Failed
run_tests.TestSimulation_mosek_conif.test_table_diff (from run_tests.TestSimulation_mosek_conif-20200714173124)
Failing for the past 1 build
(Since Failed
)
Error Message
unsupported operand type(s) for -: 'list' and 'list'
Stacktrace
Traceback (most recent call last): File "/Users/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/local/opt/python/Frameworks/Python.framework/Versions/3.7/lib/python3.7/unittest/case.py", line 883, in assertAlmostEqual diff = abs(first - second) 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 0.011% 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 0.03% 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. Final solution let signomial constraints slacken by 0.022%. 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% 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 0.03%. 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 1.2% 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. Final solution let signomial constraints slacken by 0.068%. 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 0.00027% 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 0.0033% 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 0.0026% 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 0.024% 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.