+02 -1.0 3.67e+01 - 1.00e+00 1.00e+00f 1\n", - " 13 4.2816330e+05 2.92e-06 1.44e-02 -1.0 1.64e-01 - 1.00e+00 1.00e+00h 1\n", - " 14 4.2816330e+05 3.58e-12 1.88e-10 -2.5 2.55e-05 - 1.00e+00 1.00e+00h 1\n", - " 15 4.2816330e+05 4.18e-12 6.06e-12 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", - " 16 4.2816330e+05 5.09e-12 3.46e-12 -5.7 3.95e-08 - 1.00e+00 1.00e+00h 1\n", - " 17 4.2816330e+05 3.50e-12 5.36e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 17\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", - "Dual infeasibility......: 5.3638619013303694e-12 1.0374938104246155e-09\n", - "Constraint violation....: 4.9127553950366949e-13 3.4958702599396933e-12\n", - "Complementarity.........: 2.5059035597864302e-09 4.8469917395796542e-07\n", - "Overall NLP error.......: 2.5059035597864302e-09 4.8469917395796542e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 19\n", - "Number of objective gradient evaluations = 18\n", - "Number of equality constraint evaluations = 19\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 18\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 17\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.011\n", - "Total CPU secs in NLP function evaluations = 0.002\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", - "Ipopt 3.13.2: bound_relax_factor=0\n", - "honor_original_bounds=no\n", - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 1.50e+04 9.72e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 2.1140621e+07 5.69e+02 1.74e+04 -1.0 9.20e+03 - 9.48e-02 1.00e+00f 1\n", - " 2 2.7192445e+07 1.01e+01 6.77e+02 -1.0 6.67e+01 - 1.00e+00 1.00e+00h 1\n", - " 3 2.7186294e+07 2.68e-03 2.93e-01 -1.0 3.18e-01 - 1.00e+00 1.00e+00f 1\n", - " 4 2.7186294e+07 1.79e-10 9.00e-08 -1.7 1.58e-04 - 1.00e+00 1.00e+00h 1\n", - " 5 2.7186294e+07 5.57e-12 6.03e-10 -3.8 4.19e-06 - 1.00e+00 1.00e+00h 1\n", - " 6 2.7186294e+07 4.69e-12 1.24e-09 -5.7 3.14e-08 - 1.00e+00 1.00e+00h 1\n", - " 7 2.7186294e+07 4.12e-12 7.28e-10 -8.6 3.90e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 7\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 1.4055363392027401e+05 2.7186293738994986e+07\n", - "Dual infeasibility......: 7.2797911218231232e-10 1.4080784272619497e-07\n", - "Constraint violation....: 5.4978276510060899e-14 4.1211478674085811e-12\n", - "Complementarity.........: 2.5059035597865472e-09 4.8469917395798797e-07\n", - "Overall NLP error.......: 2.5059035597865472e-09 4.8469917395798797e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 8\n", - "Number of objective gradient evaluations = 8\n", - "Number of equality constraint evaluations = 8\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 8\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 7\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", - "Ipopt 3.13.2: bound_relax_factor=0\n", - "honor_original_bounds=no\n", - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 1.70e+03 1.00e+02 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 4.6387946e+09 1.63e+03 2.20e+04 -1.0 7.94e+03 - 2.58e-01 4.56e-02f 1\n", - " 2 4.6312518e+09 1.62e+03 2.19e+04 -1.0 8.01e+03 - 7.47e-01 8.57e-04f 1\n", - " 3 4.6299809e+09 1.62e+03 6.03e+06 -1.0 1.30e+03 - 1.00e+00 9.08e-04f 1\n", - " 4 3.4045767e+09 1.71e+02 8.63e+06 -1.0 1.24e+03 - 1.00e+00 1.00e+00f 1\n", - " 5 3.2357580e+09 1.04e-06 1.04e+07 -1.0 1.71e+02 0.0 3.67e-01 1.00e+00f 1\n", - " 6 3.2335364e+09 7.43e-03 1.67e+07 -1.0 5.07e+02 - 1.00e+00 1.34e-02f 1\n", - " 7 3.0814424e+09 4.30e+01 1.10e+07 -1.0 5.19e+02 - 1.00e+00 1.00e+00f 1\n", - " 8 3.0074467e+09 1.00e+00 2.28e+05 -1.0 1.35e+02 -0.5 1.00e+00 1.00e+00f 1\n", - " 9 2.8905662e+09 1.28e+01 3.92e+05 -1.0 3.38e+02 -1.0 1.00e+00 1.00e+00f 1\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 10 2.4797474e+09 1.64e+02 1.29e+06 -1.0 1.31e+03 -1.4 1.00e+00 1.00e+00f 1\n", - " 11 2.7418252e+09 5.14e+01 1.73e+07 -1.0 8.37e+04 - 2.44e-01 2.23e-02H 1\n", - " 12 1.9145132e+09 1.64e+03 9.70e+06 -1.0 4.53e+03 -1.9 7.97e-01 1.00e+00f 1\n", - " 13 1.0117309e+09 5.41e+01 4.54e+06 -1.0 3.05e+03 - 3.70e-02 1.00e+00f 1\n", - " 14 9.9642132e+08 4.47e-01 4.35e+06 -1.0 8.39e+01 - 4.55e-04 1.00e+00f 1\n", - " 15 2.6824003e+08 1.38e+03 2.06e+07 -1.0 2.71e+03 -1.5 1.25e-03 1.00e+00f 1\n", - " 16 3.7706102e+06 2.82e+01 1.15e+06 -1.0 1.91e+03 - 1.00e+00 1.00e+00f 1\n", - " 17 4.3043555e+05 3.35e+00 2.95e+04 -1.0 2.95e+02 - 1.00e+00 1.00e+00f 1\n", - " 18 4.2816340e+05 4.89e-03 2.80e+01 -1.0 7.70e+00 - 1.00e+00 1.00e+00f 1\n", - " 19 4.2816330e+05 6.04e-09 2.94e-05 -1.0 7.38e-03 - 1.00e+00 1.00e+00h 1\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 20 4.2816330e+05 3.69e-12 1.91e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", - " 21 4.2816330e+05 4.18e-12 4.47e-12 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", - " 22 4.2816330e+05 1.88e-12 2.88e-12 -5.7 3.95e-08 - 1.00e+00 1.00e+00H 1\n", - " 23 4.2816330e+05 3.50e-12 6.44e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 23\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 2.2136120434349154e+03 4.2816329651878146e+05\n", - "Dual infeasibility......: 6.4351411026732109e-12 1.2447037574879725e-09\n", - "Constraint violation....: 3.8099280283971188e-13 3.4958702599396929e-12\n", - "Complementarity.........: 2.5059035597864302e-09 4.8469917395796542e-07\n", - "Overall NLP error.......: 2.5059035597864302e-09 4.8469917395796542e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 26\n", - "Number of objective gradient evaluations = 24\n", - "Number of equality constraint evaluations = 26\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 24\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 23\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.015\n", - "Total CPU secs in NLP function evaluations = 0.003\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", - "Ipopt 3.13.2: bound_relax_factor=0\n", - "honor_original_bounds=no\n", - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 5.14e+03 9.96e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 5.1361724e+05 1.16e+03 5.52e+03 -1.0 9.68e+03 - 7.33e-01 1.00e+00f 1\n", - " 2 4.2850802e+05 3.35e+01 5.98e+02 -1.0 3.02e+01 - 1.00e+00 1.00e+00f 1\n", - " 3 4.2816351e+05 3.18e-02 2.65e+00 -1.0 1.13e+00 - 1.00e+00 1.00e+00h 1\n", - " 4 4.2816330e+05 2.81e-08 6.99e-06 -1.0 1.21e-03 - 1.00e+00 1.00e+00h 1\n", - " 5 4.2816330e+05 3.47e-12 2.02e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", - " 6 4.2816330e+05 5.31e-12 1.01e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00H 1\n", - " 7 4.2816330e+05 3.04e-12 1.11e-11 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", - " 8 4.2816330e+05 3.50e-12 7.58e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 8\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", - "Dual infeasibility......: 7.5828253298624225e-12 1.4666921874539710e-09\n", - "Constraint violation....: 8.0852985719428312e-14 3.4958702599396929e-12\n", - "Complementarity.........: 2.5059035597864302e-09 4.8469917395796542e-07\n", - "Overall NLP error.......: 2.5059035597864302e-09 4.8469917395796542e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 10\n", - "Number of objective gradient evaluations = 9\n", - "Number of equality constraint evaluations = 10\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 9\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 8\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.005\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", - "Ipopt 3.13.2: bound_relax_factor=0\n", - "honor_original_bounds=no\n", - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 8.79e+03 9.90e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 4.3423860e+05 1.42e+02 3.82e+03 -1.0 9.65e+03 - 9.02e-01 1.00e+00f 1\n", - " 2 4.2817922e+05 8.43e-01 1.02e+02 -1.0 4.91e+00 - 1.00e+00 1.00e+00f 1\n", - " 3 4.2816330e+05 2.99e-05 1.09e-02 -1.0 4.22e-02 - 1.00e+00 1.00e+00h 1\n", - " 4 4.2816330e+05 3.18e-12 1.14e-10 -2.5 2.42e-05 - 1.00e+00 1.00e+00h 1\n", - " 5 4.2816330e+05 2.76e-12 2.30e-03 -3.8 7.13e-07 - 1.00e+00 5.00e-01h 2\n", - " 6 4.2816330e+05 3.69e-12 1.15e-03 -3.8 3.56e-07 - 1.00e+00 5.00e-01h 2\n", - " 7 4.2816330e+05 4.18e-12 2.30e-11 -5.7 2.18e-07 - 1.00e+00 1.00e+00H 1\n", - " 8 4.2816330e+05 3.50e-12 1.89e-11 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 8\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 2.2136120434349154e+03 4.2816329651878146e+05\n", - "Dual infeasibility......: 1.8948306952157019e-11 3.6650367855318503e-09\n", - "Constraint violation....: 5.8616380029777494e-14 3.4958702599396925e-12\n", - "Complementarity.........: 2.5059035597864302e-09 4.8469917395796542e-07\n", - "Overall NLP error.......: 2.5059035597864302e-09 4.8469917395796542e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 14\n", - "Number of objective gradient evaluations = 9\n", - "Number of equality constraint evaluations = 14\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 9\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 8\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.005\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", - "Ipopt 3.13.2: bound_relax_factor=0\n", - "honor_original_bounds=no\n", - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 2.01e+03 1.03e+02 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 3.1611112e+09 1.31e+04 1.30e+07 -1.0 5.97e+03 -2.0 8.06e-02 3.16e-01f 1\n", - " 2 1.7336542e+07 2.79e+03 2.12e+06 -1.0 7.56e+03 - 1.17e-02 1.00e+00f 1\n", - " 3 2.7661414e+07 1.95e+02 2.35e+05 -1.0 7.82e+02 - 8.45e-01 1.00e+00h 1\n", - " 4 2.7187779e+07 1.42e+00 3.85e+03 -1.0 6.38e+01 - 1.00e+00 1.00e+00f 1\n", - " 5 2.7186293e+07 2.57e-04 9.05e-01 -1.0 1.01e+00 - 1.00e+00 1.00e+00f 1\n", - " 6 2.7186294e+07 1.00e-11 4.03e-08 -1.7 2.25e-04 - 1.00e+00 1.00e+00h 1\n", - " 7 2.7186294e+07 3.52e-12 6.33e-10 -3.8 4.19e-06 - 1.00e+00 1.00e+00h 1\n", - " 8 2.7186294e+07 4.69e-12 1.07e-09 -5.7 3.14e-08 - 1.00e+00 1.00e+00h 1\n", - " 9 2.7186294e+07 4.12e-12 7.17e-10 -8.6 3.90e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 9\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 1.4055363392027401e+05 2.7186293738994986e+07\n", - "Dual infeasibility......: 7.1723805334492121e-10 1.3873027580404212e-07\n", - "Constraint violation....: 3.9907987116676576e-13 4.1211478674085811e-12\n", - "Complementarity.........: 2.5059035597865472e-09 4.8469917395798797e-07\n", - "Overall NLP error.......: 2.5059035597865472e-09 4.8469917395798797e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 10\n", - "Number of objective gradient evaluations = 10\n", - "Number of equality constraint evaluations = 10\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 10\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 9\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.006\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", - "Ipopt 3.13.2: bound_relax_factor=0\n", - "honor_original_bounds=no\n", - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 9.28e+03 9.77e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 8.7268227e+06 3.80e+03 3.76e+04 -1.0 9.35e+03 - 2.32e-01 1.00e+00f 1\n", - " 2 2.6617881e+07 3.09e+02 1.04e+04 -1.0 2.12e+02 - 7.03e-01 1.00e+00h 1\n", - " 3 2.7179492e+07 2.60e+00 1.35e+02 -1.0 1.25e+01 - 1.00e+00 1.00e+00h 1\n", - " 4 2.7186293e+07 1.89e-04 3.26e-02 -1.0 1.05e-01 - 1.00e+00 1.00e+00h 1\n", - " 5 2.7186294e+07 4.97e-12 8.21e-10 -2.5 2.46e-05 - 1.00e+00 1.00e+00h 1\n", - " 6 2.7186294e+07 3.75e-12 3.04e-10 -3.8 5.65e-07 - 1.00e+00 1.00e+00h 1\n", - " 7 2.7186294e+07 4.69e-12 6.58e-10 -5.7 3.14e-08 - 1.00e+00 1.00e+00h 1\n", - " 8 2.7186294e+07 4.12e-12 9.83e-10 -8.6 3.90e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 8\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 1.4055363392027403e+05 2.7186293738994990e+07\n", - "Dual infeasibility......: 9.8263762867883811e-10 1.9006463559245624e-07\n", - "Constraint violation....: 1.0134854702842251e-13 4.1211478674085811e-12\n", - "Complementarity.........: 2.5059035597865472e-09 4.8469917395798797e-07\n", - "Overall NLP error.......: 2.5059035597865472e-09 4.8469917395798797e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 9\n", - "Number of objective gradient evaluations = 9\n", - "Number of equality constraint evaluations = 9\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 9\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 8\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.005\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", - "Ipopt 3.13.2: bound_relax_factor=0\n", - "honor_original_bounds=no\n", - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 4.05e+02 7.12e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 4.2427232e+09 1.39e+03 1.78e+06 -1.0 6.75e+03 -2.0 2.33e-01 1.21e-01f 1\n", - " 2 4.2335006e+09 1.39e+03 1.77e+06 -1.0 1.56e+04 - 3.72e-01 7.80e-04f 1\n", - " 3 4.2319049e+09 1.39e+03 2.67e+06 -1.0 5.42e+03 - 7.99e-01 4.52e-04f 1\n", - " 4 2.4661840e+09 1.62e+03 1.40e+07 -1.0 5.45e+03 - 1.00e+00 8.02e-01f 1\n", - " 5 1.4734739e+09 1.44e+03 1.59e+07 -1.0 7.78e+03 - 2.47e-01 1.00e+00f 1\n", - " 6 9.7692393e+08 2.37e+02 5.76e+06 -1.0 2.22e+03 - 5.27e-03 1.00e+00f 1\n", - " 7 6.1520348e+08 7.93e+01 5.07e+06 -1.0 9.78e+02 - 5.43e-05 1.00e+00f 1\n", - " 8 1.7354679e+08 2.47e+03 1.42e+07 -1.0 1.01e+04 - 9.76e-02 5.00e-01f 2\n", - " 9 3.1087330e+07 3.84e+02 1.69e+06 -1.0 1.44e+03 - 1.00e+00 1.00e+00f 1\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 10 2.7129946e+07 1.89e+01 7.70e+04 -1.0 3.15e+02 - 1.00e+00 1.00e+00f 1\n", - " 11 2.7186030e+07 6.02e-02 2.49e+02 -1.0 1.83e+01 - 1.00e+00 1.00e+00h 1\n", - " 12 2.7186294e+07 6.36e-07 2.66e-03 -1.0 6.05e-02 - 1.00e+00 1.00e+00h 1\n", - " 13 2.7186294e+07 3.58e-12 1.12e-09 -2.5 2.04e-05 - 1.00e+00 1.00e+00h 1\n", - " 14 2.7186294e+07 4.41e-12 4.03e-10 -3.8 5.65e-07 - 1.00e+00 1.00e+00h 1\n", - " 15 2.7186294e+07 4.69e-12 8.72e-11 -5.7 3.14e-08 - 1.00e+00 1.00e+00h 1\n", - " 16 2.7186294e+07 4.12e-12 5.21e-10 -8.6 3.90e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 16\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 1.4055363392027401e+05 2.7186293738994986e+07\n", - "Dual infeasibility......: 5.2078719776239223e-10 1.0073217845016686e-07\n", - "Constraint violation....: 3.0797477427240214e-13 4.1211478674085811e-12\n", - "Complementarity.........: 2.5059035597865472e-09 4.8469917395798797e-07\n", - "Overall NLP error.......: 2.5059035597865472e-09 4.8469917395798797e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 20\n", - "Number of objective gradient evaluations = 17\n", - "Number of equality constraint evaluations = 20\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 17\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 16\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.010\n", - "Total CPU secs in NLP function evaluations = 0.002\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", - "Ipopt 3.13.2: bound_relax_factor=0\n", - "honor_original_bounds=no\n", - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 4.47e+03 9.77e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 4.8483214e+09 1.07e+03 1.08e+07 -1.0 3.06e+02 0.0 1.33e-01 1.00e+00f 1\n", - " 2 2.7753495e+09 2.82e+03 8.80e+06 -1.0 1.07e+04 - 3.66e-01 3.05e-01f 1\n", - " 3 2.6846467e+09 2.73e+03 8.51e+06 -1.0 5.59e+03 - 1.00e+00 3.18e-02f 1\n", - " 4 2.6837149e+09 2.72e+03 8.51e+06 -1.0 5.41e+03 - 1.00e+00 3.43e-04f 1\n", - " 5 2.6591967e+09 2.70e+03 8.42e+06 -1.0 4.45e+03 - 1.00e+00 1.08e-02f 1\n", - " 6 1.2784608e+09 1.22e+02 1.09e+07 -1.0 4.43e+03 - 1.00e+00 1.00e+00f 1\n", - " 7 1.1089604e+09 3.44e+02 6.21e+06 -1.0 3.45e+03 - 2.95e-01 1.00e+00f 1\n", - " 8 1.0049821e+09 2.85e+01 4.47e+06 -1.0 8.11e+02 - 5.20e-03 1.00e+00f 1\n", - " 9 3.7408506e+08 2.96e+03 1.10e+07 -1.0 4.03e+03 - 2.70e-05 1.00e+00f 1\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 10 5.0448479e+06 1.49e+02 9.10e+05 -1.0 2.54e+03 - 1.00e+00 1.00e+00f 1\n", - " 11 4.2998863e+05 4.45e+00 3.44e+04 -1.0 2.81e+02 - 1.00e+00 1.00e+00f 1\n", - " 12 4.2816332e+05 3.34e-03 2.10e+01 -1.0 6.77e+00 - 1.00e+00 1.00e+00f 1\n", - " 13 4.2816330e+05 1.51e-09 8.78e-06 -1.0 4.22e-03 - 1.00e+00 1.00e+00h 1\n", - " 14 4.2816330e+05 3.69e-12 2.07e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", - " 15 4.2816330e+05 5.31e-12 2.82e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00H 1\n", - " 16 4.2816330e+05 3.04e-12 2.81e-11 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", - " 17 4.2816330e+05 3.50e-12 5.99e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 17\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", - "Dual infeasibility......: 5.9912096017597981e-12 1.1588372320399661e-09\n", - "Constraint violation....: 1.1815048168495169e-12 3.4958702599396929e-12\n", - "Complementarity.........: 2.5059035597864302e-09 4.8469917395796542e-07\n", - "Overall NLP error.......: 2.5059035597864302e-09 4.8469917395796542e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 19\n", - "Number of objective gradient evaluations = 18\n", - "Number of equality constraint evaluations = 19\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 18\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 17\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.010\n", - "Total CPU secs in NLP function evaluations = 0.002\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", - "Ipopt 3.13.2: bound_relax_factor=0\n", - "honor_original_bounds=no\n", - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 1.25e+04 9.65e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 2.7174519e+07 6.12e+02 8.07e+03 -1.0 9.16e+03 - 6.72e-01 1.00e+00f 1\n", - " 2 2.7157675e+07 1.43e+01 5.15e+02 -1.0 1.08e+01 - 1.00e+00 1.00e+00f 1\n", - " 3 2.7186261e+07 8.71e-03 1.33e+00 -1.0 5.48e-01 - 1.00e+00 1.00e+00h 1\n", - " 4 2.7186294e+07 3.15e-09 1.28e-06 -1.0 4.74e-04 - 1.00e+00 1.00e+00h 1\n", - " 5 2.7186294e+07 3.52e-12 3.69e-10 -2.5 2.05e-05 - 1.00e+00 1.00e+00h 1\n", - " 6 2.7186294e+07 4.41e-12 8.31e-11 -3.8 5.65e-07 - 1.00e+00 1.00e+00h 1\n", - " 7 2.7186294e+07 4.69e-12 3.86e-10 -5.7 3.14e-08 - 1.00e+00 1.00e+00h 1\n", - " 8 2.7186294e+07 4.12e-12 7.90e-10 -8.6 3.90e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 8\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 1.4055363392027401e+05 2.7186293738994986e+07\n", - "Dual infeasibility......: 7.8999762948675547e-10 1.5280364519439663e-07\n", - "Constraint violation....: 4.7101559432420681e-14 4.1211478674085811e-12\n", - "Complementarity.........: 2.5059035597865472e-09 4.8469917395798797e-07\n", - "Overall NLP error.......: 2.5059035597865472e-09 4.8469917395798797e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 9\n", - "Number of objective gradient evaluations = 9\n", - "Number of equality constraint evaluations = 9\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 9\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 8\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.005\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", - "Ipopt 3.13.2: bound_relax_factor=0\n", - "honor_original_bounds=no\n", - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 4.14e+03 9.57e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 2.4927485e+07 2.77e+03 5.05e+04 -1.0 9.14e+03 - 6.25e-01 1.00e+00f 1\n", - " 2 2.6826558e+07 1.63e+02 5.13e+03 -1.0 3.63e+01 - 8.14e-01 1.00e+00h 1\n", - " 3 2.7184194e+07 7.34e-01 6.95e+01 -1.0 5.26e+00 - 1.00e+00 1.00e+00h 1\n", - " 4 2.7186294e+07 1.49e-05 3.78e-03 -1.0 3.04e-02 - 1.00e+00 1.00e+00h 1\n", - " 5 2.7186294e+07 2.47e-12 8.83e-10 -2.5 2.11e-05 - 1.00e+00 1.00e+00h 1\n", - " 6 2.7186294e+07 4.41e-12 6.32e-10 -3.8 5.65e-07 - 1.00e+00 1.00e+00h 1\n", - " 7 2.7186294e+07 4.69e-12 2.33e-10 -5.7 3.14e-08 - 1.00e+00 1.00e+00h 1\n", - " 8 2.7186294e+07 4.12e-12 8.30e-10 -8.6 3.90e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 8\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 1.4055363392027401e+05 2.7186293738994986e+07\n", - "Dual infeasibility......: 8.2984251878098616e-10 1.6051055987271177e-07\n", - "Constraint violation....: 8.3214129700933826e-14 4.1211478674085811e-12\n", - "Complementarity.........: 2.5059035597865472e-09 4.8469917395798797e-07\n", - "Overall NLP error.......: 2.5059035597865472e-09 4.8469917395798797e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 9\n", - "Number of objective gradient evaluations = 9\n", - "Number of equality constraint evaluations = 9\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 9\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 8\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.005\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", - "Ipopt 3.13.2: bound_relax_factor=0\n", - "honor_original_bounds=no\n", - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 2.01e+04 1.01e+02 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 5.1232904e+06 2.26e+03 2.68e+04 -1.0 9.80e+03 - 1.37e-01 1.00e+00f 1\n", - " 2 4.4576929e+05 1.76e+02 6.61e+03 -1.0 2.13e+02 - 1.00e+00 1.00e+00f 1\n", - " 3 4.2819162e+05 1.33e+00 2.00e+02 -1.0 1.07e+01 - 1.00e+00 1.00e+00f 1\n", - " 4 4.2816330e+05 7.39e-05 3.52e-02 -1.0 7.94e-02 - 1.00e+00 1.00e+00h 1\n", - " 5 4.2816330e+05 3.84e-12 2.26e-10 -2.5 2.16e-05 - 1.00e+00 1.00e+00h 1\n", - " 6 4.2816330e+05 4.18e-12 1.33e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", - " 7 4.2816330e+05 1.88e-12 1.97e-12 -5.7 3.95e-08 - 1.00e+00 1.00e+00H 1\n", - " 8 4.2816330e+05 3.50e-12 8.34e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 8\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 2.2136120434349154e+03 4.2816329651878146e+05\n", - "Dual infeasibility......: 8.3365341010308284e-12 1.6124767358510909e-09\n", - "Constraint violation....: 6.5831840901751260e-14 3.4958702599396929e-12\n", - "Complementarity.........: 2.5059035597864302e-09 4.8469917395796542e-07\n", - "Overall NLP error.......: 2.5059035597864302e-09 4.8469917395796542e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 10\n", - "Number of objective gradient evaluations = 9\n", - "Number of equality constraint evaluations = 10\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 9\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 8\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.005\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", - "Ipopt 3.13.2: bound_relax_factor=0\n", - "honor_original_bounds=no\n", - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 2.29e+04 9.88e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 7.8310154e+05 1.91e+03 9.41e+03 -1.0 9.72e+03 - 6.19e-02 1.00e+00f 1\n", - " 2 4.3005112e+05 8.54e+01 1.56e+03 -1.0 6.58e+01 - 1.00e+00 1.00e+00f 1\n", - " 3 4.2816476e+05 2.09e-01 6.47e+00 -1.0 3.77e+00 - 1.00e+00 1.00e+00f 1\n", - " 4 4.2816330e+05 1.24e-06 1.73e-04 -1.0 8.41e-03 - 1.00e+00 1.00e+00h 1\n", - " 5 4.2816330e+05 3.92e-12 2.06e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", - " 6 4.2816330e+05 4.18e-12 2.30e-03 -3.8 7.13e-07 - 1.00e+00 5.00e-01h 2\n", - " 7 4.2816330e+05 3.69e-12 1.15e-03 -3.8 3.56e-07 - 1.00e+00 5.00e-01h 2\n", - " 8 4.2816330e+05 4.18e-12 2.66e-11 -5.7 2.18e-07 - 1.00e+00 1.00e+00H 1\n", - " 9 4.2816330e+05 3.50e-12 4.40e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 9\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 2.2136120434349154e+03 4.2816329651878146e+05\n", - "Dual infeasibility......: 4.3963917237901670e-12 8.5036290746094841e-10\n", - "Constraint violation....: 3.5289081297329319e-14 3.4958702599396929e-12\n", - "Complementarity.........: 2.5059035597864302e-09 4.8469917395796542e-07\n", - "Overall NLP error.......: 2.5059035597864302e-09 4.8469917395796542e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 15\n", - "Number of objective gradient evaluations = 10\n", - "Number of equality constraint evaluations = 15\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 10\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 9\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.006\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", - "Ipopt 3.13.2: bound_relax_factor=0\n", - "honor_original_bounds=no\n", - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 4.99e+03 9.57e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 2.8459805e+07 9.05e+02 3.08e+03 -1.0 9.14e+03 - 8.90e-01 1.00e+00f 1\n", - " 2 2.7214552e+07 2.03e+01 2.33e+02 -1.0 1.11e+01 - 1.00e+00 1.00e+00f 1\n", - " 3 2.7186309e+07 1.13e-02 3.06e-01 -1.0 2.51e-01 - 1.00e+00 1.00e+00f 1\n", - " 4 2.7186294e+07 3.47e-09 1.40e-07 -1.7 1.44e-04 - 1.00e+00 1.00e+00h 1\n", - " 5 2.7186294e+07 4.43e-12 5.75e-10 -3.8 4.19e-06 - 1.00e+00 1.00e+00h 1\n", - " 6 2.7186294e+07 4.69e-12 6.00e-10 -5.7 3.14e-08 - 1.00e+00 1.00e+00h 1\n", - " 7 2.7186294e+07 4.12e-12 7.10e-10 -8.6 3.90e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 7\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 1.4055363392027401e+05 2.7186293738994986e+07\n", - "Dual infeasibility......: 7.0996209573073779e-10 1.3732293886500667e-07\n", - "Constraint violation....: 6.4371955330339621e-14 4.1211478674085811e-12\n", - "Complementarity.........: 2.5059035597865472e-09 4.8469917395798797e-07\n", - "Overall NLP error.......: 2.5059035597865472e-09 4.8469917395798797e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 8\n", - "Number of objective gradient evaluations = 8\n", - "Number of equality constraint evaluations = 8\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 8\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 7\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", - "Ipopt 3.13.2: bound_relax_factor=0\n", - "honor_original_bounds=no\n", - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 1.30e+04 9.67e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 2.6124087e+07 9.63e+02 1.22e+04 -1.0 9.18e+03 - 6.10e-01 1.00e+00f 1\n", - " 2 2.7077188e+07 3.41e+01 1.82e+03 -1.0 2.77e+01 - 1.00e+00 1.00e+00h 1\n", - " 3 2.7186081e+07 5.00e-02 9.45e+00 -1.0 1.70e+00 - 1.00e+00 1.00e+00h 1\n", - " 4 2.7186294e+07 1.02e-07 5.14e-05 -1.0 2.98e-03 - 1.00e+00 1.00e+00h 1\n", - " 5 2.7186294e+07 2.67e-12 4.46e-10 -2.5 2.05e-05 - 1.00e+00 1.00e+00h 1\n", - " 6 2.7186294e+07 4.41e-12 3.04e-10 -3.8 5.65e-07 - 1.00e+00 1.00e+00h 1\n", - " 7 2.7186294e+07 4.69e-12 5.16e-10 -5.7 3.14e-08 - 1.00e+00 1.00e+00h 1\n", - " 8 2.7186294e+07 4.12e-12 5.64e-10 -8.6 3.90e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 8\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 1.4055363392027401e+05 2.7186293738994986e+07\n", - "Dual infeasibility......: 5.6444294344749279e-10 1.0917620008437956e-07\n", - "Constraint violation....: 5.0470631526657618e-14 4.1211478674085811e-12\n", - "Complementarity.........: 2.5059035597865472e-09 4.8469917395798797e-07\n", - "Overall NLP error.......: 2.5059035597865472e-09 4.8469917395798797e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 9\n", - "Number of objective gradient evaluations = 9\n", - "Number of equality constraint evaluations = 9\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 9\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 8\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.005\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", - "Ipopt 3.13.2: bound_relax_factor=0\n", - "honor_original_bounds=no\n", - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 4.15e+03 1.10e+02 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 4.9476936e+09 1.04e+03 3.45e+05 -1.0 1.14e+02 0.0 5.04e-01 1.00e+00f 1\n", - " 2 3.7919093e+09 2.35e+03 8.76e+05 -1.0 6.79e+03 - 5.62e-01 4.46e-01f 1\n", - " 3 3.4291421e+09 5.47e+02 1.25e+07 -1.0 6.72e+02 -0.5 1.00e+00 1.00e+00f 1\n", - " 4 1.4862561e+09 1.08e+03 2.19e+07 -1.0 2.79e+03 - 7.85e-01 1.00e+00f 1\n", - " 5 1.0803970e+09 6.74e+03 3.96e+07 -1.0 1.93e+04 - 1.00e+00 5.85e-01f 1\n", - " 6 7.1117865e+07 1.32e+03 7.64e+06 -1.0 3.91e+03 - 1.70e-01 1.00e+00f 1\n", - " 7 1.2641918e+06 1.32e+02 7.40e+05 -1.0 1.21e+03 - 1.00e+00 1.00e+00f 1\n", - " 8 4.2836369e+05 1.99e+00 1.09e+04 -1.0 1.45e+02 - 1.00e+00 1.00e+00f 1\n", - " 9 4.2816330e+05 4.66e-04 2.53e+00 -1.0 2.20e+00 - 1.00e+00 1.00e+00h 1\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 10 4.2816330e+05 2.83e-11 1.37e-07 -1.0 5.11e-04 - 1.00e+00 1.00e+00h 1\n", - " 11 4.2816330e+05 4.92e-12 2.04e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", - " 12 4.2816330e+05 5.31e-12 1.30e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00H 1\n", - " 13 4.2816330e+05 3.04e-12 1.29e-11 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", - " 14 4.2816330e+05 3.50e-12 1.17e-11 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 14\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", - "Dual infeasibility......: 1.1672349337973593e-11 2.2576998464963993e-09\n", - "Constraint violation....: 7.6028072726330720e-13 3.4958702599396929e-12\n", - "Complementarity.........: 2.5059035597864302e-09 4.8469917395796542e-07\n", - "Overall NLP error.......: 2.5059035597864302e-09 4.8469917395796542e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 16\n", - "Number of objective gradient evaluations = 15\n", - "Number of equality constraint evaluations = 16\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 15\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 14\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.009\n", - "Total CPU secs in NLP function evaluations = 0.002\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", - "Ipopt 3.13.2: bound_relax_factor=0\n", - "honor_original_bounds=no\n", - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 4.57e+02 9.29e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 4.9537123e+09 3.20e+02 4.79e+05 -1.0 1.07e+02 0.0 6.34e-01 1.00e+00f 1\n", - " 2 4.7229788e+09 2.17e+02 2.38e+06 -1.0 2.42e+02 -0.5 7.40e-01 1.00e+00f 1\n", - " 3 4.6081291e+09 1.51e+01 1.98e+05 -1.0 8.20e+01 -0.1 1.00e+00 1.00e+00f 1\n", - " 4 3.3364039e+09 4.66e+03 3.38e+06 -1.0 1.97e+04 - 1.21e-01 1.01e-01f 1\n", - " 5 8.5294751e+08 1.47e+03 1.28e+07 -1.0 6.99e+03 - 1.00e+00 1.00e+00f 1\n", - " 6 1.7203645e+09 1.37e+03 8.91e+06 -1.0 2.40e+04 - 3.10e-01 9.05e-02H 1\n", - " 7 1.7200201e+09 1.35e+03 8.82e+06 -1.0 1.79e+02 -0.5 1.00e+00 1.03e-02f 1\n", - " 8 1.7199986e+09 1.35e+03 8.82e+06 -1.0 2.26e+02 -1.0 1.00e+00 1.29e-04f 1\n", - " 9 1.0216189e+09 7.78e+01 5.04e+06 -1.0 1.88e+03 - 1.92e-01 1.00e+00f 1\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 10 9.9725562e+08 3.93e+00 4.37e+06 -1.0 3.37e+02 - 2.74e-03 1.00e+00f 1\n", - " 11 2.7996079e+08 1.31e+03 1.96e+07 -1.0 2.64e+03 -1.5 1.29e-03 1.00e+00f 1\n", - " 12 4.8482859e+06 2.85e+01 1.31e+06 -1.0 1.93e+03 - 8.31e-01 1.00e+00f 1\n", - " 13 4.3209489e+05 4.39e+00 3.90e+04 -1.0 3.41e+02 - 1.00e+00 1.00e+00f 1\n", - " 14 4.2816348e+05 8.48e-03 4.87e+01 -1.0 1.02e+01 - 1.00e+00 1.00e+00f 1\n", - " 15 4.2816330e+05 1.82e-08 8.87e-05 -1.0 1.28e-02 - 1.00e+00 1.00e+00h 1\n", - " 16 4.2816330e+05 4.18e-12 2.22e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", - " 17 4.2816330e+05 5.31e-12 1.01e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00H 1\n", - " 18 4.2816330e+05 3.04e-12 1.54e-11 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", - " 19 4.2816330e+05 3.50e-12 4.40e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 19\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", - "Dual infeasibility......: 4.3995938736571737e-12 8.5098227662596132e-10\n", - "Constraint violation....: 4.6942418461511737e-13 3.4958702599396929e-12\n", - "Complementarity.........: 2.5059035597864302e-09 4.8469917395796542e-07\n", - "Overall NLP error.......: 2.5059035597864302e-09 4.8469917395796542e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 22\n", - "Number of objective gradient evaluations = 20\n", - "Number of equality constraint evaluations = 22\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 20\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 19\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.013\n", - "Total CPU secs in NLP function evaluations = 0.002\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", - "Ipopt 3.13.2: bound_relax_factor=0\n", - "honor_original_bounds=no\n", - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 8.99e+03 9.64e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 2.5839765e+07 3.16e+02 8.96e+03 -1.0 9.16e+03 - 6.70e-01 1.00e+00f 1\n", - " 2 2.7166753e+07 4.34e+00 8.37e+01 -1.0 1.51e+01 - 1.00e+00 1.00e+00h 1\n", - " 3 2.7186290e+07 8.47e-04 9.25e-02 -1.0 2.86e-01 - 1.00e+00 1.00e+00h 1\n", - " 4 2.7186294e+07 3.28e-11 9.72e-09 -2.5 3.34e-05 - 1.00e+00 1.00e+00h 1\n", - " 5 2.7186294e+07 5.43e-12 1.38e-09 -3.8 5.65e-07 - 1.00e+00 1.00e+00h 1\n", - " 6 2.7186294e+07 4.69e-12 1.41e-09 -5.7 3.14e-08 - 1.00e+00 1.00e+00h 1\n", - " 7 2.7186294e+07 4.12e-12 8.95e-10 -8.6 3.90e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 7\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 1.4055363392027401e+05 2.7186293738994986e+07\n", - "Dual infeasibility......: 8.9532613730863700e-10 1.7317659232403083e-07\n", - "Constraint violation....: 5.0554756559712512e-14 4.1211478674085811e-12\n", - "Complementarity.........: 2.5059035597865472e-09 4.8469917395798797e-07\n", - "Overall NLP error.......: 2.5059035597865472e-09 4.8469917395798797e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 8\n", - "Number of objective gradient evaluations = 8\n", - "Number of equality constraint evaluations = 8\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 8\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 7\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", - "Ipopt 3.13.2: bound_relax_factor=0\n", - "honor_original_bounds=no\n", - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 2.98e+03 1.00e+02 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 4.0130337e+09 1.42e+04 3.05e+06 -1.0 9.58e+03 - 2.26e-02 1.43e-01f 1\n", - " 2 2.0683948e+07 6.26e+02 4.78e+05 -1.0 8.07e+03 - 2.73e-02 1.00e+00f 1\n", - " 3 2.7200387e+07 8.95e+00 1.34e+04 -1.0 1.07e+02 - 1.00e+00 1.00e+00h 1\n", - " 4 2.7186293e+07 2.26e-03 2.41e+00 -1.0 3.61e+00 - 1.00e+00 1.00e+00f 1\n", - " 5 2.7186294e+07 4.02e-10 1.02e-06 -1.0 9.28e-04 - 1.00e+00 1.00e+00h 1\n", - " 6 2.7186294e+07 3.18e-12 1.31e-09 -2.5 2.05e-05 - 1.00e+00 1.00e+00h 1\n", - " 7 2.7186294e+07 4.41e-12 7.76e-10 -3.8 5.65e-07 - 1.00e+00 1.00e+00h 1\n", - " 8 2.7186294e+07 4.69e-12 3.74e-10 -5.7 3.14e-08 - 1.00e+00 1.00e+00h 1\n", - " 9 2.7186294e+07 4.12e-12 8.08e-10 -8.6 3.90e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 9\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 1.4055363392027401e+05 2.7186293738994986e+07\n", - "Dual infeasibility......: 8.0801464593843588e-10 1.5628854905560542e-07\n", - "Constraint violation....: 1.3908096331519524e-12 4.1211478674085811e-12\n", - "Complementarity.........: 2.5059035597865472e-09 4.8469917395798797e-07\n", - "Overall NLP error.......: 2.5059035597865472e-09 4.8469917395798797e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 10\n", - "Number of objective gradient evaluations = 10\n", - "Number of equality constraint evaluations = 10\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 10\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 9\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.005\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", - "Ipopt 3.13.2: bound_relax_factor=0\n", - "honor_original_bounds=no\n", - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 9.84e+03 1.00e+02 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 1.7575504e+06 9.13e+02 1.38e+04 -1.0 9.75e+03 - 4.47e-01 1.00e+00f 1\n", - " 2 4.2824668e+05 2.49e+01 1.18e+03 -1.0 1.05e+02 - 1.00e+00 1.00e+00f 1\n", - " 3 4.2816335e+05 1.64e-02 2.86e+00 -1.0 6.45e-01 - 1.00e+00 1.00e+00h 1\n", - " 4 4.2816330e+05 6.88e-09 3.30e-06 -1.0 4.97e-04 - 1.00e+00 1.00e+00h 1\n", - " 5 4.2816330e+05 3.58e-12 2.27e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", - " 6 4.2816330e+05 4.18e-12 1.86e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", - " 7 4.2816330e+05 1.88e-12 6.52e-12 -5.7 3.95e-08 - 1.00e+00 1.00e+00H 1\n", - " 8 4.2816330e+05 3.50e-12 8.34e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 8\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 2.2136120434349154e+03 4.2816329651878146e+05\n", - "Dual infeasibility......: 8.3365341010308284e-12 1.6124767358510909e-09\n", - "Constraint violation....: 7.9278799075454343e-14 3.4958702599396933e-12\n", - "Complementarity.........: 2.5059035597864302e-09 4.8469917395796542e-07\n", - "Overall NLP error.......: 2.5059035597864302e-09 4.8469917395796542e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 10\n", - "Number of objective gradient evaluations = 9\n", - "Number of equality constraint evaluations = 10\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 9\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 8\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.005\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", - "Ipopt 3.13.2: bound_relax_factor=0\n", - "honor_original_bounds=no\n", - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 5.18e+03 9.94e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 4.4742544e+05 1.01e+03 5.01e+03 -1.0 9.66e+03 - 8.96e-01 1.00e+00f 1\n", - " 2 4.2822932e+05 2.50e+01 3.20e+02 -1.0 1.24e+01 - 1.00e+00 1.00e+00f 1\n", - " 3 4.2816333e+05 1.69e-02 4.70e-01 -1.0 3.08e-01 - 1.00e+00 1.00e+00h 1\n", - " 4 4.2816330e+05 7.77e-09 5.77e-07 -1.7 1.95e-04 - 1.00e+00 1.00e+00h 1\n", - " 5 4.2816330e+05 5.83e-12 6.32e-12 -3.8 5.28e-06 - 1.00e+00 1.00e+00h 1\n", - " 6 4.2816330e+05 5.09e-12 5.58e-12 -5.7 3.95e-08 - 1.00e+00 1.00e+00h 1\n", - " 7 4.2816330e+05 3.50e-12 2.86e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 7\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", - "Dual infeasibility......: 2.8627514714548167e-12 5.5372173763117912e-10\n", - "Constraint violation....: 7.8113496119784019e-14 3.4958702599396929e-12\n", - "Complementarity.........: 2.5059035597864302e-09 4.8469917395796542e-07\n", - "Overall NLP error.......: 2.5059035597864302e-09 4.8469917395796542e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 8\n", - "Number of objective gradient evaluations = 8\n", - "Number of equality constraint evaluations = 8\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 8\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 7\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", - "Ipopt 3.13.2: bound_relax_factor=0\n", - "honor_original_bounds=no\n", - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 1.70e+03 1.00e+02 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 4.8345448e+09 1.66e+03 6.22e+02 -1.0 7.58e+03 - 3.85e-01 2.30e-02f 1\n", - " 2 4.8321364e+09 1.66e+03 6.32e+04 -1.0 7.40e+03 - 9.64e-01 2.63e-04f 1\n", - " 3 4.7840829e+09 1.64e+03 4.48e+06 -1.0 4.93e+03 - 1.00e+00 1.38e-02f 1\n", - " 4 2.5854958e+09 1.81e+03 2.34e+07 -1.0 5.10e+03 - 1.00e+00 1.00e+00f 1\n", - " 5 2.4006679e+09 2.48e+03 3.67e+07 -1.0 1.01e+04 - 2.59e-01 1.00e+00f 1\n", - " 6 1.0935406e+09 5.82e+02 7.41e+06 -1.0 3.94e+03 - 6.77e-03 1.00e+00f 1\n", - " 7 6.7957486e+08 5.18e+01 5.05e+06 -1.0 9.85e+02 - 8.72e-05 1.00e+00f 1\n", - " 8 2.6794664e+08 3.62e+03 1.93e+07 -1.0 2.03e+04 - 3.53e-02 2.81e-01f 2\n", - " 9 3.6194377e+07 6.17e+02 2.61e+06 -1.0 1.73e+03 - 1.00e+00 1.00e+00f 1\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 10 2.7100647e+07 4.15e+01 1.63e+05 -1.0 4.49e+02 - 1.00e+00 1.00e+00f 1\n", - " 11 2.7185103e+07 2.75e-01 1.11e+03 -1.0 3.84e+01 - 1.00e+00 1.00e+00h 1\n", - " 12 2.7186294e+07 1.30e-05 5.36e-02 -1.0 2.71e-01 - 1.00e+00 1.00e+00h 1\n", - " 13 2.7186294e+07 3.52e-12 3.60e-10 -2.5 1.90e-05 - 1.00e+00 1.00e+00h 1\n", - " 14 2.7186294e+07 4.41e-12 1.67e-10 -3.8 5.65e-07 - 1.00e+00 1.00e+00h 1\n", - " 15 2.7186294e+07 4.69e-12 1.53e-10 -5.7 3.14e-08 - 1.00e+00 1.00e+00h 1\n", - " 16 2.7186294e+07 4.12e-12 6.92e-10 -8.6 3.90e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 16\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 1.4055363392027401e+05 2.7186293738994986e+07\n", - "Dual infeasibility......: 6.9159932411192459e-10 1.3377115803112010e-07\n", - "Constraint violation....: 1.6697754290362354e-13 4.1211478674085811e-12\n", - "Complementarity.........: 2.5059035597865472e-09 4.8469917395798797e-07\n", - "Overall NLP error.......: 2.5059035597865472e-09 4.8469917395798797e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 20\n", - "Number of objective gradient evaluations = 17\n", - "Number of equality constraint evaluations = 20\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 17\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 16\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.009\n", - "Total CPU secs in NLP function evaluations = 0.002\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", - "Ipopt 3.13.2: bound_relax_factor=0\n", - "honor_original_bounds=no\n", - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 1.40e+04 9.84e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 6.7882418e+06 2.02e+03 4.86e+04 -1.0 9.42e+03 - 1.15e-01 1.00e+00f 1\n", - " 2 2.7335745e+07 1.17e+02 1.07e+04 -1.0 2.72e+02 - 9.36e-01 1.00e+00h 1\n", - " 3 2.7186688e+07 3.81e-01 7.08e+01 -1.0 1.50e+00 - 1.00e+00 1.00e+00f 1\n", - " 4 2.7186294e+07 4.25e-06 1.56e-03 -1.0 8.68e-03 - 1.00e+00 1.00e+00f 1\n", - " 5 2.7186294e+07 4.12e-12 8.98e-10 -2.5 2.03e-05 - 1.00e+00 1.00e+00h 1\n", - " 6 2.7186294e+07 4.41e-12 1.60e-10 -3.8 5.65e-07 - 1.00e+00 1.00e+00h 1\n", - " 7 2.7186294e+07 4.69e-12 8.78e-11 -5.7 3.14e-08 - 1.00e+00 1.00e+00h 1\n", - " 8 2.7186294e+07 4.12e-12 5.06e-10 -8.6 3.90e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 8\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 1.4055363392027401e+05 2.7186293738994986e+07\n", - "Dual infeasibility......: 5.0623528253402496e-10 9.7917504572095877e-08\n", - "Constraint violation....: 8.9958290210069930e-14 4.1211478674085811e-12\n", - "Complementarity.........: 2.5059035597865472e-09 4.8469917395798797e-07\n", - "Overall NLP error.......: 2.5059035597865472e-09 4.8469917395798797e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 9\n", - "Number of objective gradient evaluations = 9\n", - "Number of equality constraint evaluations = 9\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 9\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 8\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.005\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", - "Ipopt 