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def test_infeasible(self):
np.random.seed(0)
c = np.ones(1)
b = np.array([1.0, -1.0])
A = sparse.csc_matrix(np.ones((2, 1)))
cone_dims = {"f": 2}
with self.assertRaises(cone_prog.SolverError, msg='Solver ecos returned status Infeasible'):
cone_prog.solve_and_derivative(A, b, c, cone_dims, solver="ECOS")
data["s"] = warm_start[2]
kwargs.setdefault("verbose", False)
result = scs.solve(data, cone_dict, **kwargs)
status = result["info"]["status"]
if status == "Solved/Inaccurate" and "acceleration_lookback" not in kwargs:
# anderson acceleration is sometimes unstable
result = scs.solve(data, cone_dict, acceleration_lookback=0, **kwargs)
status = result["info"]["status"]
if status == "Solved/Inaccurate":
warnings.warn("Solved/Inaccurate.")
elif status != "Solved":
if raise_on_error:
raise SolverError("Solver scs returned status %s" % status)
else:
result["D"] = None
result["DT"] = None
return result
x = result["x"]
y = result["y"]
s = result["s"]
# pre-compute quantities for the derivative
m, n = A.shape
N = m + n + 1
cones = cone_lib.parse_cone_dict(cone_dict)
cones_parsed = cone_lib.parse_cone_dict_cpp(cones)
z = (x, y - s, np.array([1]))
u, v, w = z