def test_compare_dense_and_sparse(self):
D = np.diag(range(1, 101))
A = np.hstack([D, D, D, D])
A_sparse = csc_matrix(A)
np.random.seed(0)
Z, LS, Y = projections(A)
Z_sparse, LS_sparse, Y_sparse = projections(A_sparse)
for k in range(20):
z = np.random.normal(size=(400, ))
assert_array_almost_equal(Z.dot(z), Z_sparse.dot(z))
assert_array_almost_equal(LS.dot(z), LS_sparse.dot(z))
x = np.random.normal(size=(100, ))
assert_array_almost_equal(Y.dot(x), Y_sparse.dot(x))
def test_compare_dense_and_sparse(self):
D = np.diag(range(1, 101))
A = np.hstack([D, D, D, D])
A_sparse = csc_matrix(A)
np.random.seed(0)
Z, LS, Y = projections(A)
Z_sparse, LS_sparse, Y_sparse = projections(A_sparse)
for k in range(20):
z = np.random.normal(size=(400,))
assert_array_almost_equal(Z.dot(z), Z_sparse.dot(z))
assert_array_almost_equal(LS.dot(z), LS_sparse.dot(z))
x = np.random.normal(size=(100,))
assert_array_almost_equal(Y.dot(x), Y_sparse.dot(x))
def test_compare_dense_and_sparse2(self):
D1 = np.diag([-1.7, 1, 0.5])
D2 = np.diag([1, -0.6, -0.3])
D3 = np.diag([-0.3, -1.5, 2])
A = np.hstack([D1, D2, D3])
A_sparse = csc_matrix(A)
np.random.seed(0)
Z, LS, Y = projections(A)
Z_sparse, LS_sparse, Y_sparse = projections(A_sparse)
for k in range(1):
z = np.random.normal(size=(9, ))
assert_array_almost_equal(Z.dot(z), Z_sparse.dot(z))
assert_array_almost_equal(LS.dot(z), LS_sparse.dot(z))
x = np.random.normal(size=(3, ))
assert_array_almost_equal(Y.dot(x), Y_sparse.dot(x))
def test_compare_dense_and_sparse2(self):
D1 = np.diag([-1.7, 1, 0.5])
D2 = np.diag([1, -0.6, -0.3])
D3 = np.diag([-0.3, -1.5, 2])
A = np.hstack([D1, D2, D3])
A_sparse = csc_matrix(A)
np.random.seed(0)
Z, LS, Y = projections(A)
Z_sparse, LS_sparse, Y_sparse = projections(A_sparse)
for k in range(1):
z = np.random.normal(size=(9,))
assert_array_almost_equal(Z.dot(z), Z_sparse.dot(z))
assert_array_almost_equal(LS.dot(z), LS_sparse.dot(z))
x = np.random.normal(size=(3,))
assert_array_almost_equal(Y.dot(x), Y_sparse.dot(x))
def test_negative_curvature_unconstrained(self):
H = csc_matrix([[1, 2, 1, 3], [2, 0, 2, 4], [1, 2, 0, 2], [3, 4, 2,
0]])
A = csc_matrix([[1, 0, 1, 0], [0, 1, 0, 1]])
c = np.array([-2, -3, -3, 1])
b = -np.array([3, 0])
Z, _, Y = projections(A)
with pytest.raises(ValueError):
projected_cg(H, c, Z, Y, b, tol=0)
def test_trust_region_infeasible(self):
H = csc_matrix([[6, 2, 1, 3], [2, 5, 2, 4], [1, 2, 4, 5], [3, 4, 5,
7]])
A = csc_matrix([[1, 0, 1, 0], [0, 1, 1, 1]])
c = np.array([-2, -3, -3, 1])
b = -np.array([3, 0])
trust_radius = 1
Z, _, Y = projections(A)
with pytest.raises(ValueError):
projected_cg(H, c, Z, Y, b, trust_radius=trust_radius)
def test_nocedal_example(self):
H = csc_matrix([[6, 2, 1], [2, 5, 2], [1, 2, 4]])
A = csc_matrix([[1, 0, 1], [0, 1, 1]])
c = np.array([-8, -3, -3])
b = -np.array([3, 0])
Z, _, Y = projections(A)
x, info = projected_cg(H, c, Z, Y, b)
assert_equal(info["stop_cond"], 4)
assert_equal(info["hits_boundary"], False)
assert_array_almost_equal(x, [2, -1, 1])
def test_3d_example(self):
A = np.array([[1, 8, 1],
[4, 2, 2]])
b = np.array([-16, 2])
Z, LS, Y = projections(A)
newton_point = np.array([-1.37090909, 2.23272727, -0.49090909])
cauchy_point = np.array([0.11165723, 1.73068711, 0.16748585])
origin = np.zeros_like(newton_point)
# newton_point inside boundaries
x = modified_dogleg(A, Y, b, 3, [-np.inf, -np.inf, -np.inf],
[np.inf, np.inf, np.inf])
assert_array_almost_equal(x, newton_point)
# line between cauchy_point and newton_point contains best point
# (spherical constrain is active).
x = modified_dogleg(A, Y, b, 2, [-np.inf, -np.inf, -np.inf],
[np.inf, np.inf, np.inf])
z = cauchy_point
d = newton_point-cauchy_point
t = ((x-z)/(d))
assert_array_almost_equal(t, 0.40807330*np.ones(3))
assert_array_almost_equal(np.linalg.norm(x), 2)
