Passed

gpkit.tests.from_paths.TestFiles.test_gpkitmodels_SP_SimPleAC_SimPleAC_mission_py_cvxopt (from gpkit.tests.from_paths.TestFiles-20210321174637)

Standard Output

Using solver 'cvxopt'
 for 11 free variables
  in 14 posynomial inequalities.
Solving took 0.0268 seconds.
Using solver 'cvxopt'
 for 9 free variables
  in 15 posynomial inequalities.
Solving took 0.00849 seconds.
Using solver 'cvxopt'
 for 25 free variables
  in 35 posynomial inequalities.
Solving took 0.0175 seconds.
Using solver 'cvxopt'
 for 25 free variables
  in 35 posynomial inequalities.
Solving took 0.0122 seconds.
Starting a sequence of GP solves
 for 53 free variables
  in 11 locally
...[truncated 4873 chars]...
econds.
Solved cost was 5717.

GP Solve 2
Using solver 'cvxopt'
 for 21 free variables
  in 22 posynomial inequalities.
Solving took 0.0111 seconds.
Solved cost was 4538.

GP Solve 3
Using solver 'cvxopt'
 for 21 free variables
  in 22 posynomial inequalities.
Solving took 0.0167 seconds.
Solved cost was 4536.

GP Solve 4
Using solver 'cvxopt'
 for 21 free variables
  in 22 posynomial inequalities.
Solving took 0.0136 seconds.
Solved cost was 4536.

Solving took 0.0558 seconds and 4 GP solves.
	

Standard Error

/Users/jenkins/workspace/CE_gplibrary_Push_research_models/cvxopt/venv2_gpkit/lib/python3.7/site-packages/numpy/core/_asarray.py:171: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.
  return array(a, dtype, copy=False, order=order, subok=True)
/Users/jenkins/workspace/CE_gplibrary_Push_research_models/cvxopt/venv2_gpkit/lib/python3.7/site-packages/numpy/core/_asarray.py:171: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndar
...[truncated 3409 chars]...
nit of the quantity is stripped when downcasting to ndarray.
  return ufunc.reduce(obj, axis, dtype, out, **passkwargs)
/Users/jenkins/workspace/CE_gplibrary_Push_research_models/cvxopt/gpkit/gpkit/constraints/sgp.py:178: UserWarning: SGP not convergent: Cost rose by 6.1% 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.
  % (100*(cost - prevcost)/prevcost, len(self.gps)))