Failed

run_tests.TestSimulation_mosek_cli.test_table_diff (from run_tests.TestSimulation_mosek_cli-20200415151023)

Failing for the past 1 build (Since #82 )

Took 4 sec.

Error Message

Lists differ: ['L/D & 1.71e+01 & 1.66e+01 & 1.74e+01 & 1.77e+01\n'[722 chars]3\n'] != ['L/D & 1.7e+01 & 1.63e+01 & 1.71e+01 & 1.74e+01\n',[716 chars]3\n']  First differing element 0: 'L/D & 1.71e+01 & 1.66e+01 & 1.74e+01 & 1.77e+01\n' 'L/D & 1.7e+01 & 1.63e+01 & 1.71e+01 & 1.74e+01\n'  Diff is 2318 characters long. Set self.maxDiff to None to see it.

Stacktrace

Traceback (most recent call last):
  File "/jenkins/workspace/CE_robust_PR/mosek/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 & 1.71e+01 & 1.66e+01 & 1.74e+01 & 1.77e+01\n'[722 chars]3\n'] != ['L/D & 1.7e+01 & 1.63e+01 & 1.71e+01 & 1.74e+01\n',[716 chars]3\n']

First differing element 0:
'L/D & 1.71e+01 & 1.66e+01 & 1.74e+01 & 1.77e+01\n'
'L/D & 1.7e+01 & 1.63e+01 & 1.71e+01 & 1.74e+01\n'

Diff is 2318 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.

Solution check warning: Sum of nu^T * A did not vanish.
Final solution let signomial constraints slacken by 2.1%. 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 0.083% 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 3.1% 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 0.056%. 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 4.8% 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 0.0053% 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 0.067%. 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 0.019% 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 0.025% 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 0.051% 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 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 4.8% 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 23%. 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 0.051% on GP solve 7. 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 0.078%. 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)`.

Final solution let signomial constraints slacken by 0.74%. 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 0.05% 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 0.0078% 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.

Failed

Error Message

Stacktrace

Standard Output

Standard Error