def setup(self, meth, prob):
if prob not in enabled_rr_problems:
raise NotImplementedError("skipped")
if (meth.__name__, prob) in self.known_fails:
raise NotImplementedError("Known issues with these benchmarks.")
dir_path = os.path.dirname(os.path.realpath(__file__))
datafile = os.path.join(dir_path, "linprog_benchmark_files",
prob + ".npz")
data = np.load(datafile, allow_pickle=True)
c, A_eq, A_ub, b_ub, b_eq = (data["c"], data["A_eq"], data["A_ub"],
data["b_ub"], data["b_eq"])
bounds = np.squeeze(data["bounds"])
x0 = np.zeros(c.shape)
lp = _LPProblem(c, A_ub, b_ub, A_eq, b_eq, bounds, x0)
lp_cleaned = _clean_inputs(lp)
# rr_method is None here because we're not using RR
res = _presolve(lp_cleaned, rr=False, rr_method=None, tol=1e-9)[0]
self.A_eq, self.b_eq = res.A_eq, res.b_eq
self.true_rank = np.linalg.matrix_rank(self.A_eq)
if meth == _remove_redundancy_pivot_sparse:
self.A_eq = csc_matrix(self.A_eq)
self.rr_A = None
def time_netlib_presolve(self, meth, prob):
_presolve(self.lp_cleaned, rr=False, rr_method=None, tol=1e-9)
