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zero_dim = np.random.randint(1, 10)
pos_dim = np.random.randint(1, 10)
soc_dim = [np.random.randint(1, 10) for _ in range(
np.random.randint(1, 10))]
psd_dim = [np.random.randint(1, 10) for _ in range(
np.random.randint(1, 10))]
exp_dim = np.random.randint(3, 18)
cones = [(cone_lib.ZERO, zero_dim), (cone_lib.POS, pos_dim),
(cone_lib.SOC, soc_dim), (cone_lib.PSD, psd_dim),
(cone_lib.EXP, exp_dim), (cone_lib.EXP_DUAL, exp_dim)]
size = zero_dim + pos_dim + sum(soc_dim) + sum(
[cone_lib.vec_psd_dim(d) for d in psd_dim]) + 2 * 3 * exp_dim
x = np.random.randn(size)
for dual in [False, True]:
cone_list_cpp = cone_lib.parse_cone_dict_cpp(cones)
proj_x = cone_lib.pi(x, cones, dual=dual)
dx = 1e-7 * np.random.randn(size)
z = cone_lib.pi(x + dx, cones, dual=dual)
Dpi = _diffcp.dprojection(x, cone_list_cpp, dual)
np.testing.assert_allclose(
Dpi.matvec(dx), z - proj_x, atol=1e-6)
Dpi = _diffcp.dprojection_dense(x, cone_list_cpp, dual)
np.testing.assert_allclose(Dpi @ dx, z - proj_x, atol=1e-6)
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
Q = sparse.bmat([
[None, A.T, np.expand_dims(c, - 1)],
[-A, None, np.expand_dims(b, -1)],
[-np.expand_dims(c, -1).T, -np.expand_dims(b, -1).T, None]
])
D_proj_dual_cone = _diffcp.dprojection(v, cones_parsed, True)
if mode == "dense":
Q_dense = Q.todense()
M = _diffcp.M_dense(Q_dense, cones_parsed, u, v, w)
MT = M.T
else:
M = _diffcp.M_operator(Q, cones_parsed, u, v, w)