3.13.2: bound_relax_factor=0\n", - "honor_original_bounds=no\n", - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 5.24e+02 1.06e+02 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 2.8138975e+09 9.77e+03 5.01e+06 -1.0 1.59e+04 - 1.46e-01 1.92e-01f 1\n", - " 2 1.2458106e+07 2.49e+03 2.37e+06 -1.0 6.91e+03 - 1.10e-02 1.00e+00f 1\n", - " 3 5.9638509e+05 2.45e+02 2.68e+05 -1.0 5.41e+02 - 8.34e-01 1.00e+00f 1\n", - " 4 4.2833613e+05 3.87e+00 6.05e+03 -1.0 7.33e+01 - 1.00e+00 1.00e+00f 1\n", - " 5 4.2816332e+05 1.27e-03 2.81e+00 -1.0 1.76e+00 - 1.00e+00 1.00e+00h 1\n", - " 6 4.2816330e+05 1.69e-10 4.83e-07 -1.0 7.86e-04 - 1.00e+00 1.00e+00h 1\n", - " 7 4.2816330e+05 3.38e-12 1.99e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", - " 8 4.2816330e+05 4.18e-12 2.79e-12 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", - " 9 4.2816330e+05 5.09e-12 4.90e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 10 4.2816330e+05 3.50e-12 6.96e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 10\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", - "Dual infeasibility......: 6.9554776294329938e-12 1.3453487658386204e-09\n", - "Constraint violation....: 4.1257311072239357e-13 3.4958702599396933e-12\n", - "Complementarity.........: 2.5059035597864302e-09 4.8469917395796542e-07\n", - "Overall NLP error.......: 2.5059035597864302e-09 4.8469917395796542e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 11\n", - "Number of objective gradient evaluations = 11\n", - "Number of equality constraint evaluations = 11\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 11\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 10\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.006\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", - "Ipopt 3.13.2: bound_relax_factor=0\n", - "honor_original_bounds=no\n", - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 5.14e+03 1.01e+02 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 1.2558366e+07 9.25e+03 1.72e+06 -1.0 9.81e+03 - 3.06e-01 1.00e+00f 1\n", - " 2 1.4246827e+06 1.46e+03 3.89e+05 -1.0 4.34e+02 - 2.34e-01 1.00e+00f 1\n", - " 3 4.3546419e+05 8.26e+01 3.35e+04 -1.0 1.54e+02 - 9.56e-01 1.00e+00f 1\n", - " 4 4.2817095e+05 3.63e-01 2.26e+02 -1.0 1.32e+01 - 1.00e+00 1.00e+00f 1\n", - " 5 4.2816330e+05 7.79e-06 7.54e-03 -1.0 7.81e-02 - 1.00e+00 1.00e+00h 1\n", - " 6 4.2816330e+05 3.69e-12 1.66e-10 -2.5 2.53e-05 - 1.00e+00 1.00e+00h 1\n", - " 7 4.2816330e+05 4.18e-12 4.88e-12 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", - " 8 4.2816330e+05 5.09e-12 4.37e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", - " 9 4.2816330e+05 3.50e-12 1.42e-11 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 9\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", - "Dual infeasibility......: 1.4181073467955190e-11 2.7429445833665322e-09\n", - "Constraint violation....: 1.7763568394002505e-13 3.4958702599396925e-12\n", - "Complementarity.........: 2.5059035597864302e-09 4.8469917395796542e-07\n", - "Overall NLP error.......: 2.5059035597864302e-09 4.8469917395796542e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 10\n", - "Number of objective gradient evaluations = 10\n", - "Number of equality constraint evaluations = 10\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 10\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 9\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.005\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", - "Ipopt 3.13.2: bound_relax_factor=0\n", - "honor_original_bounds=no\n", - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 1.81e+04 9.66e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 2.9003335e+07 1.13e+03 1.55e+04 -1.0 9.14e+03 - 3.32e-01 1.00e+00f 1\n", - " 2 2.7238948e+07 3.20e+01 1.01e+03 -1.0 1.69e+01 - 1.00e+00 1.00e+00f 1\n", - " 3 2.7186331e+07 4.27e-02 2.05e+00 -1.0 5.65e-01 - 1.00e+00 1.00e+00f 1\n", - " 4 2.7186294e+07 7.75e-08 3.69e-06 -1.0 5.69e-04 - 1.00e+00 1.00e+00h 1\n", - " 5 2.7186294e+07 3.21e-12 5.51e-10 -2.5 2.05e-05 - 1.00e+00 1.00e+00h 1\n", - " 6 2.7186294e+07 4.41e-12 1.76e-10 -3.8 5.65e-07 - 1.00e+00 1.00e+00h 1\n", - " 7 2.7186294e+07 4.69e-12 2.40e-10 -5.7 3.14e-08 - 1.00e+00 1.00e+00h 1\n", - " 8 2.7186294e+07 4.12e-12 8.01e-10 -8.6 3.90e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 8\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 1.4055363392027401e+05 2.7186293738994986e+07\n", - "Dual infeasibility......: 8.0073868832425246e-10 1.5488121211656997e-07\n", - "Constraint violation....: 3.7925743719988039e-14 4.1211478674085811e-12\n", - "Complementarity.........: 2.5059035597865472e-09 4.8469917395798797e-07\n", - "Overall NLP error.......: 2.5059035597865472e-09 4.8469917395798797e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 9\n", - "Number of objective gradient evaluations = 9\n", - "Number of equality constraint evaluations = 9\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 9\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 8\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.005\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", - "Ipopt 3.13.2: bound_relax_factor=0\n", - "honor_original_bounds=no\n", - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 3.55e+03 9.62e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 2.5339484e+08 1.19e+04 3.21e+05 -1.0 9.18e+03 - 2.52e-01 7.84e-01f 1\n", - " 2 2.2997591e+07 1.30e+03 4.50e+04 -1.0 1.97e+03 - 2.63e-02 1.00e+00f 1\n", - " 3 2.7064893e+07 4.24e+01 5.28e+03 -1.0 6.00e+01 - 1.00e+00 1.00e+00h 1\n", - " 4 2.7186154e+07 4.87e-02 1.67e+01 -1.0 1.89e+00 - 1.00e+00 1.00e+00h 1\n", - " 5 2.7186294e+07 5.94e-08 4.87e-05 -1.0 2.66e-03 - 1.00e+00 1.00e+00h 1\n", - " 6 2.7186294e+07 2.79e-12 3.95e-10 -2.5 2.05e-05 - 1.00e+00 1.00e+00h 1\n", - " 7 2.7186294e+07 4.41e-12 1.99e-10 -3.8 5.65e-07 - 1.00e+00 1.00e+00h 1\n", - " 8 2.7186294e+07 4.69e-12 3.32e-11 -5.7 3.14e-08 - 1.00e+00 1.00e+00h 1\n", - " 9 2.7186294e+07 4.12e-12 5.94e-10 -8.6 3.90e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 9\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 1.4055363392027401e+05 2.7186293738994986e+07\n", - "Dual infeasibility......: 5.9354677390422649e-10 1.1480554784052137e-07\n", - "Constraint violation....: 1.6765154534897615e-13 4.1211478674085811e-12\n", - "Complementarity.........: 2.5059035597865472e-09 4.8469917395798797e-07\n", - "Overall NLP error.......: 2.5059035597865472e-09 4.8469917395798797e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 10\n", - "Number of objective gradient evaluations = 10\n", - "Number of equality constraint evaluations = 10\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 10\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 9\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.005\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", - "Ipopt 3.13.2: bound_relax_factor=0\n", - "honor_original_bounds=no\n", - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 1.41e+04 1.01e+02 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 2.9401960e+06 1.02e+03 1.97e+04 -1.0 9.77e+03 - 3.26e-01 1.00e+00f 1\n", - " 2 4.2927250e+05 4.60e+01 1.72e+03 -1.0 1.52e+02 - 1.00e+00 1.00e+00f 1\n", - " 3 4.2816494e+05 8.94e-02 1.45e+01 -1.0 1.80e+00 - 1.00e+00 1.00e+00h 1\n", - " 4 4.2816330e+05 3.28e-07 1.56e-04 -1.0 4.73e-03 - 1.00e+00 1.00e+00h 1\n", - " 5 4.2816330e+05 3.58e-12 2.16e-10 -2.5 2.58e-05 - 1.00e+00 1.00e+00h 1\n", - " 6 4.2816330e+05 5.31e-12 6.42e-12 -3.8 7.13e-07 - 1.00e+00 1.00e+00H 1\n", - " 7 4.2816330e+05 3.04e-12 1.36e-11 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", - " 8 4.2816330e+05 3.50e-12 6.22e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 8\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", - "Dual infeasibility......: 6.2153811273360234e-12 1.2021971422198111e-09\n", - "Constraint violation....: 7.1908036628402804e-14 3.4958702599396929e-12\n", - "Complementarity.........: 2.5059035597864302e-09 4.8469917395796542e-07\n", - "Overall NLP error.......: 2.5059035597864302e-09 4.8469917395796542e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 10\n", - "Number of objective gradient evaluations = 9\n", - "Number of equality constraint evaluations = 10\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 9\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 8\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.005\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", - "Ipopt 3.13.2: bound_relax_factor=0\n", - "honor_original_bounds=no\n", - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 1.12e+04 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 4.4982169e+05 6.60e+01 4.87e+03 -1.0 9.66e+03 - 7.20e-01 1.00e+00f 1\n", - " 2 4.2816538e+05 2.24e-01 6.53e+01 -1.0 1.30e+01 - 1.00e+00 1.00e+00f 1\n", - " 3 4.2816330e+05 1.71e-06 1.54e-03 -1.0 1.79e-02 - 1.00e+00 1.00e+00h 1\n", - " 4 4.2816330e+05 3.64e-12 1.98e-10 -2.5 2.58e-05 - 1.00e+00 1.00e+00h 1\n", - " 5 4.2816330e+05 4.18e-12 7.60e-12 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", - " 6 4.2816330e+05 5.09e-12 3.60e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", - " 7 4.2816330e+05 3.50e-12 3.49e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 7\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", - "Dual infeasibility......: 3.4900991718842455e-12 6.7506515924652994e-10\n", - "Constraint violation....: 5.1473574040930074e-14 3.4958702599396925e-12\n", - "Complementarity.........: 2.5059035597864302e-09 4.8469917395796542e-07\n", - "Overall NLP error.......: 2.5059035597864302e-09 4.8469917395796542e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 8\n", - "Number of objective gradient evaluations = 8\n", - "Number of equality constraint evaluations = 8\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 8\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 7\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", - "Ipopt 3.13.2: bound_relax_factor=0\n", - "honor_original_bounds=no\n", - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 5.28e+03 9.78e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 1.9323923e+07 7.90e+03 4.80e+05 -1.0 9.33e+03 - 2.42e-01 1.00e+00f 1\n", - " 2 2.3574045e+07 1.13e+03 1.06e+05 -1.0 1.29e+02 - 6.53e-01 1.00e+00h 1\n", - " 3 2.6987010e+07 4.63e+01 1.05e+04 -1.0 5.24e+01 - 1.00e+00 1.00e+00h 1\n", - " 4 2.7185919e+07 8.56e-02 5.03e+01 -1.0 2.77e+00 - 1.00e+00 1.00e+00h 1\n", - " 5 2.7186294e+07 2.43e-07 3.68e-04 -1.0 1.09e-02 - 1.00e+00 1.00e+00h 1\n", - " 6 2.7186294e+07 3.52e-12 9.77e-10 -2.5 2.05e-05 - 1.00e+00 1.00e+00h 1\n", - " 7 2.7186294e+07 4.41e-12 3.25e-10 -3.8 5.65e-07 - 1.00e+00 1.00e+00h 1\n", - " 8 2.7186294e+07 4.69e-12 4.15e-10 -5.7 3.14e-08 - 1.00e+00 1.00e+00h 1\n", - " 9 2.7186294e+07 4.12e-12 7.50e-10 -8.6 3.90e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 9\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 1.4055363392027401e+05 2.7186293738994986e+07\n", - "Dual infeasibility......: 7.4980698502496848e-10 1.4502985354332181e-07\n", - "Constraint violation....: 1.1133569257438686e-13 4.1211478674085811e-12\n", - "Complementarity.........: 2.5059035597865472e-09 4.8469917395798797e-07\n", - "Overall NLP error.......: 2.5059035597865472e-09 4.8469917395798797e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 10\n", - "Number of objective gradient evaluations = 10\n", - "Number of equality constraint evaluations = 10\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 10\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 9\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.005\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", - "Ipopt 3.13.2: bound_relax_factor=0\n", - "honor_original_bounds=no\n", - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 2.18e+04 9.74e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 2.2432629e+07 2.91e+03 2.66e+04 -1.0 9.25e+03 - 1.74e-01 1.00e+00f 1\n", - " 2 2.6283816e+07 2.45e+02 8.03e+03 -1.0 8.73e+01 - 8.79e-01 1.00e+00h 1\n", - " 3 2.7175244e+07 2.54e+00 3.60e+02 -1.0 1.33e+01 - 1.00e+00 1.00e+00h 1\n", - " 4 2.7186293e+07 2.70e-04 1.09e-01 -1.0 1.57e-01 - 1.00e+00 1.00e+00h 1\n", - " 5 2.7186294e+07 5.40e-12 1.77e-09 -2.5 1.89e-05 - 1.00e+00 1.00e+00h 1\n", - " 6 2.7186294e+07 4.41e-12 1.55e-10 -3.8 5.65e-07 - 1.00e+00 1.00e+00h 1\n", - " 7 2.7186294e+07 4.69e-12 6.07e-10 -5.7 3.14e-08 - 1.00e+00 1.00e+00h 1\n", - " 8 2.7186294e+07 4.12e-12 9.83e-10 -8.6 3.90e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 8\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 1.4055363392027403e+05 2.7186293738994990e+07\n", - "Dual infeasibility......: 9.8263762867873223e-10 1.9006463559243576e-07\n", - "Constraint violation....: 5.4622972811557702e-14 4.1211478674085811e-12\n", - "Complementarity.........: 2.5059035597865472e-09 4.8469917395798797e-07\n", - "Overall NLP error.......: 2.5059035597865472e-09 4.8469917395798797e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 9\n", - "Number of objective gradient evaluations = 9\n", - "Number of equality constraint evaluations = 9\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 9\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 8\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.005\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Warning: estimated values of mu do not seem consistent - using mu=10^(-8.6)\n", - " run theta1_init theta2_init theta1_est theta2_est objective\n", - "0 0 0.904117 -1.570500 1.204754 -0.646637 2.718629e+07\n", - "1 1 1.646200 1.969240 1.204754 -0.646637 2.718629e+07\n", - "2 2 -0.500088 -0.410770 -1.500300 0.501315 4.281633e+05\n", - "3 3 -0.032914 1.388731 -1.500300 0.501315 4.281633e+05\n", - "4 4 1.175866 -0.947448 1.204754 -0.646637 2.718629e+07\n", - "5 5 0.434035 0.596308 1.204754 -0.646637 2.718629e+07\n", - "6 6 -1.290840 -1.037834 -1.500300 0.501315 4.281633e+05\n", - "7 7 -1.114986 1.637305 -1.500300 0.501315 4.281633e+05\n", - "8 8 0.218859 -0.196021 1.204754 -0.646637 2.718629e+07\n", - "9 9 1.477024 0.343782 1.204754 -0.646637 2.718629e+07\n", - "10 10 -0.372907 -1.785309 1.204754 -0.646637 2.718629e+07\n", - "11 11 -0.840302 0.764188 -1.500300 0.501315 4.281633e+05\n", - "12 12 1.947122 -1.322903 1.204754 -0.646637 2.718629e+07\n", - "13 13 0.689208 1.220790 1.204754 -0.646637 2.718629e+07\n", - "14 14 -1.582145 -0.662319 -1.500300 0.501315 4.281633e+05\n", - "15 15 -1.744031 1.077411 -1.500300 0.501315 4.281633e+05\n", - "16 16 0.605718 -0.511126 1.204754 -0.646637 2.718629e+07\n", - "17 17 1.847797 0.909993 1.204754 -0.646637 2.718629e+07\n", - "18 18 -0.985865 -1.476522 -1.500300 0.501315 4.281633e+05\n", - "19 19 -0.453270 0.450540 -1.500300 0.501315 4.281633e+05\n", - "20 20 1.317900 -1.884256 1.204754 -0.646637 2.718629e+07\n", - "21 21 0.076058 1.533004 -1.500300 0.501315 4.281633e+05\n", - "22 22 -1.211183 -0.099532 -1.500300 0.501315 4.281633e+05\n", - "23 23 -1.379087 0.702927 -1.500300 0.501315 4.281633e+05\n", - "24 24 0.267140 -1.136644 1.204754 -0.646637 2.718629e+07\n", - "25 25 1.025302 1.284538 1.204754 -0.646637 2.718629e+07\n", - "26 26 -0.137004 -0.851065 1.204754 -0.646637 2.718629e+07\n", - "27 27 -0.669836 1.826000 -1.500300 0.501315 4.281633e+05\n", - "28 28 1.554977 -0.259714 1.204754 -0.646637 2.718629e+07\n", - "29 29 0.797052 0.157482 1.204754 -0.646637 2.718629e+07\n", - "30 30 -1.911667 -1.724012 -1.500300 0.501315 4.281633e+05\n", - "31 31 -1.986316 1.915811 -1.500300 0.501315 4.281633e+05\n", - "32 32 0.848017 -0.482338 1.204754 -0.646637 2.718629e+07\n", - "33 33 1.605694 0.067732 1.204754 -0.646637 2.718629e+07\n", - "34 34 -0.743749 -1.501449 -1.500300 0.501315 4.281633e+05\n", - "35 35 -0.211159 0.542803 -1.500300 0.501315 4.281633e+05\n", - "36 36 1.075776 -1.109333 1.204754 -0.646637 2.718629e+07\n", - "37 37 0.318351 1.444601 1.204754 -0.646637 2.718629e+07\n", - "38 38 -1.453490 -0.878315 -1.500300 0.501315 4.281633e+05\n", - "39 39 -1.136904 0.290293 -1.500300 0.501315 4.281633e+05\n", - "40 40 0.024975 -1.856822 1.204754 -0.646637 2.718629e+07\n", - "41 41 1.267545 1.693189 1.204754 -0.646637 2.718629e+07\n", - "42 42 -0.379231 -0.126904 -1.500300 0.501315 4.281633e+05\n", - "43 43 -0.912072 1.167345 -1.500300 0.501315 4.281633e+05\n", - "44 44 1.797196 -0.733872 1.204754 -0.646637 2.718629e+07\n", - "45 45 0.554877 0.820121 1.204754 -0.646637 2.718629e+07\n", - "46 46 -1.669509 -1.253837 -1.500300 0.501315 4.281633e+05\n", - "47 47 -1.500417 0.979125 -1.500300 0.501315 4.281633e+05\n", - "48 48 0.646298 -1.420653 1.204754 -0.646637 2.718629e+07\n", - "49 49 1.903972 1.005914 1.204754 -0.646637 2.718629e+07\n", - "\n", - "Unique estimated theta_1 values:\n", - "[ 1.20475361 -1.50030035]\n", - "\n", - "Unique estimated theta_2 values:\n", - "[-0.64663711 0.5013147 -0.64663711]\n" - ] - } - ], - "source": [ - "\n", - "# Run parmest estimation for multiple random initial guesses of theta within bounds\n", - "\n", - "num_runs = 50 # Number of random initializations\n", - "theta_names = ['theta_1', 'theta_2']\n", - "theta1_bounds = (-2, 2)\n", - "theta2_bounds = (-2, 2)\n", - "\n", - "results = []\n", - "\n", - "for run in range(num_runs):\n", - " # Sobol sampling for initial values\n", - " if run == 0:\n", - " sampler = scipy.stats.qmc.Sobol(d=len(theta_names), seed = 12345)\n", - " sobol_samples = sampler.random(num_runs + 1)[1:]\n", - " sobol_theta1 = theta1_bounds[0] + (theta1_bounds[1] - theta1_bounds[0]) * sobol_samples[:, 0]\n", - " sobol_theta2 = theta2_bounds[0] + (theta2_bounds[1] - theta2_bounds[0]) * sobol_samples[:, 1]\n", - " theta1_init = sobol_theta1[run]\n", - " theta2_init = sobol_theta2[run]\n", - " theta_initial = {1: theta1_init, 2: theta2_init}\n", - " # Create experiment and estimator\n", - " exp = Simple_Multimodal(data_df, theta_initial=theta_initial)\n", - " pest = parmest.Estimator([exp], tee=True)\n", - " \n", - " # Estimate parameters\n", - " obj, theta, cov = pest.theta_est(calc_cov=True, cov_n=len(conc))\n", - " results.append({\n", - " 'run': run,\n", - " 'theta1_init': theta1_init,\n", - " 'theta2_init': theta2_init,\n", - " 'theta1_est': theta['theta_1'],\n", - " 'theta2_est': theta['theta_2'],\n", - " 'objective': obj\n", - " })\n", - "\n", - "# Convert results to DataFrame for inspection\n", - "random_init_results_df = pd.DataFrame(results)\n", - "print(random_init_results_df)\n", - "\n", - "# Print unique values of estimated parameters\n", - "print(\"\\nUnique estimated theta_1 values:\")\n", - "print(random_init_results_df['theta1_est'].unique())\n", - "print(\"\\nUnique estimated theta_2 values:\")\n", - "print(random_init_results_df['theta2_est'].unique())\n" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "44c57a03", - "metadata": {}, - "outputs": [], - "source": [ - "# # Print the model ef_instance from the Estimator\n", - "# ef_instance = pest.ef_instance\n", - "\n", - "# ef_instance.pprint()" - ] - }, - { - "cell_type": "markdown", - "id": "7301ae3c", - "metadata": {}, - "source": [ - "### Integrated Multistart test\n", - "\n", - "Now checking results against embedded feature version of multistart. Same settings" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "e6841131", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Starting multistart optimization with 50 restarts using sobol sampling method.\n", - "Setting theta_2 to -1.570499699562788\n", - "Current value of theta_1 is 0.9041174054145813\n", - "Current value of theta_2 is -1.570499699562788\n", - "Ipopt 3.13.2: \n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", - " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", - " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", - " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", - " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", - " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 6\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", - "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", - "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", - "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 7\n", - "Number of objective gradient evaluations = 7\n", - "Number of equality constraint evaluations = 7\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 7\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 6\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Restart 1/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", - "theta_2 0.501315\n", - "dtype: float64\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Applications/anaconda3/envs/parmest-dev-mac2/lib/python3.13/site-packages/scipy/stats/_qmc.py:993: UserWarning: The balance properties of Sobol' points require n to be a power of 2.\n", - " sample = self._random(n, workers=workers)\n", - "/Users/scini/Documents/GitHub/pyomo/pyomo/contrib/parmest/parmest.py:1208: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", - " results_df.at[i, f'converged_{name}'] = converged_theta[j] if not np.isnan(converged_theta_vals[i, j]) else np.nan\n", - "/Users/scini/Documents/GitHub/pyomo/pyomo/contrib/parmest/parmest.py:1212: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value 'successful' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.