Merge pull request #1573 from opencobra/develop

Develop

test/verifiedTests/base/testSolvers/testGurobiQP.m

@@ -0,0 +1,112 @@
+%tests if Gurobi correctly solves a set of QP problems
+
+solvers = prepareTest('requiredSolvers',{'gurobi'});
+
+
+fprintf('Checking gurobi solver ...\n')
+
+clear params
+params.OutputFlag = 0;
+
+tol= 1e-6;
+
+c = [3; -4];
+b = [5; 0];
+Q = sparse([8, 1; 1, 8]);
+A = sparse([1, 1; -1, 1]);
+
+% Build model
+qp.Q = Q;
+qp.obj = c;
+qp.A = A;
+qp.rhs = b;
+qp.lb = [0, 0];
+qp.ub = [inf, inf];
+qp.modelsense = 'min';
+qp.sense = ['<'; '<'];
+
+% Solve
+result = gurobi(qp,params);
+
+% Get primal/dual values
+x = result.x;
+lam = result.pi;
+
+fprintf('%s\n','Problem qp')
+% Check optimality conditions
+disp('Check 2*Q*x + c - A''*lam = 0 (stationarity):');
+disp(2*Q*x + c - A'*lam);
+assert(norm(2*Q*x + c - A'*lam,inf)<1e-8)
+
+disp('Check A*x - b <= 0 (primal feasibility):');
+disp(A*x - b);
+assert(all((A*x - b)<=0))
+
+disp('Check x >= 0 (primal feasibility):');
+disp(x);
+assert(all(x>0))
+
+disp('Check lam <= 0 (dual feasibility):');
+disp(lam);
+assert(all(lam<=0))
+
+disp('Check lam''*(A*x - b) = 0 (complementarity):');
+disp(lam'*(A*x - b))
+assert(norm(lam'*(A*x - b),inf)<1e-8)
+
+QPproblem2.Q = sparse([1, 0, 0; 0, 1, 0; 0, 0, 1]); 
+QPproblem2.modelsense = 'min';
+QPproblem2.obj = -1*[0, 0, 0]';
+QPproblem2.A = sparse([1, -1, -1 ; 0, 0, 1]);
+QPproblem2.rhs = [0, 5]'; 
+QPproblem2.lb = [0, -inf, 0]';
+QPproblem2.ub = [inf, inf, inf]';
+QPproblem2.sense = ['='; '='];
+
+result = gurobi(QPproblem2,params);
+
+fprintf('%s\n%s%g\n','QPproblem2','Optimal objective is: ', result.objval)
+fprintf('%s\n','optimal primal is: ')
+disp(result.x)
+assert(abs(result.objval - 37.5)<tol)
+assert(all((result.x - [2.5;-2.5;5])<tol))
+fprintf('\n')
+
+QPproblem3.Q = sparse([1, 0, 0; 0, 1, 0; 0, 0, 1]); 
+QPproblem3.modelsense = 'min'; 
+QPproblem3.obj = -1*[1, 1, 1]'; 
+QPproblem3.A = sparse([1, -1, 0 ; 0, 1, -1]); 
+QPproblem3.rhs = [0, 0]'; 
+QPproblem3.lb = [0, 0, 0]';
+QPproblem3.ub = [inf, inf, inf]';
+QPproblem3.sense = ['='; '='];
+
+result = gurobi(QPproblem3,params);
+
+fprintf('%s\n%s%g\n','QPproblem3','Optimal objective is: ', result.objval)
+fprintf('%s\n','optimal primal is: ')
+disp(result.x)
+assert(abs(result.objval + 0.75)<tol)
+assert(all((result.x - [0.5;0.5;0.5])<tol))
+fprintf('\n')
+
+QPproblem4.obj = [200; 400];
+QPproblem4.A = sparse([1 / 40, 1 / 60; 1 / 50, 1 / 50]);
+QPproblem4.b = [1; 1];
+QPproblem4.lb = [0; 0];
+QPproblem4.ub = [1; 1];
+QPproblem4.modelsense = 'max';
+QPproblem4.sense = ['<'; '<'];
+QPproblem4.Q = sparse(2,2);
+
+result = gurobi(QPproblem4,params);
+
+fprintf('%s\n%s%g\n','QPproblem4','Optimal objective is: ', result.objval)
+fprintf('%s\n','optimal primal is: ')
+disp(result.x)
+assert(abs(result.objval + 0)<tol)
+assert(all((result.x - [0;0])<tol))
+fprintf('\n')
+
+fprintf('Done.\n')
+

test/verifiedTests/base/testSolvers/testSolveCobraLP.m

@@ -19,6 +19,7 @@
 else
     useSolversIfAvailable = {'pdco'};
 end
+
 solvers = prepareTest('needsLP',true,'useSolversIfAvailable',useSolversIfAvailable);
 
 % save the current path