Failed

run_tests.TestSimulation_mosek_conif.test_table_diff (from run_tests.TestSimulation_mosek_conif-20200302201219)

Error Message

flat() got an unexpected keyword argument 'constraintsets'

Stacktrace

Traceback (most recent call last):
  File "/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/reynolds/optimizer/mosek/robust/robust/testing/t_simulation.py", line 66, in test_table_diff
    bm = RobustModel(m, 'box', gamma=gamma, twoTerm = True, boyd = False, simpleModel = False)
  File "/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/reynolds/optimizer/mosek/robust/robust/robust.py", line 132, in __init__
    all_constraints = model.flat(constraintsets=False)
TypeError: flat() got an unexpected keyword argument 'constraintsets'
		

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