\n", - " results_df.at[i, \"solver termination\"] = solver_termination if 'solver_termination' in locals() else np.nan\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Setting theta_2 to 1.9692404232919216\n", - "Current value of theta_1 is 1.6461998745799065\n", - "Current value of theta_2 is 1.9692404232919216\n", - "Ipopt 3.13.2: \n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", - " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", - " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", - " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", - " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", - " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 6\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", - "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", - "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", - "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 7\n", - "Number of objective gradient evaluations = 7\n", - "Number of equality constraint evaluations = 7\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 7\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 6\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Restart 2/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", - "theta_2 0.501315\n", - "dtype: float64\n", - "Setting theta_2 to -0.4107704162597656\n", - "Current value of theta_1 is -0.5000881142914295\n", - "Current value of theta_2 is -0.4107704162597656\n", - "Ipopt 3.13.2: \n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", - " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", - " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", - " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", - " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", - " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 6\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", - "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", - "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", - "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 7\n", - "Number of objective gradient evaluations = 7\n", - "Number of equality constraint evaluations = 7\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 7\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 6\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Restart 3/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", - "theta_2 0.501315\n", - "dtype: float64\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/scini/Documents/GitHub/pyomo/pyomo/contrib/parmest/parmest.py:1208: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", - " results_df.at[i, f'converged_{name}'] = converged_theta[j] if not np.isnan(converged_theta_vals[i, j]) else np.nan\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Setting theta_2 to 1.3887314423918724\n", - "Current value of theta_1 is -0.03291422128677368\n", - "Current value of theta_2 is 1.3887314423918724\n", - "Ipopt 3.13.2: \n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", - " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", - " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", - " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", - " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", - " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 6\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", - "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", - "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", - "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 7\n", - "Number of objective gradient evaluations = 7\n", - "Number of equality constraint evaluations = 7\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 7\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 6\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Restart 4/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", - "theta_2 0.501315\n", - "dtype: float64\n", - "Setting theta_2 to -0.9474484585225582\n", - "Current value of theta_1 is 1.1758659072220325\n", - "Current value of theta_2 is -0.9474484585225582\n", - "Ipopt 3.13.2: \n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", - " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", - " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", - " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", - " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", - " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 6\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", - "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", - "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", - "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 7\n", - "Number of objective gradient evaluations = 7\n", - "Number of equality constraint evaluations = 7\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 7\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 6\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Restart 5/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", - "theta_2 0.501315\n", - "dtype: float64\n", - "Setting theta_2 to 0.5963075272738934\n", - "Current value of theta_1 is 0.4340350441634655\n", - "Current value of theta_2 is 0.5963075272738934\n", - "Ipopt 3.13.2: \n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", - " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", - " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", - " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", - " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", - " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 6\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", - "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", - "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", - "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 7\n", - "Number of objective gradient evaluations = 7\n", - "Number of equality constraint evaluations = 7\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 7\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 6\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Restart 6/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", - "theta_2 0.501315\n", - "dtype: float64\n", - "Setting theta_2 to -1.03783418238163\n", - "Current value of theta_1 is -1.2908396199345589\n", - "Current value of theta_2 is -1.03783418238163\n", - "Ipopt 3.13.2: \n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", - " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", - " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", - " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", - " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", - " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 6\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", - "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", - "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", - "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 7\n", - "Number of objective gradient evaluations = 7\n", - "Number of equality constraint evaluations = 7\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 7\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 6\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Restart 7/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", - "theta_2 0.501315\n", - "dtype: float64\n", - "Setting theta_2 to 1.637305174022913\n", - "Current value of theta_1 is -1.114986129105091\n", - "Current value of theta_2 is 1.637305174022913\n", - "Ipopt 3.13.2: \n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", - " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", - " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", - " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", - " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", - " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 6\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", - "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", - "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", - "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 7\n", - "Number of objective gradient evaluations = 7\n", - "Number of equality constraint evaluations = 7\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 7\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 6\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Restart 8/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", - "theta_2 0.501315\n", - "dtype: float64\n", - "Setting theta_2 to -0.19602123647928238\n", - "Current value of theta_1 is 0.21885917708277702\n", - "Current value of theta_2 is -0.19602123647928238\n", - "Ipopt 3.13.2: \n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", - " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", - " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", - " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", - " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", - " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 6\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", - "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", - "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", - "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 7\n", - "Number of objective gradient evaluations = 7\n", - "Number of equality constraint evaluations = 7\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 7\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 6\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Restart 9/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", - "theta_2 0.501315\n", - "dtype: float64\n", - "Setting theta_2 to 0.34378181397914886\n", - "Current value of theta_1 is 1.4770240969955921\n", - "Current value of theta_2 is 0.34378181397914886\n", - "Ipopt 3.13.2: \n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", - " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", - " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", - " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", - " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", - " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 6\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", - "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", - "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", - "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 7\n", - "Number of objective gradient evaluations = 7\n", - "Number of equality constraint evaluations = 7\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 7\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 6\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Restart 10/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", - "theta_2 0.501315\n", - "dtype: float64\n", - "Setting theta_2 to -1.7853094227612019\n", - "Current value of theta_1 is -0.37290745973587036\n", - "Current value of theta_2 is -1.7853094227612019\n", - "Ipopt 3.13.2: \n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", - " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", - " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", - " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", - " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", - " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 6\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", - "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", - "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", - "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 7\n", - "Number of objective gradient evaluations = 7\n", - "Number of equality constraint evaluations = 7\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 7\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 6\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Restart 11/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", - "theta_2 0.501315\n", - "dtype: float64\n", - "Setting theta_2 to 0.764187891036272\n", - "Current value of theta_1 is -0.8403021283447742\n", - "Current value of theta_2 is 0.764187891036272\n", - "Ipopt 3.13.2: \n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", - " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", - " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", - " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", - " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", - " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 6\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", - "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", - "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", - "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 7\n", - "Number of objective gradient evaluations = 7\n", - "Number of equality constraint evaluations = 7\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 7\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 6\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Restart 12/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", - "theta_2 0.501315\n", - "dtype: float64\n", - "Setting theta_2 to -1.3229034766554832\n", - "Current value of theta_1 is 1.9471220299601555\n", - "Current value of theta_2 is -1.3229034766554832\n", - "Ipopt 3.13.2: \n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", - " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", - " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", - " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", - " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", - " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 6\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", - "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", - "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", - "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 7\n", - "Number of objective gradient evaluations = 7\n", - "Number of equality constraint evaluations = 7\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 7\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 6\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Restart 13/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", - "theta_2 0.501315\n", - "dtype: float64\n", - "Setting theta_2 to 1.2207895517349243\n", - "Current value of theta_1 is 0.6892080903053284\n", - "Current value of theta_2 is 1.2207895517349243\n", - "Ipopt 3.13.2: \n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", - " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", - " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", - " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", - " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", - " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 6\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", - "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", - "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", - "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 7\n", - "Number of objective gradient evaluations = 7\n", - "Number of equality constraint evaluations = 7\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 7\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 6\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Restart 14/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", - "theta_2 0.501315\n", - "dtype: float64\n", - "Setting theta_2 to -0.6623185910284519\n", - "Current value of theta_1 is -1.5821445994079113\n", - "Current value of theta_2 is -0.6623185910284519\n", - "Ipopt 3.13.2: \n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", - " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", - " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", - " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", - " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", - " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 6\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", - "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", - "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", - "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 7\n", - "Number of objective gradient evaluations = 7\n", - "Number of equality constraint evaluations = 7\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 7\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 6\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Restart 15/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", - "theta_2 0.501315\n", - "dtype: float64\n", - "Setting theta_2 to 1.077410764992237\n", - "Current value of theta_1 is -1.7440308779478073\n", - "Current value of theta_2 is 1.077410764992237\n", - "Ipopt 3.13.2: \n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", - " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", - " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", - " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", - " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", - " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 6\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", - "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", - "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", - "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 7\n", - "Number of objective gradient evaluations = 7\n", - "Number of equality constraint evaluations = 7\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 7\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 6\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Restart 16/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", - "theta_2 0.501315\n", - "dtype: float64\n", - "Setting theta_2 to -0.5111263506114483\n", - "Current value of theta_1 is 0.6057177819311619\n", - "Current value of theta_2 is -0.5111263506114483\n", - "Ipopt 3.13.2: \n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", - " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", - " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", - " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", - " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", - " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 6\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", - "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", - "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", - "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 7\n", - "Number of objective gradient evaluations = 7\n", - "Number of equality constraint evaluations = 7\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 7\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 6\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Restart 17/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", - "theta_2 0.501315\n", - "dtype: float64\n", - "Setting theta_2 to 0.909992840141058\n", - "Current value of theta_1 is 1.84779679402709\n", - "Current value of theta_2 is 0.909992840141058\n", - "Ipopt 3.13.2: \n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", - " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", - " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", - " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", - " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", - " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 6\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", - "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", - "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", - "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 7\n", - "Number of objective gradient evaluations = 7\n", - "Number of equality constraint evaluations = 7\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 7\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 6\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Restart 18/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", - "theta_2 0.501315\n", - "dtype: float64\n", - "Setting theta_2 to -1.4765218794345856\n", - "Current value of theta_1 is -0.9858651980757713\n", - "Current value of theta_2 is -1.4765218794345856\n", - "Ipopt 3.13.2: \n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", - " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", - " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", - " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", - " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", - " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 6\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", - "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", - "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", - "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 7\n", - "Number of objective gradient evaluations = 7\n", - "Number of equality constraint evaluations = 7\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 7\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 6\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Restart 19/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", - "theta_2 0.501315\n", - "dtype: float64\n", - "Setting theta_2 to 0.4505395218729973\n", - "Current value of theta_1 is -0.45326995477080345\n", - "Current value of theta_2 is 0.4505395218729973\n", - "Ipopt 