Failed

run_tests.TestTwoTermApproximation_mosek_cli.test_check_if_permutation_exists (from run_tests.TestTwoTermApproximation_mosek_cli-20200302201058)

Error Message

'PosynomialInequality' object has no attribute 'as_posyslt1'

Stacktrace

Traceback (most recent call last):
  File "/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/reynolds/optimizer/mosek/robust/robust/testing/t_two_term_approximation.py", line 62, in test_check_if_permutation_exists
    for i, constraint in enumerate(data_constraints)]
  File "/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/reynolds/optimizer/mosek/robust/robust/testing/t_two_term_approximation.py", line 62, in <listcomp>
    for i, constraint in enumerate(data_constraints)]
AttributeError: 'PosynomialInequality' object has no attribute 'as_posyslt1'
		

Standard Output

SGP not convergent: Cost rose by 9% on iteration 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 iteration 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 iteration 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 iteration 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 12% on iteration 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 iteration 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 iteration 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 iteration 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 iteration 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 iteration 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 12% on iteration 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 iteration 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