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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,272 +1,6 @@ | ||
| # Standard Python modules | ||
| import datetime | ||
| import os | ||
| import time | ||
| try: | ||
| from paropt.paropt_pyoptsparse import ParOptSparse as ParOpt | ||
| except: | ||
|
|
||
| # External modules | ||
| import numpy as np | ||
|
|
||
| # isort: off | ||
| # Attempt to import mpi4py. | ||
| # If PYOPTSPARSE_REQUIRE_MPI is set to a recognized positive value, attempt import | ||
| # and raise exception on failure. If set to anything else, no import is attempted. | ||
| if "PYOPTSPARSE_REQUIRE_MPI" in os.environ: | ||
| if os.environ["PYOPTSPARSE_REQUIRE_MPI"].lower() in ["always", "1", "true", "yes"]: | ||
| try: | ||
| from paropt import ParOpt as _ParOpt | ||
| from mpi4py import MPI | ||
| except ImportError: | ||
| _ParOpt = None | ||
| else: | ||
| _ParOpt = None | ||
| # If PYOPTSPARSE_REQUIRE_MPI is unset, attempt to import mpi4py. | ||
| # Since ParOpt requires mpi4py, if either _ParOpt or mpi4py is unavailable | ||
| # we disable the optimizer. | ||
| else: | ||
| try: | ||
| from paropt import ParOpt as _ParOpt | ||
| from mpi4py import MPI | ||
| except ImportError: | ||
| _ParOpt = None | ||
| # isort: on | ||
|
|
||
| # Local modules | ||
| from ..pyOpt_error import Error | ||
| from ..pyOpt_optimizer import Optimizer | ||
| from ..pyOpt_utils import INFINITY | ||
|
|
||
|
|
||
| class ParOpt(Optimizer): | ||
| """ | ||
| ParOpt optimizer class | ||
| ParOpt has the capability to handle distributed design vectors. | ||
| This is not replicated here since pyOptSparse does not have the | ||
| capability to handle this type of design problem. | ||
| """ | ||
|
|
||
| def __init__(self, raiseError=True, options={}): | ||
| name = "ParOpt" | ||
| category = "Local Optimizer" | ||
| if _ParOpt is None: | ||
| if raiseError: | ||
| raise Error("There was an error importing ParOpt") | ||
|
|
||
| # Create and fill-in the dictionary of default option values | ||
| self.defOpts = {} | ||
| paropt_default_options = _ParOpt.getOptionsInfo() | ||
| # Manually override the options with missing default values | ||
| paropt_default_options["ip_checkpoint_file"].default = "default.out" | ||
| paropt_default_options["problem_name"].default = "problem" | ||
| for option_name in paropt_default_options: | ||
| # Get the type and default value of the named argument | ||
| _type = None | ||
| if paropt_default_options[option_name].option_type == "bool": | ||
| _type = bool | ||
| elif paropt_default_options[option_name].option_type == "int": | ||
| _type = int | ||
| elif paropt_default_options[option_name].option_type == "float": | ||
| _type = float | ||
| else: | ||
| _type = str | ||
| default_value = paropt_default_options[option_name].default | ||
|
|
||
| # Set the entry into the dictionary | ||
| self.defOpts[option_name] = [_type, default_value] | ||
|
|
||
| self.set_options = {} | ||
| self.informs = {} | ||
| super().__init__(name, category, defaultOptions=self.defOpts, informs=self.informs, options=options) | ||
|
|
||
| # ParOpt requires a dense Jacobian format | ||
| self.jacType = "dense2d" | ||
|
|
||
| return | ||
|
|
||
| def __call__( | ||
| self, optProb, sens=None, sensStep=None, sensMode=None, storeHistory=None, hotStart=None, storeSens=True | ||
| ): | ||
| """ | ||
| This is the main routine used to solve the optimization | ||
| problem. | ||
| Parameters | ||
| ---------- | ||
| optProb : Optimization or Solution class instance | ||
| This is the complete description of the optimization problem | ||
| to be solved by the optimizer | ||
| sens : str or python Function. | ||
| Specifiy method to compute sensitivities. To | ||
| explictly use pyOptSparse gradient class to do the | ||
| derivatives with finite differenes use \'FD\'. \'sens\' | ||
| may also be \'CS\' which will cause pyOptSpare to compute | ||
| the derivatives using the complex step method. Finally, | ||
| \'sens\' may be a python function handle which is expected | ||
| to compute the sensitivities directly. For expensive | ||
| function evaluations and/or problems with large numbers of | ||
| design variables this is the preferred method. | ||
| sensStep : float | ||
| Set the step size to use for design variables. Defaults to | ||
| 1e-6 when sens is \'FD\' and 1e-40j when sens is \'CS\'. | ||
| sensMode : str | ||
| Use \'pgc\' for parallel gradient computations. Only | ||
| available with mpi4py and each objective evaluation is | ||
| otherwise serial | ||
| storeHistory : str | ||
| File name of the history file into which the history of | ||
| this optimization will be stored | ||
| hotStart : str | ||
| File name of the history file to "replay" for the | ||
| optimziation. The optimization problem used to generate | ||
| the history file specified in \'hotStart\' must be | ||
| **IDENTICAL** to the currently supplied \'optProb\'. By | ||
| identical we mean, **EVERY SINGLE PARAMETER MUST BE | ||
| IDENTICAL**. As soon as he requested evaluation point | ||
| from ParOpt does not match the history, function and | ||
| gradient evaluations revert back to normal evaluations. | ||
| storeSens : bool | ||
| Flag sepcifying if sensitivities are to be stored in hist. | ||
| This is necessay for hot-starting only. | ||
| """ | ||
| self.startTime = time.time() | ||
| self.callCounter = 0 | ||
| self.storeSens = storeSens | ||
|
|
||
| if len(optProb.constraints) == 0: | ||
| # If the problem is unconstrained, add a dummy constraint. | ||
| self.unconstrained = True | ||
| optProb.dummyConstraint = True | ||
|
|
||
| # Save the optimization problem and finalize constraint | ||
| # Jacobian, in general can only do on root proc | ||
| self.optProb = optProb | ||
| self.optProb.finalize() | ||
| # Set history/hotstart | ||
| self._setHistory(storeHistory, hotStart) | ||
| self._setInitialCacheValues() | ||
| self._setSens(sens, sensStep, sensMode) | ||
| blx, bux, xs = self._assembleContinuousVariables() | ||
| xs = np.maximum(xs, blx) | ||
| xs = np.minimum(xs, bux) | ||
|
|
||
| # The number of design variables | ||
| n = len(xs) | ||
|
|
||
| oneSided = True | ||
|
|
||
| if self.unconstrained: | ||
| m = 0 | ||
| else: | ||
| indices, blc, buc, fact = self.optProb.getOrdering(["ne", "le", "ni", "li"], oneSided=oneSided) | ||
| m = len(indices) | ||
| self.optProb.jacIndices = indices | ||
| self.optProb.fact = fact | ||
| self.optProb.offset = buc | ||
|
|
||
| if self.optProb.comm.rank == 0: | ||
|
|
||
| class Problem(_ParOpt.Problem): | ||
| def __init__(self, ptr, n, m, xs, blx, bux): | ||
| super().__init__(MPI.COMM_SELF, n, m) | ||