3.13.2: \n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", - " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", - " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", - " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", - " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", - " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 6\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", - "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", - "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", - "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 7\n", - "Number of objective gradient evaluations = 7\n", - "Number of equality constraint evaluations = 7\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 7\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 6\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Restart 20/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", - "theta_2 0.501315\n", - "dtype: float64\n", - "Setting theta_2 to -1.8842555843293667\n", - "Current value of theta_1 is 1.3178998976945877\n", - "Current value of theta_2 is -1.8842555843293667\n", - "Ipopt 3.13.2: \n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", - " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", - " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", - " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", - " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", - " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 6\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", - "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", - "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", - "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 7\n", - "Number of objective gradient evaluations = 7\n", - "Number of equality constraint evaluations = 7\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 7\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 6\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Restart 21/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", - "theta_2 0.501315\n", - "dtype: float64\n", - "Setting theta_2 to 1.5330041460692883\n", - "Current value of theta_1 is 0.07605770975351334\n", - "Current value of theta_2 is 1.5330041460692883\n", - "Ipopt 3.13.2: \n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", - " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", - " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", - " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", - " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", - " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 6\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", - "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", - "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", - "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 7\n", - "Number of objective gradient evaluations = 7\n", - "Number of equality constraint evaluations = 7\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 7\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 6\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Restart 22/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", - "theta_2 0.501315\n", - "dtype: float64\n", - "Setting theta_2 to -0.09953175485134125\n", - "Current value of theta_1 is -1.211183074861765\n", - "Current value of theta_2 is -0.09953175485134125\n", - "Ipopt 3.13.2: \n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", - " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", - " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", - " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", - " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", - " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 6\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", - "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", - "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", - "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 7\n", - "Number of objective gradient evaluations = 7\n", - "Number of equality constraint evaluations = 7\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 7\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 6\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Restart 23/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", - "theta_2 0.501315\n", - "dtype: float64\n", - "Setting theta_2 to 0.7029270566999912\n", - "Current value of theta_1 is -1.3790867365896702\n", - "Current value of theta_2 is 0.7029270566999912\n", - "Ipopt 3.13.2: \n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", - " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", - " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", - " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", - " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", - " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 6\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", - "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", - "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", - "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 7\n", - "Number of objective gradient evaluations = 7\n", - "Number of equality constraint evaluations = 7\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 7\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 6\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Restart 24/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", - "theta_2 0.501315\n", - "dtype: float64\n", - "Setting theta_2 to -1.136644072830677\n", - "Current value of theta_1 is 0.2671399489045143\n", - "Current value of theta_2 is -1.136644072830677\n", - "Ipopt 3.13.2: \n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", - " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", - " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", - " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", - " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", - " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 6\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", - "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", - "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", - "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 7\n", - "Number of objective gradient evaluations = 7\n", - "Number of equality constraint evaluations = 7\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 7\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 6\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Restart 25/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", - "theta_2 0.501315\n", - "dtype: float64\n", - "Setting theta_2 to 1.2845380455255508\n", - "Current value of theta_1 is 1.0253018885850906\n", - "Current value of theta_2 is 1.2845380455255508\n", - "Ipopt 3.13.2: \n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", - " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", - " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", - " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", - " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", - " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 6\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", - "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", - "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", - "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 7\n", - "Number of objective gradient evaluations = 7\n", - "Number of equality constraint evaluations = 7\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 7\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 6\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Restart 26/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", - "theta_2 0.501315\n", - "dtype: float64\n", - "Setting theta_2 to -0.8510647006332874\n", - "Current value of theta_1 is -0.13700413331389427\n", - "Current value of theta_2 is -0.8510647006332874\n", - "Ipopt 3.13.2: \n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", - " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", - " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", - " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", - " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", - " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 6\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", - "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", - "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", - "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 7\n", - "Number of objective gradient evaluations = 7\n", - "Number of equality constraint evaluations = 7\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 7\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 6\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Restart 27/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", - "theta_2 0.501315\n", - "dtype: float64\n", - "Setting theta_2 to 1.8259997852146626\n", - "Current value of theta_1 is -0.6698363646864891\n", - "Current value of theta_2 is 1.8259997852146626\n", - "Ipopt 3.13.2: \n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", - " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", - " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", - " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", - " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", - " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 6\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", - "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", - "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", - "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 7\n", - "Number of objective gradient evaluations = 7\n", - "Number of equality constraint evaluations = 7\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 7\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 6\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Restart 28/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", - "theta_2 0.501315\n", - "dtype: float64\n", - "Setting theta_2 to -0.25971441715955734\n", - "Current value of theta_1 is 1.5549770556390285\n", - "Current value of theta_2 is -0.25971441715955734\n", - "Ipopt 3.13.2: \n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", - " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", - " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", - " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", - " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", - " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 6\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", - "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", - "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", - "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 7\n", - "Number of objective gradient evaluations = 7\n", - "Number of equality constraint evaluations = 7\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 7\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 6\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Restart 29/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", - "theta_2 0.501315\n", - "dtype: float64\n", - "Setting theta_2 to 0.1574823558330536\n", - "Current value of theta_1 is 0.7970522679388523\n", - "Current value of theta_2 is 0.1574823558330536\n", - "Ipopt 3.13.2: \n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", - " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", - " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", - " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", - " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", - " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 6\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", - "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", - "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", - "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 7\n", - "Number of objective gradient evaluations = 7\n", - "Number of equality constraint evaluations = 7\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 7\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 6\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Restart 30/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", - "theta_2 0.501315\n", - "dtype: float64\n", - "Setting theta_2 to -1.7240123488008976\n", - "Current value of theta_1 is -1.9116670340299606\n", - "Current value of theta_2 is -1.7240123488008976\n", - "Ipopt 3.13.2: \n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", - " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", - " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", - " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", - " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", - " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 6\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", - "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", - "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", - "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 7\n", - "Number of objective gradient evaluations = 7\n", - "Number of equality constraint evaluations = 7\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 7\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 6\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Restart 31/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", - "theta_2 0.501315\n", - "dtype: float64\n", - "Setting theta_2 to 1.9158114604651928\n", - "Current value of theta_1 is -1.9863164275884628\n", - "Current value of theta_2 is 1.9158114604651928\n", - "Ipopt 3.13.2: \n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", - " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", - " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", - " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", - " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", - " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 6\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", - "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", - "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", - "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 7\n", - "Number of objective gradient evaluations = 7\n", - "Number of equality constraint evaluations = 7\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 7\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 6\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Restart 32/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", - "theta_2 0.501315\n", - "dtype: float64\n", - "Setting theta_2 to -0.4823381155729294\n", - "Current value of theta_1 is 0.8480171598494053\n", - "Current value of theta_2 is -0.4823381155729294\n", - "Ipopt 3.13.2: \n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", - " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", - " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", - " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", - " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", - " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 6\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", - "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", - "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", - "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 7\n", - "Number of objective gradient evaluations = 7\n", - "Number of equality constraint evaluations = 7\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 7\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 6\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Restart 33/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", - "theta_2 0.501315\n", - "dtype: float64\n", - "Setting theta_2 to 0.06773220747709274\n", - "Current value of theta_1 is 1.6056938730180264\n", - "Current value of theta_2 is 0.06773220747709274\n", - "Ipopt 3.13.2: \n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", - " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", - " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", - " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", - " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", - " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 6\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", - "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", - "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", - "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 7\n", - "Number of objective gradient evaluations = 7\n", - "Number of equality constraint evaluations = 7\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 7\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 6\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Restart 34/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", - "theta_2 0.501315\n", - "dtype: float64\n", - "Setting theta_2 to -1.5014492236077785\n", - "Current value of theta_1 is -0.7437494024634361\n", - "Current value of theta_2 is -1.5014492236077785\n", - "Ipopt 3.13.2: \n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", - " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", - " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", - " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", - " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", - " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 6\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", - "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", - "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", - "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 7\n", - "Number of objective gradient evaluations = 7\n", - "Number of equality constraint evaluations = 7\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 7\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 6\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Restart 35/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", - "theta_2 0.501315\n", - "dtype: float64\n", - "Setting theta_2 to 0.5428028963506222\n", - "Current value of theta_1 is -0.21115940436720848\n", - "Current value of theta_2 is 0.5428028963506222\n", - "Ipopt 3.13.2: \n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", - " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", - " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", - " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", - " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", - " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 6\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", - "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", - "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", - "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 7\n", - "Number of objective gradient evaluations = 7\n", - "Number of equality constraint evaluations = 7\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 7\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 6\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Restart 36/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", - "theta_2 0.501315\n", - "dtype: float64\n", - "Setting theta_2 to -1.1093328893184662\n", - "Current value of theta_1 is 1.0757764726877213\n", - "Current value of theta_2 is -1.1093328893184662\n", - "Ipopt 3.13.2: \n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", - " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", - " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", - " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", - " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", - " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 6\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", - "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", - "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", - "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 7\n", - "Number of objective gradient evaluations = 7\n", - "Number of equality constraint evaluations = 7\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 7\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 6\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Restart 37/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", - "theta_2 0.501315\n", - "dtype: float64\n", - "Setting theta_2 to 1.4446007087826729\n", - "Current value of theta_1 is 0.3183508887887001\n", - "Current value of theta_2 is 1.4446007087826729\n", - "Ipopt 3.13.2: \n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", - " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", - " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", - " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", - " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", - " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 6\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", - "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", - "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", - "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 7\n", - "Number of objective gradient evaluations = 7\n", - "Number of equality constraint evaluations = 7\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 7\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 6\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Restart 38/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", - "theta_2 0.501315\n", - "dtype: float64\n", - "Setting theta_2 to -0.8783153407275677\n", - "Current value of theta_1 is -1.4534900821745396\n", - "Current value of theta_2 is -0.8783153407275677\n", - "Ipopt 3.13.2: \n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", - " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", - " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", - " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", - " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", - " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 6\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", - "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", - "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", - "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 7\n", - "Number of objective gradient evaluations = 7\n", - "Number of equality constraint evaluations = 7\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 7\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 6\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Restart 39/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", - "theta_2 0.501315\n", - "dtype: float64\n", - "Setting theta_2 to 0.29029324650764465\n", - "Current value of theta_1 is -1.1369038261473179\n", - "Current value of theta_2 is 0.29029324650764465\n", - "Ipopt 3.13.2: \n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", - " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", - " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", - " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", - " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", - " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 6\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", - "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", - "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", - "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 7\n", - "Number of objective gradient evaluations = 7\n", - "Number of equality constraint evaluations = 7\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 7\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 6\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Restart 40/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", - "theta_2 0.501315\n", - "dtype: float64\n", - "Setting theta_2 to -1.8568222858011723\n", - "Current value of theta_1 is 0.024974681437015533\n", - "Current value of theta_2 is -1.8568222858011723\n", - "Ipopt 3.13.2: \n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", - " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", - " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", - " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", - " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", - " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 6\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", - "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", - "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", - "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 7\n", - "Number of objective gradient evaluations = 7\n", - "Number of equality constraint evaluations = 7\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 7\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 6\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Restart 41/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", - "theta_2 0.501315\n", - "dtype: float64\n", - "Setting theta_2 to 1.6931888945400715\n", - "Current value of theta_1 is 1.2675453573465347\n", - "Current value of theta_2 is 1.6931888945400715\n", - "Ipopt 3.13.2: \n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", - " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", - " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", - " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", - " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", - " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 6\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", - "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", - "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", - "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 7\n", - "Number of objective gradient evaluations = 7\n", - "Number of equality constraint evaluations = 7\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 7\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 6\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Restart 42/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", - "theta_2 0.501315\n", - "dtype: float64\n", - "Setting theta_2 to -0.12690448015928268\n", - "Current value of theta_1 is -0.37923091277480125\n", - "Current value of theta_2 is -0.12690448015928268\n", - "Ipopt 3.13.2: \n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", - " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", - " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", - " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", - " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", - " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 6\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", - "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", - "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", - "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 7\n", - "Number of objective gradient evaluations = 7\n", - "Number of equality constraint evaluations = 7\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 7\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 6\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Restart 43/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", - "theta_2 0.501315\n", - "dtype: float64\n", - "Setting theta_2 to 1.1673445254564285\n", - "Current value of theta_1 is -0.912072204053402\n", - "Current value of theta_2 is 1.1673445254564285\n", - "Ipopt 3.13.2: \n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", - " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", - " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", - " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", - " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", - " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 6\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", - "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", - "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", - "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 7\n", - "Number of objective gradient evaluations = 7\n", - "Number of equality constraint evaluations = 7\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 7\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 6\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Restart 44/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", - "theta_2 0.501315\n", - "dtype: float64\n", - "Setting theta_2 to -0.7338721342384815\n", - "Current value of theta_1 is 1.7971962057054043\n", - "Current value of theta_2 is -0.7338721342384815\n", - "Ipopt 3.13.2: \n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", - " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", - " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", - " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", - " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", - " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 6\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", - "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", - "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", - "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 7\n", - "Number of objective gradient evaluations = 7\n", - "Number of equality constraint evaluations = 7\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 7\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 6\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Restart 45/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", - "theta_2 0.501315\n", - "dtype: float64\n", - "Setting theta_2 to 0.8201205767691135\n", - "Current value of theta_1 is 0.5548769868910313\n", - "Current value of theta_2 is 0.8201205767691135\n", - "Ipopt 3.13.2: \n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", - " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", - " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", - " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", - " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", - " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 6\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", - "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", - "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", - "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 7\n", - "Number of objective gradient evaluations = 7\n", - "Number of equality constraint evaluations = 7\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 7\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 6\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Restart 46/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", - "theta_2 0.501315\n", - "dtype: float64\n", - "Setting theta_2 to -1.253836639225483\n", - "Current value of theta_1 is -1.669509395956993\n", - "Current value of theta_2 is -1.253836639225483\n", - "Ipopt 3.13.2: \n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", - " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", - " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", - " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", - " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", - " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 6\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", - "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", - "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", - "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 7\n", - "Number of objective gradient evaluations = 7\n", - "Number of equality constraint evaluations = 7\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 7\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 6\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Restart 47/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", - "theta_2 0.501315\n", - "dtype: float64\n", - "Setting theta_2 to 0.9791254512965679\n", - "Current value of theta_1 is -1.500417035073042\n", - "Current value of theta_2 is 0.9791254512965679\n", - "Ipopt 3.13.2: \n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", - " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", - " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", - " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", - " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", - " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 6\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", - "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", - "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", - "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 7\n", - "Number of objective gradient evaluations = 7\n", - "Number of equality constraint evaluations = 7\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 7\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 6\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Restart 48/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", - "theta_2 0.501315\n", - "dtype: float64\n", - "Setting theta_2 to -1.420653060078621\n", - "Current value of theta_1 is 0.6462979316711426\n", - "Current value of theta_2 is -1.420653060078621\n", - "Ipopt 3.13.2: \n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", - " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", - " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", - " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", - " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", - " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 6\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", - "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", - "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", - "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 7\n", - "Number of objective gradient evaluations = 7\n", - "Number of equality constraint evaluations = 7\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 7\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 6\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Restart 49/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", - "theta_2 0.501315\n", - "dtype: float64\n", - "Setting theta_2 to 1.0059135183691978\n", - "Current value of theta_1 is 1.9039716646075249\n", - "Current value of theta_2 is 1.0059135183691978\n", - "Ipopt 3.13.2: \n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 3000\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 1003\n", - "\n", - "Total number of variables............................: 1002\n", - " variables with only lower bounds: 0\n", - " variables with lower and upper bounds: 2\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1000\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 5.0538096e+09 9.64e+03 9.89e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 4.2816331e+05 1.62e-03 1.13e+01 -1.0 9.65e+03 - 1.00e+00 1.00e+00f 1\n", - " 2 4.2816330e+05 1.19e-09 1.27e-05 -1.0 5.43e-03 - 1.00e+00 1.00e+00h 1\n", - " 3 4.2816330e+05 4.18e-12 2.00e-10 -2.5 2.59e-05 - 1.00e+00 1.00e+00h 1\n", - " 4 4.2816330e+05 4.18e-12 1.42e-11 -3.8 7.13e-07 - 1.00e+00 1.00e+00h 1\n", - " 5 4.2816330e+05 5.09e-12 4.36e-12 -5.7 3.96e-08 - 1.00e+00 1.00e+00h 1\n", - " 6 4.2816330e+05 3.50e-12 7.21e-12 -8.6 4.91e-10 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 6\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 2.2136120434349150e+03 4.2816329651878134e+05\n", - "Dual infeasibility......: 7.2074760468434291e-12 1.3940910345825789e-09\n", - "Constraint violation....: 5.6153732331586577e-14 3.4958702599396929e-12\n", - "Complementarity.........: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "Overall NLP error.......: 2.5059035597864260e-09 4.8469917395796457e-07\n", - "\n", - "\n", - "Number of objective function evaluations = 7\n", - "Number of objective gradient evaluations = 7\n", - "Number of equality constraint evaluations = 7\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 7\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 6\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n", - "Restart 50/50: Objective Value = 428163.2965187811, Theta = theta_1 -1.500300\n", - "theta_2 0.501315\n", - "dtype: float64\n", - "\n", - "\n", - "Results DataFrame:\n", - " theta_1 theta_2 converged_theta_1 converged_theta_2 \\\n", - "0 0.904117 -1.570500 -1.5003 0.501315 \n", - "1 1.646200 1.969240 -1.5003 0.501315 \n", - "2 -0.500088 -0.410770 -1.5003 0.501315 \n", - "3 -0.032914 1.388731 -1.5003 0.501315 \n", - "4 1.175866 -0.947448 -1.5003 0.501315 \n", - "5 0.434035 0.596308 -1.5003 0.501315 \n", - "6 -1.290840 -1.037834 -1.5003 0.501315 \n", - "7 -1.114986 1.637305 -1.5003 0.501315 \n", - "8 0.218859 -0.196021 -1.5003 0.501315 \n", - "9 1.477024 0.343782 -1.5003 0.501315 \n", - "10 -0.372907 -1.785309 -1.5003 0.501315 \n", - "11 -0.840302 0.764188 -1.5003 0.501315 \n", - "12 1.947122 -1.322903 -1.5003 0.501315 \n", - "13 0.689208 1.220790 -1.5003 0.501315 \n", - "14 -1.582145 -0.662319 -1.5003 0.501315 \n", - "15 -1.744031 1.077411 -1.5003 0.501315 \n", - "16 0.605718 -0.511126 -1.5003 0.501315 \n", - "17 1.847797 0.909993 -1.5003 0.501315 \n", - "18 -0.985865 -1.476522 -1.5003 0.501315 \n", - "19 -0.453270 0.450540 -1.5003 0.501315 \n", - "20 1.317900 -1.884256 -1.5003 0.501315 \n", - "21 0.076058 1.533004 -1.5003 0.501315 \n", - "22 -1.211183 -0.099532 -1.5003 0.501315 \n", - "23 -1.379087 0.702927 -1.5003 0.501315 \n", - "24 0.267140 -1.136644 -1.5003 0.501315 \n", - "25 1.025302 1.284538 -1.5003 0.501315 \n", - "26 -0.137004 -0.851065 -1.5003 0.501315 \n", - "27 -0.669836 1.826000 -1.5003 0.501315 \n", - "28 1.554977 -0.259714 -1.5003 0.501315 \n", - "29 0.797052 0.157482 -1.5003 0.501315 \n", - "30 -1.911667 -1.724012 -1.5003 0.501315 \n", - "31 -1.986316 1.915811 -1.5003 0.501315 \n", - "32 0.848017 -0.482338 -1.5003 0.501315 \n", - "33 1.605694 0.067732 -1.5003 0.501315 \n", - "34 -0.743749 -1.501449 -1.5003 0.501315 \n", - "35 -0.211159 0.542803 -1.5003 0.501315 \n", - "36 1.075776 -1.109333 -1.5003 0.501315 \n", - "37 0.318351 1.444601 -1.5003 0.501315 \n", - "38 -1.453490 -0.878315 -1.5003 0.501315 \n", - "39 -1.136904 0.290293 -1.5003 0.501315 \n", - "40 0.024975 -1.856822 -1.5003 0.501315 \n", - "41 1.267545 1.693189 -1.5003 0.501315 \n", - "42 -0.379231 -0.126904 -1.5003 0.501315 \n", - "43 -0.912072 1.167345 -1.5003 0.501315 \n", - "44 1.797196 -0.733872 -1.5003 0.501315 \n", - "45 0.554877 0.820121 -1.5003 0.501315 \n", - "46 -1.669509 -1.253837 -1.5003 0.501315 \n", - "47 -1.500417 0.979125 -1.5003 0.501315 \n", - "48 0.646298 -1.420653 -1.5003 0.501315 \n", - "49 1.903972 1.005914 -1.5003 0.501315 \n", - "\n", - " initial objective final objective solver termination solve_time \n", - "0 7.220190e+08 428163.296519 successful NaN \n", - "1 1.136218e+10 428163.296519 successful NaN \n", - "2 4.193775e+09 428163.296519 successful NaN \n", - "3 4.072387e+09 428163.296519 successful NaN \n", - "4 8.684940e+07 428163.296519 successful NaN \n", - "5 3.426209e+09 428163.296519 successful NaN \n", - "6 1.655079e+09 428163.296519 successful NaN \n", - "7 9.394389e+08 428163.296519 successful NaN \n", - "8 4.087402e+09 428163.296519 successful NaN \n", - "9 1.685132e+09 428163.296519 successful NaN \n", - "10 3.826184e+09 428163.296519 successful NaN \n", - "11 2.305593e+09 428163.296519 successful NaN \n", - "12 1.052740e+10 428163.296519 successful NaN \n", - "13 2.766225e+09 428163.296519 successful NaN \n", - "14 1.074960e+09 428163.296519 successful NaN \n", - "15 1.255159e+09 428163.296519 successful NaN \n", - "16 2.216156e+09 428163.296519 successful NaN \n", - "17 9.265699e+09 428163.296519 successful NaN \n", - "18 2.791493e+09 428163.296519 successful NaN \n", - "19 4.117223e+09 428163.296519 successful NaN \n", - "20 1.783957e+09 428163.296519 successful NaN \n", - "21 3.932326e+09 428163.296519 successful NaN \n", - "22 9.345576e+08 428163.296519 successful NaN \n", - "23 1.248299e+08 428163.296519 successful NaN \n", - "24 3.404587e+09 428163.296519 successful NaN \n", - "25 2.374101e+09 428163.296519 successful NaN \n", - "26 4.522286e+09 428163.296519 successful NaN \n", - "27 2.285725e+09 428163.296519 successful NaN \n", - "28 1.544874e+09 428163.296519 successful NaN \n", - "29 1.609095e+09 428163.296519 successful NaN \n", - "30 7.979763e+09 428163.296519 successful NaN \n", - "31 7.531637e+09 428163.296519 successful NaN \n", - "32 1.022801e+09 428163.296519 successful NaN \n", - "33 2.387392e+09 428163.296519 successful NaN \n", - "34 3.402058e+09 428163.296519 successful NaN \n", - "35 4.564792e+09 428163.296519 successful NaN \n", - "36 2.227816e+08 428163.296519 successful NaN \n", - "37 3.700507e+09 428163.296519 successful NaN \n", - "38 1.293657e+09 428163.296519 successful NaN \n", - "39 1.049181e+09 428163.296519 successful NaN \n", - "40 3.409571e+09 428163.296519 successful NaN \n", - "41 4.407327e+09 428163.296519 successful NaN \n", - "42 4.488860e+09 428163.296519 successful NaN \n", - "43 1.760318e+09 428163.296519 successful NaN \n", - "44 5.155549e+09 428163.296519 successful NaN \n", - "45 3.009035e+09 428163.296519 successful NaN \n", - "46 2.948710e+09 428163.296519 successful NaN \n", - "47 1.650676e+08 428163.296519 successful NaN \n", - "48 1.612714e+09 428163.296519 successful NaN \n", - "49 1.129338e+10 428163.296519 successful NaN \n", - "\n", - "Unique converged_theta_1 values: [-1.50030035]\n", - "Unique converged_theta_2 values: [0.5013147]\n" - ] - } - ], - "source": [ - "exp_list = [] \n", - "conc = data_df[\"x\"].values # substrate concentration (control variable)\n", - "vel = data_df[\"y\"].values # reaction velocity (output variable)\n", - "n_exp = 1\n", - "\n", - "# exp_list to separate each experiment\n", - "# for i in range(n_exp):\n", - "exp_list.append(Simple_Multimodal(data_df))\n", - "\n", - "# Creating an Estimator object\n", - "pest = parmest.Estimator(exp_list, tee = True) \n", - "\n", - "# obj, theta = pest.theta_est()\n", - "results_df = pest.theta_est_multistart(multistart_sampling_method=\"sobol\", n_restarts=50, seed = 12345)\n", - "\n", - "print(\"\\n\\nResults DataFrame:\")\n", - "print(results_df)\n", - "\n", - "# # Print unique parameter values\n", - "print(\"\\nUnique converged_theta_1 values:\", results_df['converged_theta_1'].unique())\n", - "print(\"Unique converged_theta_2 