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def test_ecos_solve(self):
np.random.seed(0)
m = 20
n = 10
A, b, c, cone_dims = utils.least_squares_eq_scs_data(m, n)
cone_dims.pop("q")
cone_dims.pop("s")
cone_dims.pop("ep")
x, y, s, derivative, adjoint_derivative = cone_prog.solve_and_derivative(
A, b, c, cone_dims, solver="ECOS")
# check optimality conditions
np.testing.assert_allclose(A @ x + s, b, atol=1e-8)
np.testing.assert_allclose(A.T @ y + c, 0, atol=1e-8)
np.testing.assert_allclose(s @ y, 0, atol=1e-8)
np.testing.assert_allclose(s, cone_lib.pi(
s, cone_lib.parse_cone_dict(cone_dims), dual=False), atol=1e-8)
np.testing.assert_allclose(y, cone_lib.pi(
y, cone_lib.parse_cone_dict(cone_dims), dual=True), atol=1e-8)
x = cp.Variable(10)
cone_dims.pop("ep")
x, y, s, derivative, adjoint_derivative = cone_prog.solve_and_derivative(
A, b, c, cone_dims, solver="ECOS")
# check optimality conditions
np.testing.assert_allclose(A @ x + s, b, atol=1e-8)
np.testing.assert_allclose(A.T @ y + c, 0, atol=1e-8)
np.testing.assert_allclose(s @ y, 0, atol=1e-8)
np.testing.assert_allclose(s, cone_lib.pi(
s, cone_lib.parse_cone_dict(cone_dims), dual=False), atol=1e-8)
np.testing.assert_allclose(y, cone_lib.pi(
y, cone_lib.parse_cone_dict(cone_dims), dual=True), atol=1e-8)
x = cp.Variable(10)
prob = cp.Problem(cp.Minimize(cp.sum_squares(np.random.randn(5, 10) @ x) + np.random.randn(10) @ x), [cp.norm2(x) <= 1, np.random.randn(2, 10) @ x == np.random.randn(2)])
A, b, c, cone_dims = utils.scs_data_from_cvxpy_problem(prob)
x, y, s, derivative, adjoint_derivative = cone_prog.solve_and_derivative(
A, b, c, cone_dims, solver="ECOS")
# check optimality conditions
np.testing.assert_allclose(A @ x + s, b, atol=1e-8)
np.testing.assert_allclose(A.T @ y + c, 0, atol=1e-8)
np.testing.assert_allclose(s @ y, 0, atol=1e-8)
np.testing.assert_allclose(s, cone_lib.pi(
s, cone_lib.parse_cone_dict(cone_dims), dual=False), atol=1e-8)
np.testing.assert_allclose(y, cone_lib.pi(
y, cone_lib.parse_cone_dict(cone_dims), dual=True), atol=1e-8)
import cvxpy as cp
import numpy as np
from scipy import sparse
from scipy.sparse import linalg as splinalg
import time
import diffcp.cone_program as cone_prog
import diffcp.cones as cone_lib
import diffcp.utils as utils
m = 100
n = 50
A, b, c, cone_dims = utils.least_squares_eq_scs_data(m, n)
for mode in ["lsqr", "dense"]:
x, y, s, derivative, adjoint_derivative = cone_prog.solve_and_derivative(
A, b, c, cone_dims, eps=1e-10, mode=mode)
dA = utils.get_random_like(
A, lambda n: np.random.normal(0, 1e-2, size=n))
db = np.random.normal(0, 1e-2, size=b.size)
dc = np.random.normal(0, 1e-2, size=c.size)
derivative_time = 0.0
for _ in range(10):
tic = time.time()
dx, dy, ds = derivative(dA, db, dc)
toc = time.time()
derivative_time += (toc - tic) / 10
import diffcp
import utils
import IPython as ipy
import time
import numpy as np
m = 100
n = 50
batch_size = 16
n_jobs = 1
As, bs, cs, Ks = [], [], [], []
for _ in range(batch_size):
A, b, c, K = diffcp.utils.least_squares_eq_scs_data(m, n)
As += [A]
bs += [b]
cs += [c]
Ks += [K]
def time_function(f, N=1):
result = []
for i in range(N):
tic = time.time()
f()
toc = time.time()
result += [toc - tic]
return np.mean(result), np.std(result)
for n_jobs in range(1, 8):