def _test_mpc_1norm(self):
self.mpc_setup(objective='1norm')
from cvxpy_codegen_solver import cg_solve
var_dict, stats_dict = cg_solve(x0=self.x0_val,
A=self.A_val,
B=self.B_val)
self.assertAlmostEqualMatrices(self.x_opt, var_dict['x'])
def _run_test_atom(self, atom_name):
self.param_setup(atom=atom_name)
from cvxpy_codegen_solver import cg_solve
param_names = list(inspect.signature(cg_solve).parameters.keys())
params = dict()
for p in param_names:
params[p] = self.params[p]
var_dict, stats_dict = cg_solve(**params)
print(self.x_opt)
print(var_dict['x'])
self.assertAlmostEqualMatrices(self.x_opt, var_dict['x'])
def _test_optimal(self):
from cvxpy_codegen_solver import cg_solve
vars, stats = cg_solve()
self.assertEqual(stats['status'], 'optimal')
self.assertAlmostEqual(stats['objective'], 1.0)
def _test_unbounded(self):
from cvxpy_codegen_solver import cg_solve
vars, stats = cg_solve()
self.assertEqual(stats['status'], 'unbounded')
self.assertAlmostEqual(stats['objective'], -np.inf)
def _test_infeas(self):
from cvxpy_codegen_solver import cg_solve
vars, stats = cg_solve()
self.assertEqual(stats['status'], 'infeasible')
self.assertAlmostEqual(stats['objective'], np.inf)
def _test_least_squares(self):
self.mpc_setup()
from cvxpy_codegen_solver import cg_solve
var_dict, stats_dict = cg_solve(A=self.A_val, b=self.b_val)
self.assertAlmostEqualMatrices(self.x_opt, var_dict['x'])
