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Failed

run_tests.TestSimulation_mosek_cli.test_table_diff (from run_tests.TestSimulation_mosek_cli-20200714174902)

Failing for the past 1 build (Since Failed #237 )
Took 6.8 sec.

Error Message

unsupported operand type(s) for -: 'list' and 'list'

Stacktrace

Traceback (most recent call last):
  File "/jenkins/workspace/CE_gplibrary_Push_research_models/mosek/robust/robust/testing/t_simulation.py", line 107, in test_table_diff
    self.assertAlmostEqual(open(origfilename, 'r').readlines(), open(filename, 'r').readlines())
  File "/usr/lib/python3.5/unittest/case.py", line 861, in assertAlmostEqual
    if round(abs(second-first), places) == 0:
TypeError: unsupported operand type(s) for -: 'list' and 'list'
		

Standard Output

SGP not convergent: Cost rose by 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 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 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 0.51% 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 0.0033%. 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 0.042%. 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.

Final solution let signomial constraints slacken by 0.023%. 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 0.058% 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 0.0045%. 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 0.0017% 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 0.042% 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 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 8.1% 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 0.46% 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 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 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 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 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 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.

Solution check warning: Dual cost 630406.3137522705 does not match primal cost 630186.6
Final solution let signomial constraints slacken by 1.8%. 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 0.07% 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 0.022% 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 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 8.1% 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 0.46% 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 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 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.

	

Standard Error