Failed
run_tests.TestSimulation_mosek_cli.test_simulate (from run_tests.TestSimulation_mosek_cli-20200303000533)
Failing for the past 1 build
(Since Unstable
)
Error Message
0.09999999999999998 != 0.0
Stacktrace
Traceback (most recent call last): File "C:\Users\jenkins\workspace\CE_gpkit_PR_research_models\buildnode\windows10x64\optimizer\mosek\robust\robust\testing\t_simulation.py", line 44, in test_simulate self.assertEqual(simulation_results[keys[-1]][0], 0.) AssertionError: 0.09999999999999998 != 0.0
Standard Output
SGP not convergent: Cost rose by 9% on GP solve 2. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities. SGP not convergent: Cost rose by 9% on GP solve 2. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities. SGP not convergent: Cost rose by 9% on GP solve 2. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities. SGP not convergent: Cost rose by 0.013% on GP solve 4. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities. Final solution let signomial constraints slacken by 0.01%. Calling .localsolve with a higher `pccp_penalty` (it was 200 this time) will reduce final slack if the model is solvable with less. If you think it might not be, check by solving with `use_pccp=False, x0=(this model's final solution)`. SGP not convergent: Cost rose by 0.18% on GP solve 5. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities. SGP not convergent: Cost rose by 0.056% on GP solve 3. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities.