Passed

gpkit.tests.from_paths.TestFiles.test_gpkitmodels_GP_aircraft_wing_wing_test_py_cvxopt (from gpkit.tests.from_paths.TestFiles-20210412161813)

Standard Output

Using solver 'cvxopt'
 for 11 free variables
  in 14 posynomial inequalities.
Solving took 0.035 seconds.
Using solver 'cvxopt'
 for 9 free variables
  in 15 posynomial inequalities.
Solving took 0.0109 seconds.
Using solver 'cvxopt'
 for 25 free variables
  in 35 posynomial inequalities.
Solving took 0.0122 seconds.
Using solver 'cvxopt'
 for 25 free variables
  in 35 posynomial inequalities.
Solving took 0.0187 seconds.
Starting a sequence of GP solves
 for 53 free variables
  in 11 locally-G
...[truncated 4870 chars]...
econds.
Solved cost was 5717.

GP Solve 2
Using solver 'cvxopt'
 for 21 free variables
  in 22 posynomial inequalities.
Solving took 0.0193 seconds.
Solved cost was 4538.

GP Solve 3
Using solver 'cvxopt'
 for 21 free variables
  in 22 posynomial inequalities.
Solving took 0.0179 seconds.
Solved cost was 4536.

GP Solve 4
Using solver 'cvxopt'
 for 21 free variables
  in 22 posynomial inequalities.
Solving took 0.0175 seconds.
Solved cost was 4536.

Solving took 0.0745 seconds and 4 GP solves.
	

Standard Error

/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/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_gpkit_PR_research_models/buildnode/macys_VM/optimizer/cvxopt/venv2_gpkit/lib/python3.7/site-packages/numpy/core/_asarray.py:171: UnitStrippedWarning: The unit of the
...[truncated 3735 chars]...
ty is stripped when downcasting to ndarray.
  return ufunc.reduce(obj, axis, dtype, out, **passkwargs)
/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/cvxopt/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)))