Failed
robust.testing.t_legacy.TestLegacy.test_simple_wing (from robust.testing.t_legacy.TestLegacy-20190924142921)
Failing for the past 1 build
(Since Failed
)
Error Message
Geometric Program is not fully bounded: C_f has no upper bound
Stacktrace
Traceback (most recent call last): File "/jenkins/workspace/robust_PullRequest/cvxopt/robust/testing/t_legacy.py", line 42, in test_simple_wing nominal_number_of_constraints, directly_uncertain_vars_subs) File "/jenkins/workspace/robust_PullRequest/cvxopt/robust/simulations/simulate.py", line 172, in print_variable_gamma_results number_of_time_average_solves) File "/jenkins/workspace/robust_PullRequest/cvxopt/robust/simulations/simulate.py", line 89, in simulate_robust_model linearizationTolerance=linearization_tolerance) File "/jenkins/workspace/robust_PullRequest/cvxopt/robust/robust.py", line 235, in robustsolve self.setup(verbosity, **options) File "/jenkins/workspace/robust_PullRequest/cvxopt/robust/robust.py", line 207, in setup feasible=True) File "/jenkins/workspace/robust_PullRequest/cvxopt/robust/robust.py", line 417, in find_number_of_piece_wise_linearization sol_upper = RobustModel.internalsolve(model_upper, verbosity=0) File "/jenkins/workspace/robust_PullRequest/cvxopt/robust/robust.py", line 454, in internalsolve return model.solve(verbosity=verbosity) File "/jenkins/workspace/robust_PullRequest/cvxopt/gpkit/gpkit/constraints/prog_factories.py", line 123, in solvefn self.program, progsolve = genfunction(self) File "/jenkins/workspace/robust_PullRequest/cvxopt/gpkit/gpkit/constraints/prog_factories.py", line 80, in programify prog = program(self.cost, self, constants, **kwargs) File "/jenkins/workspace/robust_PullRequest/cvxopt/gpkit/gpkit/constraints/gp.py", line 112, in __init__ + boundstrs) ValueError: Geometric Program is not fully bounded: C_f has no upper bound
Standard Output
SP is not converging! Last GP iteration had a higher cost (3e+03) than the previous one (2.7e+03). Results for each iteration are in (Model).program.results. If your model contains SignomialEqualities, note that convergence is not guaranteed: try replacing any SigEqs you can and solving again. SP is not converging! Last GP iteration had a higher cost (3e+03) than the previous one (2.7e+03). Results for each iteration are in (Model).program.results. If your model contains SignomialEqualities, note that convergence is not guaranteed: try replacing any SigEqs you can and solving again.