values:\", results_df['converged_theta_2'].unique())" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "parmest-dev-mac2", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.13.3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/pyomo/contrib/parmest/examples/reactor_design/multistart_example.py b/pyomo/contrib/parmest/examples/reactor_design/multistart_example.py index 90c6791c188..033c0ddcdc5 100644 --- a/pyomo/contrib/parmest/examples/reactor_design/multistart_example.py +++ b/pyomo/contrib/parmest/examples/reactor_design/multistart_example.py @@ -40,14 +40,9 @@ def main(): # Parameter estimation with multistart to avoid local minima obj, theta = pest.theta_est_multistart( - num_starts=10, - start_method='random', - random_seed=42, - max_iter=1000, - tol=1e-6, + num_starts=10, start_method='random', random_seed=42, max_iter=1000, tol=1e-6 ) - if __name__ == "__main__": main() diff --git a/pyomo/contrib/parmest/experiment.py b/pyomo/contrib/parmest/experiment.py index 16411858095..349226e824f 100644 --- a/pyomo/contrib/parmest/experiment.py +++ b/pyomo/contrib/parmest/experiment.py @@ -32,8 +32,7 @@ def get_labeled_model(self): def reinitialize_unknown_parameters(self): raise NotImplementedError( - "The reinitialize_unknown_parameters method should implemented in the subclass." \ + "The reinitialize_unknown_parameters method should implemented in the subclass." "Thi method will take new values for the unknown parameters from the Suffix " "and allow users to reinitialize the model." ) - diff --git a/pyomo/contrib/parmest/parmest.py b/pyomo/contrib/parmest/parmest.py index 2764983942a..6cc9e108f08 100644 --- a/pyomo/contrib/parmest/parmest.py +++ b/pyomo/contrib/parmest/parmest.py @@ -91,7 +91,7 @@ def ef_nonants(ef): def _experiment_instance_creation_callback( - scenario_name, node_names=None, cb_data=None, fix_vars=False, + scenario_name, node_names=None, cb_data=None, fix_vars=False ): """ This is going to be called by mpi-sppy or the local EF and it will call into @@ -253,6 +253,8 @@ def SSE(model): '''Adding pseudocode for draft implementation of the estimator class, incorporating multistart. ''' + + class Estimator(object): """ Parameter estimation class @@ -292,8 +294,7 @@ def __init__( diagnostic_mode=False, solver_options=None, ): - - '''first theta would be provided by the user in the initialization of + '''first theta would be provided by the user in the initialization of the Estimator class through the unknown parameter variables. Additional would need to be generated using the sampling method provided by the user. ''' @@ -470,44 +471,59 @@ def TotalCost_rule(model): parmest_model = utils.convert_params_to_vars(model, theta_names, fix_vars=False) return parmest_model - + # Make new private method, _generate_initial_theta: # This method will be used to generate the initial theta values for multistart # optimization. It will take the theta names and the initial theta values # and return a dictionary of theta names and their corresponding values. - def _generate_initial_theta(self, parmest_model=None, seed=None, n_restarts=None, multistart_sampling_method=None, user_provided=None): + def _generate_initial_theta( + self, + parmest_model=None, + seed=None, + n_restarts=None, + multistart_sampling_method=None, + user_provided=None, + ): """ Generate initial theta values for multistart optimization using selected sampling method. - """ + """ if n_restarts == 1: # If only one restart, return an empty list - return print("No multistart optimization needed. Please use normal theta_est()") - + return print( + "No multistart optimization needed. Please use normal theta_est()" + ) + # Get the theta names and initial theta values theta_names = self._return_theta_names() initial_theta = [parmest_model.find_component(name)() for name in theta_names] # Get the lower and upper bounds for the theta values - lower_bound = np.array([parmest_model.find_component(name).lb for name in theta_names]) - upper_bound = np.array([parmest_model.find_component(name).ub for name in theta_names]) + lower_bound = np.array( + [parmest_model.find_component(name).lb for name in theta_names] + ) + upper_bound = np.array( + [parmest_model.find_component(name).ub for name in theta_names] + ) # Check if the lower and upper bounds are defined - if any(bound is None for bound in lower_bound) and any(bound is None for bound in upper_bound): + if any(bound is None for bound in lower_bound) and any( + bound is None for bound in upper_bound + ): raise ValueError( "The lower and upper bounds for the theta values must be defined." ) - + # Check the length of theta_names and initial_theta, and make sure bounds are defined if len(theta_names) != len(initial_theta): raise ValueError( "The length of theta_names and initial_theta must be the same." ) - + if multistart_sampling_method == "uniform": # Generate random theta values using uniform distribution, with set seed for reproducibility np.random.seed(seed) # Generate random theta values for each restart (n_restarts x len(theta_names)) theta_vals_multistart = np.random.uniform( - low=lower_bound, high=upper_bound, size=(n_restarts, len(theta_names)) + low=lower_bound, high=upper_bound, size=(n_restarts, len(theta_names)) ) elif multistart_sampling_method == "latin_hypercube": @@ -523,10 +539,10 @@ def _generate_initial_theta(self, parmest_model=None, seed=None, n_restarts=None sampler = scipy.stats.qmc.Sobol(d=len(theta_names), seed=seed) # Generate theta values using Sobol sampling # The first value of the Sobol sequence is 0, so we skip it - samples = sampler.random(n=n_restarts+1)[1:] + samples = sampler.random(n=n_restarts + 1)[1:] elif multistart_sampling_method == "user_provided": - # Add user provided dataframe option + # Add user provided dataframe option if user_provided is not None: if isinstance(user_provided, np.ndarray): @@ -540,9 +556,9 @@ def _generate_initial_theta(self, parmest_model=None, seed=None, n_restarts=None raise ValueError( "The user provided numpy array must have the same number of columns as the number of theta names." ) - # Check if the user provided numpy array has the same theta names as the model - # if not, raise an error - # if not all(theta in theta_names for theta in user_provided.columns): + # Check if the user provided numpy array has the same theta names as the model + # if not, raise an error + # if not all(theta in theta_names for theta in user_provided.columns): raise ValueError( "The user provided numpy array must have the same theta names as the model." ) @@ -559,14 +575,16 @@ def _generate_initial_theta(self, parmest_model=None, seed=None, n_restarts=None raise ValueError( "The user provided dataframe must have the same number of columns as the number of theta names." ) - # Check if the user provided dataframe has the same theta names as the model - # if not, raise an error - # if not all(theta in theta_names for theta in user_provided.columns): + # Check if the user provided dataframe has the same theta names as the model + # if not, raise an error + # if not all(theta in theta_names for theta in user_provided.columns): raise ValueError( "The user provided dataframe must have the same theta names as the model." ) # If all checks pass, return the user provided dataframe - theta_vals_multistart = user_provided.iloc[0: len(initial_theta)].values + theta_vals_multistart = user_provided.iloc[ + 0 : len(initial_theta) + ].values else: raise ValueError( "The user must provide a numpy array or pandas dataframe from a previous attempt to use the 'user_provided' method." @@ -576,11 +594,16 @@ def _generate_initial_theta(self, parmest_model=None, seed=None, n_restarts=None raise ValueError( "Invalid sampling method. Choose 'uniform', 'latin_hypercube', 'sobol' or 'user_provided'." ) - - if multistart_sampling_method == "sobol" or multistart_sampling_method == "latin_hypercube": + + if ( + multistart_sampling_method == "sobol" + or multistart_sampling_method == "latin_hypercube" + ): # Scale the samples to the range of the lower and upper bounds for each theta in theta_names - theta_vals_multistart = np.array([lower_bound + (upper_bound - lower_bound) * theta for theta in samples]) - + theta_vals_multistart = np.array( + [lower_bound + (upper_bound - lower_bound) * theta for theta in samples] + ) + # Create a DataFrame where each row is an initial theta vector for a restart, # columns are theta_names, and values are the initial theta values for each restart if multistart_sampling_method == "user_provided": @@ -837,7 +860,7 @@ def _Q_at_theta(self, thetavals, initialize_parmest_model=False): # start block of code to deal with models with no constraints # (ipopt will crash or complain on such problems without special care) - instance = _experiment_instance_creation_callback("FOO0", None, dummy_cb,) + instance = _experiment_instance_creation_callback("FOO0", None, dummy_cb) try: # deal with special problems so Ipopt will not crash first = next(instance.component_objects(pyo.Constraint, active=True)) active_constraints = True @@ -854,7 +877,9 @@ def _Q_at_theta(self, thetavals, initialize_parmest_model=False): for snum in scenario_numbers: sname = "scenario_NODE" + str(snum) - instance = _experiment_instance_creation_callback(sname, None, dummy_cb, fix_vars=True) + instance = _experiment_instance_creation_callback( + sname, None, dummy_cb, fix_vars=True + ) model_theta_names = self._expand_indexed_unknowns(instance) if initialize_parmest_model: @@ -1093,7 +1118,7 @@ def theta_est_multistart( save_results=False, theta_vals=None, solver="ef_ipopt", - file_name = "multistart_results.csv", + file_name="multistart_results.csv", return_values=[], ): """ @@ -1143,26 +1168,31 @@ def theta_est_multistart( assert isinstance(multistart_sampling_method, str) assert isinstance(solver, str) assert isinstance(return_values, list) - + if n_restarts > 1 and multistart_sampling_method is not None: - + # Find the initialized values of theta from the labeled parmest model # and the theta names from the estimator object # print statement to indicate multistart optimization is starting - print(f"Starting multistart optimization with {n_restarts} restarts using {multistart_sampling_method} sampling method.") + print( + f"Starting multistart optimization with {n_restarts} restarts using {multistart_sampling_method} sampling method." + ) # @Reviewers, pyomo team: Use this or use instance creation callback? theta_names = self._return_theta_names() # Generate theta values using the sampling method parmest_model_for_bounds = self._create_parmest_model(experiment_number=0) results_df = self._generate_initial_theta( - parmest_model_for_bounds, seed=seed, n_restarts=n_restarts, - multistart_sampling_method=multistart_sampling_method, user_provided=user_provided + parmest_model_for_bounds, + seed=seed, + n_restarts=n_restarts, + multistart_sampling_method=multistart_sampling_method, + user_provided=user_provided, ) results_df = pd.DataFrame(results_df) # Extract theta_vals from the dataframe - theta_vals = results_df.iloc[:, :len(theta_names)] + theta_vals = results_df.iloc[:, : len(theta_names)] converged_theta_vals = np.zeros((n_restarts, len(theta_names))) timer = TicTocTimer() @@ -1187,7 +1217,7 @@ def theta_est_multistart( # for name in theta_names: # current_value = parmest_model.find_component(name)() # print(f"Current value of {name} is {current_value}") - + # Call the _Q_opt method with the generated theta values qopt_result = self._Q_opt( ThetaVals=theta_vals_current, @@ -1196,9 +1226,8 @@ def theta_est_multistart( return_values=return_values, ) - # Unpack results + # Unpack results objectiveval, converged_theta = qopt_result - # Since _Q_opt does not return the solver result object, we cannot check # solver termination condition directly here. Instead, we can assume @@ -1218,7 +1247,11 @@ def theta_est_multistart( # plan to add solve time if available, @Reviewers, recommendations on how from current pyomo examples would # be appreciated - solve_time = converged_theta.solve_time if hasattr(converged_theta, 'solve_time') else np.nan + solve_time = ( + converged_theta.solve_time + if hasattr(converged_theta, 'solve_time') + else np.nan + ) # # Check if the objective value is better than the best objective value # # Set a very high initial best objective value @@ -1226,12 +1259,14 @@ def theta_est_multistart( # Initialize best objective value and theta best_objectiveval = np.inf best_theta = np.inf - # Check if the final objective value is better than the best found so far + # Check if the final objective value is better than the best found so far if final_objectiveval < best_objectiveval: best_objectiveval = objectiveval best_theta = converged_theta.values - - print(f"Restart {i+1}/{n_restarts}: Objective Value = {final_objectiveval}, Theta = {converged_theta}") + + print( + f"Restart {i+1}/{n_restarts}: Objective Value = {final_objectiveval}, Theta = {converged_theta}" + ) # Stop the timer for this restart solve_time = timer.toc(f"Restart {i+1}/{n_restarts}") @@ -1239,12 +1274,24 @@ def theta_est_multistart( # Store the results in the DataFrame for this restart # Fill converged theta values for j, name in enumerate(theta_names): - results_df.at[i, f'converged_{name}'] = converged_theta[j] if not np.isnan(converged_theta_vals[i, j]) else np.nan + results_df.at[i, f'converged_{name}'] = ( + converged_theta[j] + if not np.isnan(converged_theta_vals[i, j]) + else np.nan + ) # Fill initial and final objective values, solver termination, and solve time - results_df.at[i, "initial objective"] = init_objectiveval if 'init_objectiveval' in locals() else np.nan - results_df.at[i, "final objective"] = objectiveval if 'objectiveval' in locals() else np.nan - results_df.at[i, "solver termination"] = solver_termination if 'solver_termination' in locals() else np.nan - results_df.at[i, "solve_time"] = solve_time if 'solve_time' in locals() else np.nan + results_df.at[i, "initial objective"] = ( + init_objectiveval if 'init_objectiveval' in locals() else np.nan + ) + results_df.at[i, "final objective"] = ( + objectiveval if 'objectiveval' in locals() else np.nan + ) + results_df.at[i, "solver termination"] = ( + solver_termination if 'solver_termination' in locals() else np.nan + ) + results_df.at[i, "solve_time"] = ( + solve_time if 'solve_time' in locals() else np.nan + ) # Diagnostic: print the table after each restart # print(results_df) @@ -1253,9 +1300,7 @@ def theta_est_multistart( if save_results and (i + 1) % buffer == 0: mode = 'w' if i + 1 == buffer else 'a' header = i + 1 == buffer - results_df.to_csv( - file_name, mode=mode, header=header, index=False - ) + results_df.to_csv(file_name, mode=mode, header=header, index=False) print(f"Intermediate results saved after {i + 1} iterations.") # Final save after all iterations @@ -1265,8 +1310,6 @@ def theta_est_multistart( return results_df, best_theta, best_objectiveval - - def theta_est_bootstrap( self, bootstrap_samples, From 6b3ee40a350c2d4c0d56bf8979e22efb7314116f Mon Sep 17 00:00:00 2001 From: Stephen Cini <114932899+sscini@users.noreply.github.com> Date: Fri, 27 Jun 2025 08:29:52 -0400 Subject: [PATCH 17/23] Added utility to update model using suffix values --- pyomo/contrib/doe/utils.py | 42 ++++++++++++++++++++++++++++++++++++++ 1 file changed, 42 insertions(+) diff --git a/pyomo/contrib/doe/utils.py b/pyomo/contrib/doe/utils.py index 24be6fd696a..bd991747319 100644 --- a/pyomo/contrib/doe/utils.py +++ b/pyomo/contrib/doe/utils.py @@ -100,3 +100,45 @@ def rescale_FIM(FIM, param_vals): # pass # ToDo: Write error for suffix keys that aren't ParamData or VarData # # return param_list + +# Adding utility to update parameter values in a model based on the suffix +def update_model_from_suffix(model, suffix_name, values): + """"" + Iterate over the components (variables or parameters) referenced by the + given suffix in the model, and assign each a new value from the provided iterable. + + Parameters + ---------- + model : pyomo.environ.ConcreteModel + The Pyomo model containing the suffix and components to update. + suffix_name : str + The name of the Suffix attribute on the model whose items will be updated. + Must be one of: 'experiment_outputs', 'experiment_inputs', 'unknown_parameters', or 'measurement_error'. + values : iterable of numbers + The new values to assign to each component referenced by the suffix. The length of this + iterable must match the number of items in the suffix. + + """ + # Allowed suffix names + allowed = { + 'experiment_outputs', 'experiment_inputs', + 'unknown_parameters', 'measurement_error' + } + # Validate input is an allowed suffix name + if suffix_name not in allowed: + raise ValueError(f"suffix_name must be one of {sorted(allowed)}") + # Check if the model has the specified suffix + suffix_obj = getattr(model, suffix_name, None) + if suffix_obj is None: + raise AttributeError(f"Model has no attribute '{suffix_name}'") + # Check if the suffix is a Suffix object + items = list(suffix_obj.items()) + if len(items) != len(values): + raise ValueError("values length does not match suffix length") + # Set the new values for the suffix items + for (comp, _), new_val in zip(items, values): + # Update the variable/parameter itself if it is VarData or ParamData + if isinstance(comp, (VarData, ParamData)): + comp.set_value(new_val) + else: + raise TypeError(f"Unsupported component type: {type(comp)}") From 5cadfac59294b19981d57912023b157958444298 Mon Sep 17 00:00:00 2001 From: Stephen Cini <114932899+sscini@users.noreply.github.com> Date: Fri, 27 Jun 2025 15:33:16 -0400 Subject: [PATCH 18/23] Work on Friday 6/27 applying PR comments --- pyomo/contrib/parmest/parmest.py | 58 ++++++++++++++++---------------- 1 file changed, 29 insertions(+), 29 deletions(-) diff --git a/pyomo/contrib/parmest/parmest.py b/pyomo/contrib/parmest/parmest.py index 6cc9e108f08..897e19d35ac 100644 --- a/pyomo/contrib/parmest/parmest.py +++ b/pyomo/contrib/parmest/parmest.py @@ -482,7 +482,7 @@ def _generate_initial_theta( seed=None, n_restarts=None, multistart_sampling_method=None, - user_provided=None, + user_provided_df=None, ): """ Generate initial theta values for multistart optimization using selected sampling method. @@ -512,13 +512,7 @@ def _generate_initial_theta( "The lower and upper bounds for the theta values must be defined." ) - # Check the length of theta_names and initial_theta, and make sure bounds are defined - if len(theta_names) != len(initial_theta): - raise ValueError( - "The length of theta_names and initial_theta must be the same." - ) - - if multistart_sampling_method == "uniform": + if multistart_sampling_method == "uniform_random": # Generate random theta values using uniform distribution, with set seed for reproducibility np.random.seed(seed) # Generate random theta values for each restart (n_restarts x len(theta_names)) @@ -541,58 +535,58 @@ def _generate_initial_theta( # The first value of the Sobol sequence is 0, so we skip it samples = sampler.random(n=n_restarts + 1)[1:] - elif multistart_sampling_method == "user_provided": + elif multistart_sampling_method == "user_provided_values": # Add user provided dataframe option - if user_provided is not None: + if user_provided_df is not None: - if isinstance(user_provided, np.ndarray): + if isinstance(user_provided_df, np.ndarray): # Check if the user provided numpy array has the same number of rows as the number of restarts - if user_provided.shape[0] != n_restarts: + if user_provided_df.shape[0] != n_restarts: raise ValueError( "The user provided numpy array must have the same number of rows as the number of restarts." ) # Check if the user provided numpy array has the same number of columns as the number of theta names - if user_provided.shape[1] != len(theta_names): + if user_provided_df.shape[1] != len(theta_names): raise ValueError( "The user provided numpy array must have the same number of columns as the number of theta names." ) # Check if the user provided numpy array has the same theta names as the model # if not, raise an error - # if not all(theta in theta_names for theta in user_provided.columns): + # if not all(theta in theta_names for theta in user_provided_df.columns): raise ValueError( "The user provided numpy array must have the same theta names as the model." ) # If all checks pass, return the user provided numpy array - theta_vals_multistart = user_provided - elif isinstance(user_provided, pd.DataFrame): + theta_vals_multistart = user_provided_df + elif isinstance(user_provided_df, pd.DataFrame): # Check if the user provided dataframe has the same number of rows as the number of restarts - if user_provided.shape[0] != n_restarts: + if user_provided_df.shape[0] != n_restarts: raise ValueError( "The user provided dataframe must have the same number of rows as the number of restarts." ) # Check if the user provided dataframe has the same number of columns as the number of theta names - if user_provided.shape[1] != len(theta_names): + if user_provided_df.shape[1] != len(theta_names): raise ValueError( "The user provided dataframe must have the same number of columns as the number of theta names." ) # Check if the user provided dataframe has the same theta names as the model # if not, raise an error - # if not all(theta in theta_names for theta in user_provided.columns): + # if not all(theta in theta_names for theta in user_provided_df.columns): raise ValueError( "The user provided dataframe must have the same theta names as the model." ) # If all checks pass, return the user provided dataframe - theta_vals_multistart = user_provided.iloc[ + theta_vals_multistart = user_provided_df.iloc[ 0 : len(initial_theta) ].values else: raise ValueError( - "The user must provide a numpy array or pandas dataframe from a previous attempt to use the 'user_provided' method." + "The user must provide a numpy array or pandas dataframe from a previous attempt to use the 'user_provided_values' method." ) else: raise ValueError( - "Invalid sampling method. Choose 'uniform', 'latin_hypercube', 'sobol' or 'user_provided'." + "Invalid sampling method. Choose 'uniform_random', 'latin_hypercube', 'sobol_sampling' or 'user_provided_values'." ) if ( @@ -606,10 +600,10 @@ def _generate_initial_theta( # Create a DataFrame where each row is an initial theta vector for a restart, # columns are theta_names, and values are the initial theta values for each restart - if multistart_sampling_method == "user_provided": - # If user_provided is a DataFrame, use its columns and values directly - if isinstance(user_provided, pd.DataFrame): - df_multistart = user_provided.copy() + if multistart_sampling_method == "user_provided_values": + # If user_provided_values is a DataFrame, use its columns and values directly + if isinstance(user_provided_df, pd.DataFrame): + df_multistart = user_provided_df.copy() df_multistart.columns = theta_names else: df_multistart = pd.DataFrame(theta_vals_multistart, columns=theta_names) @@ -1112,7 +1106,7 @@ def theta_est_multistart( self, n_restarts=20, buffer=10, - multistart_sampling_method="uniform", + multistart_sampling_method="uniform_random", user_provided=None, seed=None, save_results=False, @@ -1129,8 +1123,8 @@ def theta_est_multistart( n_restarts: int, optional Number of restarts for multistart. Default is 1. multistart_sampling_method: string, optional - Method used to sample theta values. Options are "uniform", "latin_hypercube", "sobol", or "user_provided". - Default is "uniform". + Method used to sample theta values. Options are "uniform_random", "latin_hypercube", "sobol", or "user_provided". + Default is "uniform_random". buffer: int, optional Number of iterations to save results dynamically. Default is 10. user_provided: pd.DataFrame or np.ndarray, optional @@ -1207,6 +1201,12 @@ def theta_est_multistart( # # Create a fresh model for each restart # parmest_model = self._create_parmest_model(experiment_number=0) theta_vals_current = theta_vals.iloc[i, :].to_dict() + # If theta_vals is provided, use it to set the current theta values + # # Convert values to a list + # theta_vals_current = list(theta_vals.iloc[i, :]) + + # # Update the model with the current theta values + # update_model_from_suffix(parmest_model, 'experiment_inputs', theta_vals_current) # # Set current theta values in the model # for name, value in theta_vals_current.items(): From 1be2d9e98f965d0f381f0d09a11784f6049c965c Mon Sep 17 00:00:00 2001 From: Stephen Cini <114932899+sscini@users.noreply.github.com> Date: Mon, 30 Jun 2025 13:45:20 -0400 Subject: [PATCH 19/23] Addressed some reviewer comments and ran black. --- pyomo/contrib/parmest/parmest.py | 66 ++++++++++++++------------------ 1 file changed, 28 insertions(+), 38 deletions(-) diff --git a/pyomo/contrib/parmest/parmest.py b/pyomo/contrib/parmest/parmest.py index 897e19d35ac..5a937683cd0 100644 --- a/pyomo/contrib/parmest/parmest.py +++ b/pyomo/contrib/parmest/parmest.py @@ -487,12 +487,6 @@ def _generate_initial_theta( """ Generate initial theta values for multistart optimization using selected sampling method. """ - if n_restarts == 1: - # If only one restart, return an empty list - return print( - "No multistart optimization needed. Please use normal theta_est()" - ) - # Get the theta names and initial theta values theta_names = self._return_theta_names() initial_theta = [parmest_model.find_component(name)() for name in theta_names] @@ -539,26 +533,7 @@ def _generate_initial_theta( # Add user provided dataframe option if user_provided_df is not None: - if isinstance(user_provided_df, np.ndarray): - # Check if the user provided numpy array has the same number of rows as the number of restarts - if user_provided_df.shape[0] != n_restarts: - raise ValueError( - "The user provided numpy array must have the same number of rows as the number of restarts." - ) - # Check if the user provided numpy array has the same number of columns as the number of theta names - if user_provided_df.shape[1] != len(theta_names): - raise ValueError( - "The user provided numpy array must have the same number of columns as the number of theta names." - ) - # Check if the user provided numpy array has the same theta names as the model - # if not, raise an error - # if not all(theta in theta_names for theta in user_provided_df.columns): - raise ValueError( - "The user provided numpy array must have the same theta names as the model." - ) - # If all checks pass, return the user provided numpy array - theta_vals_multistart = user_provided_df - elif isinstance(user_provided_df, pd.DataFrame): + if isinstance(user_provided_df, pd.DataFrame): # Check if the user provided dataframe has the same number of rows as the number of restarts if user_provided_df.shape[0] != n_restarts: raise ValueError( @@ -581,7 +556,7 @@ def _generate_initial_theta( ].values else: raise ValueError( - "The user must provide a numpy array or pandas dataframe from a previous attempt to use the 'user_provided_values' method." + "The user must provide a pandas dataframe to use the 'user_provided_values' method." ) else: @@ -594,6 +569,7 @@ def _generate_initial_theta( or multistart_sampling_method == "latin_hypercube" ): # Scale the samples to the range of the lower and upper bounds for each theta in theta_names + # The samples are in the range [0, 1], so we scale them to the range of the lower and upper bounds theta_vals_multistart = np.array( [lower_bound + (upper_bound - lower_bound) * theta for theta in samples] ) @@ -1126,7 +1102,7 @@ def theta_est_multistart( Method used to sample theta values. Options are "uniform_random", "latin_hypercube", "sobol", or "user_provided". Default is "uniform_random". buffer: int, optional - Number of iterations to save results dynamically. Default is 10. + Number of iterations to save results dynamically if save_results=True. Default is 10. user_provided: pd.DataFrame or np.ndarray, optional User provided array or dataframe of theta values for multistart optimization. seed: int, optional @@ -1158,18 +1134,32 @@ def theta_est_multistart( "Multistart is not supported in the deprecated parmest interface" ) - assert isinstance(n_restarts, int) - assert isinstance(multistart_sampling_method, str) - assert isinstance(solver, str) - assert isinstance(return_values, list) + # Validate input types + if not isinstance(n_restarts, int): + raise TypeError("n_restarts must be an integer") + if not isinstance(multistart_sampling_method, str): + raise TypeError("multistart_sampling_method must be a string") + if not isinstance(solver, str): + raise TypeError("solver must be a string") + if not isinstance(return_values, list): + raise TypeError("return_values must be a list") + + if n_restarts <= 1: + # If n_restarts is 1 or less, no multistart optimization is needed + logger.warning( + "No multistart optimization needed. Please use normal theta_est()." + ) + return self.theta_est( + solver=solver, return_values=return_values, calc_cov=False, cov_n=None + ) if n_restarts > 1 and multistart_sampling_method is not None: # Find the initialized values of theta from the labeled parmest model # and the theta names from the estimator object - # print statement to indicate multistart optimization is starting - print( + # logger statement to indicate multistart optimization is starting + logger.info( f"Starting multistart optimization with {n_restarts} restarts using {multistart_sampling_method} sampling method." ) @@ -1264,7 +1254,7 @@ def theta_est_multistart( best_objectiveval = objectiveval best_theta = converged_theta.values - print( + logger.info( f"Restart {i+1}/{n_restarts}: Objective Value = {final_objectiveval}, Theta = {converged_theta}" ) @@ -1294,19 +1284,19 @@ def theta_est_multistart( ) # Diagnostic: print the table after each restart - # print(results_df) + logger.debug(results_df) # Add buffer to save the dataframe dynamically, if save_results is True if save_results and (i + 1) % buffer == 0: mode = 'w' if i + 1 == buffer else 'a' header = i + 1 == buffer results_df.to_csv(file_name, mode=mode, header=header, index=False) - print(f"Intermediate results saved after {i + 1} iterations.") + logger.info(f"Intermediate results saved after {i + 1} iterations.") # Final save after all iterations if save_results: results_df.to_csv(file_name, mode='a', header=False, index=False) - print("Final results saved.") + logger.info("Final results saved.") return results_df, best_theta, best_objectiveval From 05381c5e921960f6ae3c605de6a60c5aa44ed986 Mon Sep 17 00:00:00 2001 From: Stephen Cini <114932899+sscini@users.noreply.github.com> Date: Sun, 6 Jul 2025 17:37:49 -0400 Subject: [PATCH 20/23] Updated argument for theta_est_multistart --- pyomo/contrib/parmest/parmest.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/pyomo/contrib/parmest/parmest.py b/pyomo/contrib/parmest/parmest.py index 9f891875641..337881144de 100644 --- a/pyomo/contrib/parmest/parmest.py +++ b/pyomo/contrib/parmest/parmest.py @@ -1082,7 +1082,7 @@ def theta_est_multistart( n_restarts=20, buffer=10, multistart_sampling_method="uniform_random", - user_provided=None, + user_provided_df=None, seed=None, save_results=False, theta_vals=None, @@ -1102,7 +1102,7 @@ def theta_est_multistart( Default is "uniform_random". buffer: int, optional Number of iterations to save results dynamically if save_results=True. Default is 10. - user_provided: pd.DataFrame or np.ndarray, optional + user_provided_df: pd.DataFrame, optional User provided array or dataframe of theta values for multistart optimization. seed: int, optional Random seed for reproducibility. @@ -1171,7 +1171,7 @@ def theta_est_multistart( seed=seed, n_restarts=n_restarts, multistart_sampling_method=multistart_sampling_method, - user_provided=user_provided, + user_provided_df=user_provided_df, ) results_df = pd.DataFrame(results_df) # Extract theta_vals from the dataframe From 07ae1e83a322148f21499317034995597ed57297 Mon Sep 17 00:00:00 2001 From: Stephen Cini <114932899+sscini@users.noreply.github.com> Date: Tue, 8 Jul 2025 14:26:27 -0400 Subject: [PATCH 21/23] Addressed majority of review comments. State before 7/8 dev meeting --- pyomo/contrib/parmest/parmest.py | 70 ++++++++++++++++---------------- 1 file changed, 36 insertions(+), 34 deletions(-) diff --git a/pyomo/contrib/parmest/parmest.py b/pyomo/contrib/parmest/parmest.py index 0b33e28c58b..6c7b18c63b1 100644 --- a/pyomo/contrib/parmest/parmest.py +++ b/pyomo/contrib/parmest/parmest.py @@ -68,6 +68,7 @@ # Add imports for HierchicalTimer import time from pyomo.common.timing import TicTocTimer +from enum import Enum from pyomo.common.deprecation import deprecated from pyomo.common.deprecation import deprecation_warning @@ -250,9 +251,15 @@ def SSE(model): return expr -'''Adding pseudocode for draft implementation of the estimator class, -incorporating multistart. -''' +class MultistartSamplingMethodLib(Enum): + """ + Enum class for multistart sampling methods. + """ + + uniform_random = "uniform_random" + latin_hypercube = "latin_hypercube" + sobol_sampling = "sobol_sampling" + user_provided_values = "user_provided_values" class Estimator(object): @@ -486,19 +493,20 @@ def _generate_initial_theta( """ Generate initial theta values for multistart optimization using selected sampling method. """ - # Get the theta names and initial theta values - theta_names = self._return_theta_names() - initial_theta = [parmest_model.find_component(name)() for name in theta_names] + # Locate the unknown parameters in the model from the suffix + suffix_params = parmest_model.unknown_parameters + + # Get the VarData objects from the suffix + theta_vars = list(suffix_params.keys()) + + # Extract names, starting values, and bounds for the theta variables + theta_names = [v.name for v in theta_vars] + initial_theta = np.array([v.value for v in theta_vars]) + lower_bound = np.array([v.lb for v in theta_vars]) + upper_bound = np.array([v.ub for v in theta_vars]) - # Get the lower and upper bounds for the theta values - lower_bound = np.array( - [parmest_model.find_component(name).lb for name in theta_names] - ) - upper_bound = np.array( - [parmest_model.find_component(name).ub for name in theta_names] - ) # Check if the lower and upper bounds are defined - if any(bound is None for bound in lower_bound) and any( + if any(bound is None for bound in lower_bound) or any( bound is None for bound in upper_bound ): raise ValueError( @@ -522,7 +530,7 @@ def _generate_initial_theta( samples = sampler.random(n=n_restarts) # Resulting samples should be size (n_restarts, len(theta_names)) - elif multistart_sampling_method == "sobol": + elif multistart_sampling_method == "sobol_sampling": sampler = scipy.stats.qmc.Sobol(d=len(theta_names), seed=seed) # Generate theta values using Sobol sampling # The first value of the Sobol sequence is 0, so we skip it @@ -545,7 +553,7 @@ def _generate_initial_theta( ) # Check if the user provided dataframe has the same theta names as the model # if not, raise an error - # if not all(theta in theta_names for theta in user_provided_df.columns): + if not all(theta in theta_names for theta in user_provided_df.columns): raise ValueError( "The user provided dataframe must have the same theta names as the model." ) @@ -564,7 +572,7 @@ def _generate_initial_theta( ) if ( - multistart_sampling_method == "sobol" + multistart_sampling_method == "sobol_sampling" or multistart_sampling_method == "latin_hypercube" ): # Scale the samples to the range of the lower and upper bounds for each theta in theta_names @@ -613,6 +621,7 @@ def _Q_opt( return_values=[], bootlist=None, calc_cov=False, + multistart=False, cov_n=None, ): """ @@ -774,6 +783,8 @@ def _Q_opt( if calc_cov: return objval, thetavals, cov + if multistart: + return objval, thetavals, solve_result else: return objval, thetavals @@ -1098,7 +1109,7 @@ def theta_est_multistart( n_restarts: int, optional Number of restarts for multistart. Default is 1. multistart_sampling_method: string, optional - Method used to sample theta values. Options are "uniform_random", "latin_hypercube", "sobol", or "user_provided". + Method used to sample theta values. Options are "uniform_random", "latin_hypercube", "sobol_sampling", or "user_provided_values". Default is "uniform_random". buffer: int, optional Number of iterations to save results dynamically if save_results=True. Default is 10. @@ -1213,16 +1224,16 @@ def theta_est_multistart( bootlist=None, solver=solver, return_values=return_values, + multistart=True, ) # Unpack results - objectiveval, converged_theta = qopt_result + objectiveval, converged_theta, solver_info = qopt_result - # Since _Q_opt does not return the solver result object, we cannot check - # solver termination condition directly here. Instead, we can assume - # that if converged_theta contains NaN, the solve failed. - if converged_theta.isnull().any(): - solver_termination = "not successful" + # Added an extra option to Q_opt to return the full solver result if multistart=True + solver_termination = solver_info.solver.termination_condition + if solver_termination != pyo.TerminationCondition.optimal: + # If the solver did not converge, set the converged theta to NaN solve_time = np.nan final_objectiveval = np.nan init_objectiveval = np.nan @@ -1232,15 +1243,6 @@ def theta_est_multistart( # Use the _Q_at_theta method to evaluate the objective at these theta values init_objectiveval, _, _ = self._Q_at_theta(theta_vals_current) final_objectiveval = objectiveval - solver_termination = "successful" - - # plan to add solve time if available, @Reviewers, recommendations on how from current pyomo examples would - # be appreciated - solve_time = ( - converged_theta.solve_time - if hasattr(converged_theta, 'solve_time') - else np.nan - ) # # Check if the objective value is better than the best objective value # # Set a very high initial best objective value @@ -1264,7 +1266,7 @@ def theta_est_multistart( # Fill converged theta values for j, name in enumerate(theta_names): results_df.at[i, f'converged_{name}'] = ( - converged_theta[j] + converged_theta.iloc[j] if not np.isnan(converged_theta_vals[i, j]) else np.nan ) From 33d838ff5a81f3ef27ed3cabe55bd9d7bf6c4b10 Mon Sep 17 00:00:00 2001 From: Stephen Cini <114932899+sscini@users.noreply.github.com> Date: Tue, 8 Jul 2025 14:37:33 -0400 Subject: [PATCH 22/23] Fixing conflict --- pyomo/contrib/doe/utils.py | 41 -------------------------------------- 1 file changed, 41 deletions(-) diff --git a/pyomo/contrib/doe/utils.py b/pyomo/contrib/doe/utils.py index bd991747319..2af5472703b 100644 --- a/pyomo/contrib/doe/utils.py +++ b/pyomo/contrib/doe/utils.py @@ -101,44 +101,3 @@ def rescale_FIM(FIM, param_vals): # # return param_list -# Adding utility to update parameter values in a model based on the suffix -def update_model_from_suffix(model, suffix_name, values): - """"" - Iterate over the components (variables or parameters) referenced by the - given suffix in the model, and assign each a new value from the provided iterable. - - Parameters - ---------- - model : pyomo.environ.ConcreteModel - The Pyomo model containing the suffix and components to update. - suffix_name : str - The name of the Suffix attribute on the model whose items will be updated. - Must be one of: 'experiment_outputs', 'experiment_inputs', 'unknown_parameters', or 'measurement_error'. - values : iterable of numbers - The new values to assign to each component referenced by the suffix. The length of this - iterable must match the number of items in the suffix. - - """ - # Allowed suffix names - allowed = { - 'experiment_outputs', 'experiment_inputs', - 'unknown_parameters', 'measurement_error' - } - # Validate input is an allowed suffix name - if suffix_name not in allowed: - raise ValueError(f"suffix_name must be one of {sorted(allowed)}") - # Check if the model has the specified suffix - suffix_obj = getattr(model, suffix_name, None) - if suffix_obj is None: - raise AttributeError(f"Model has no attribute '{suffix_name}'") - # Check if the suffix is a Suffix object - items = list(suffix_obj.items()) - if len(items) != len(values): - raise ValueError("values length does not match suffix length") - # Set the new values for the suffix items - for (comp, _), new_val in zip(items, values): - # Update the variable/parameter itself if it is VarData or ParamData - if isinstance(comp, (VarData, ParamData)): - comp.set_value(new_val) - else: - raise TypeError(f"Unsupported component type: {type(comp)}") From e7b2df189fa5db48ffeaae4977225643d24a1eca Mon Sep 17 00:00:00 2001 From: Stephen Cini <114932899+sscini@users.noreply.github.com> Date: Thu, 17 Jul 2025 14:03:53 -0400 Subject: [PATCH 23/23] Added in TODO items based on Dan morning meeting --- pyomo/contrib/parmest/parmest.py | 14 ++++++++++++-- 1 file changed, 12 insertions(+), 2 deletions(-) diff --git a/pyomo/contrib/parmest/parmest.py b/pyomo/contrib/parmest/parmest.py index 6c7b18c63b1..0ee63e8cb8e 100644 --- a/pyomo/contrib/parmest/parmest.py +++ b/pyomo/contrib/parmest/parmest.py @@ -478,6 +478,7 @@ def TotalCost_rule(model): return parmest_model + # TODO: Make so this generates the initial DATAFRAME, not the entire list of values. # Make new private method, _generate_initial_theta: # This method will be used to generate the initial theta values for multistart # optimization. It will take the theta names and the initial theta values @@ -614,6 +615,11 @@ def _instance_creation_callback(self, experiment_number=None, cb_data=None): model = self._create_parmest_model(experiment_number) return model + # TODO: Add a way to pass in a parmest_model to this function, currently cannot + # access the model within the build function. + + # I need to check, if I use the update model utility BEFORE calling _Q_opt, does it still + # work? If so, then I can remove the parmest_model argument. def _Q_opt( self, ThetaVals=None, @@ -1088,12 +1094,16 @@ def theta_est( cov_n=cov_n, ) + # TODO: Make the user provide a list of values, not the whole data frame + # TODO: Add a way to print the empty data_frame before solve so it can be previewed beforehand + # TODO: Fix so the theta values are generated at each iteration, not all beforehand in _generate_initial_theta + # Fix _generate_initial_theta to return an empty DataFrame first + # TODO: Add save model option to save the model after each iteration or at the end of the multistart def theta_est_multistart( self, n_restarts=20, - buffer=10, multistart_sampling_method="uniform_random", - user_provided_df=None, + user_provided_list=None, seed=None, save_results=False, theta_vals=None,