| self.ptr = ptr | ||
| self.n = n | ||
| self.m = m | ||
| self.xs = xs | ||
| self.blx = blx | ||
| self.bux = bux | ||
| self.fobj = 0.0 | ||
| return | ||
|
|
||
| def getVarsAndBounds(self, x, lb, ub): | ||
| """Get the variable values and bounds""" | ||
| # Find the average distance between lower and upper bound | ||
| bound_sum = 0.0 | ||
| for i in range(len(x)): | ||
| if self.blx[i] <= -INFINITY or self.bux[i] >= INFINITY: | ||
| bound_sum += 1.0 | ||
| else: | ||
| bound_sum += self.bux[i] - self.blx[i] | ||
| bound_sum = bound_sum / len(x) | ||
|
|
||
| for i in range(len(x)): | ||
| x[i] = self.xs[i] | ||
| lb[i] = self.blx[i] | ||
| ub[i] = self.bux[i] | ||
| if self.xs[i] <= self.blx[i]: | ||
| x[i] = self.blx[i] + 0.5 * np.min((bound_sum, self.bux[i] - self.blx[i])) | ||
| elif self.xs[i] >= self.bux[i]: | ||
| x[i] = self.bux[i] - 0.5 * np.min((bound_sum, self.bux[i] - self.blx[i])) | ||
|
|
||
| return | ||
|
|
||
| def evalObjCon(self, x): | ||
| """Evaluate the objective and constraint values""" | ||
| fobj, fcon, fail = self.ptr._masterFunc(x[:], ["fobj", "fcon"]) | ||
| self.fobj = fobj | ||
| return fail, fobj, -fcon | ||
|
|
||
| def evalObjConGradient(self, x, g, A): | ||
| """Evaluate the objective and constraint gradients""" | ||
| gobj, gcon, fail = self.ptr._masterFunc(x[:], ["gobj", "gcon"]) | ||
| g[:] = gobj[:] | ||
| for i in range(self.m): | ||
| A[i][:] = -gcon[i][:] | ||
| return fail | ||
|
|
||
| optTime = MPI.Wtime() | ||
|
|
||
| # Optimize the problem | ||
| problem = Problem(self, n, m, xs, blx, bux) | ||
| optimizer = _ParOpt.Optimizer(problem, self.set_options) | ||
| optimizer.optimize() | ||
| x, z, zw, zl, zu = optimizer.getOptimizedPoint() | ||
|
|
||
| # Set the total opt time | ||
| optTime = MPI.Wtime() - optTime | ||
|
|
||
| # Get the obective function value | ||
| fobj = problem.fobj | ||
|
|
||
| if self.storeHistory: | ||
| self.metadata["endTime"] = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S") | ||
| self.metadata["optTime"] = optTime | ||
| self.hist.writeData("metadata", self.metadata) | ||
| self.hist.close() | ||
|
|
||
| # Create the optimization solution. Note that the signs on the multipliers | ||
| # are switch since ParOpt uses a formulation with c(x) >= 0, while pyOpt | ||
| # uses g(x) = -c(x) <= 0. Therefore the multipliers are reversed. | ||
| sol_inform = {} | ||
| sol_inform["value"] = None | ||
| sol_inform["text"] = None | ||
|
|
||
| # If number of constraints is zero, ParOpt returns z as None. | ||
| # Thus if there is no constraints, should pass an empty list | ||
| # to multipliers instead of z. | ||
| if z is not None: | ||
| sol = self._createSolution(optTime, sol_inform, fobj, x[:], multipliers=-z) | ||
| else: | ||
| sol = self._createSolution(optTime, sol_inform, fobj, x[:], multipliers=[]) | ||
|
|
||
| # Indicate solution finished | ||
| self.optProb.comm.bcast(-1, root=0) | ||
| else: # We are not on the root process so go into waiting loop: | ||
| self._waitLoop() | ||
| sol = None | ||
|
|
||
| # Communication solution and return | ||
| sol = self._communicateSolution(sol) | ||
|
|
||
| return sol | ||
|
|
||
| def _on_setOption(self, name, value): | ||
| """ | ||
| Add the value to the set_options dictionary. | ||
| """ | ||
| self.set_options[name] = value | ||
| class ParOpt: | ||
| pass |