Failed

run_tests.TestSimulation_mosek_conif.test_table_diff (from run_tests.TestSimulation_mosek_conif-20200303092753)

Failing for the past 1 build (Since #216 )

Took 3.1 sec.

Error Message

Lists differ: ['L/D[73 chars]& 7.67e+00 & 8.27e+00\n', 'Re & 5.01e+06 & 5.7[644 chars]3\n'] != ['L/D[73 chars]& 7.66e+00 & 8.27e+00\n', 'Re & 5.01e+06 & 5.7[644 chars]3\n']  First differing element 1: 'A & 8.91e+00 & 7.54e+00 & 7.67e+00 & 8.27e+00\n' 'A & 8.91e+00 & 7.54e+00 & 7.66e+00 & 8.27e+00\n'  Diff is 941 characters long. Set self.maxDiff to None to see it.

Stacktrace

Traceback (most recent call last):
  File "/jenkins/workspace/CE_gplibrary_Push_research_models/mosek/robust/robust/testing/t_simulation.py", line 101, in test_table_diff
    self.assertEqual(open(origfilename, 'r').readlines(), open(filename, 'r').readlines())
AssertionError: Lists differ: ['L/D[73 chars]& 7.67e+00 & 8.27e+00\n', 'Re & 5.01e+06 & 5.7[644 chars]3\n'] != ['L/D[73 chars]& 7.66e+00 & 8.27e+00\n', 'Re & 5.01e+06 & 5.7[644 chars]3\n']

First differing element 1:
'A & 8.91e+00 & 7.54e+00 & 7.67e+00 & 8.27e+00\n'
'A & 8.91e+00 & 7.54e+00 & 7.66e+00 & 8.27e+00\n'

Diff is 941 characters long. Set self.maxDiff to None to see it.

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 0.037% 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.

SGP not convergent: Cost rose by 2.7% 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.

Final solution let signomial constraints slacken by 0.071%. 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 0.25% 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.

SGP not convergent: Cost rose by 0.041% 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.

Final solution let signomial constraints slacken by 0.027%. 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 0.01% 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.

SGP not convergent: Cost rose by 0.28% 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.

SGP not convergent: Cost rose by 0.35% 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.

SGP not convergent: Cost rose by 9.4% 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 0.33% 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.

SGP not convergent: Cost rose by 0.1% 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.

Final solution let signomial constraints slacken by 0.024%. 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 2.2% 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.

SGP not convergent: Cost rose by 0.14% on GP solve 6. 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 0.13% on GP solve 9. 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.

Solution check warning: Sum of nu^T * A did not vanish.
Final solution let signomial constraints slacken by 15%. 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 0.29% 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.

SGP not convergent: Cost rose by 0.033% on GP solve 6. 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.

Failed

Error Message

Stacktrace

Standard Output

Standard Error