# line between cauchy_point and newton_point contains best point
# (box constrain is active).
x = modified_dogleg(A, Y, b, 5, [-1, -np.inf, -np.inf],
[np.inf, np.inf, np.inf])
z = cauchy_point
d = newton_point-cauchy_point
t = ((x-z)/(d))
assert_array_almost_equal(t, 0.7498195*np.ones(3))
assert_array_almost_equal(x[0], -1)
# line between origin and cauchy_point contains best point
# (spherical constrain is active).
x = modified_dogleg(A, Y, b, 1, [-np.inf, -np.inf, -np.inf],
[np.inf, np.inf, np.inf])
z = origin
d = cauchy_point
t = ((x-z)/(d))
assert_array_almost_equal(t, 0.573936265*np.ones(3))
assert_array_almost_equal(np.linalg.norm(x), 1)
# line between origin and newton_point contains best point
# (box constrain is active).
x = modified_dogleg(A, Y, b, 2, [-np.inf, -np.inf, -np.inf],
[np.inf, 1, np.inf])
z = origin
d = newton_point
t = ((x-z)/(d))
assert_array_almost_equal(t, 0.4478827364*np.ones(3))
assert_array_almost_equal(x[1], 1)
def test_3d_example(self):
A = np.array([[1, 8, 1],
[4, 2, 2]])
b = np.array([-16, 2])
Z, LS, Y = projections(A)
newton_point = np.array([-1.37090909, 2.23272727, -0.49090909])
cauchy_point = np.array([0.11165723, 1.73068711, 0.16748585])
origin = np.zeros_like(newton_point)
# newton_point inside boundaries
x = modified_dogleg(A, Y, b, 3, [-np.inf, -np.inf, -np.inf],
[np.inf, np.inf, np.inf])
assert_array_almost_equal(x, newton_point)
# line between cauchy_point and newton_point contains best point
# (spherical constrain is active).
x = modified_dogleg(A, Y, b, 2, [-np.inf, -np.inf, -np.inf],
[np.inf, np.inf, np.inf])
z = cauchy_point
d = newton_point-cauchy_point
t = ((x-z)/(d))
assert_array_almost_equal(t, np.full(3, 0.40807330))
assert_array_almost_equal(np.linalg.norm(x), 2)
# line between cauchy_point and newton_point contains best point
# (box constrain is active).
x = modified_dogleg(A, Y, b, 5, [-1, -np.inf, -np.inf],
[np.inf, np.inf, np.inf])
z = cauchy_point
d = newton_point-cauchy_point
t = ((x-z)/(d))
assert_array_almost_equal(t, np.full(3, 0.7498195))
assert_array_almost_equal(x[0], -1)
# line between origin and cauchy_point contains best point
# (spherical constrain is active).
x = modified_dogleg(A, Y, b, 1, [-np.inf, -np.inf, -np.inf],
[np.inf, np.inf, np.inf])
z = origin
d = cauchy_point
t = ((x-z)/(d))
assert_array_almost_equal(t, np.full(3, 0.573936265))
assert_array_almost_equal(np.linalg.norm(x), 1)
# line between origin and newton_point contains best point
# (box constrain is active).
x = modified_dogleg(A, Y, b, 2, [-np.inf, -np.inf, -np.inf],
[np.inf, 1, np.inf])
z = origin
d = newton_point
t = ((x-z)/(d))
assert_array_almost_equal(t, np.full(3, 0.4478827364))
assert_array_almost_equal(x[1], 1)
def test_compare_with_direct_fact(self):
H = csc_matrix([[6, 2, 1, 3], [2, 5, 2, 4], [1, 2, 4, 5], [3, 4, 5,
7]])
A = csc_matrix([[1, 0, 1, 0], [0, 1, 1, 1]])
c = np.array([-2, -3, -3, 1])
b = -np.array([3, 0])
Z, _, Y = projections(A)
x, info = projected_cg(H, c, Z, Y, b, tol=0)
x_kkt, _ = eqp_kktfact(H, c, A, b)
assert_equal(info["stop_cond"], 1)
assert_equal(info["hits_boundary"], False)
assert_array_almost_equal(x, x_kkt)
def test_negative_curvature_unconstrained(self):
H = csc_matrix([[1, 2, 1, 3],
[2, 0, 2, 4],
[1, 2, 0, 2],
[3, 4, 2, 0]])
A = csc_matrix([[1, 0, 1, 0],
[0, 1, 0, 1]])
c = np.array([-2, -3, -3, 1])
b = -np.array([3, 0])
Z, _, Y = projections(A)
with pytest.raises(ValueError):
projected_cg(H, c, Z, Y, b, tol=0)
def test_hits_boundary(self):
H = csc_matrix([[6, 2, 1, 3], [2, 5, 2, 4], [1, 2, 4, 5], [3, 4, 5,
7]])
A = csc_matrix([[1, 0, 1, 0], [0, 1, 1, 1]])
c = np.array([-2, -3, -3, 1])
b = -np.array([3, 0])
trust_radius = 3
Z, _, Y = projections(A)
x, info = projected_cg(H, c, Z, Y, b, tol=0, trust_radius=trust_radius)
assert_equal(info["stop_cond"], 2)
assert_equal(info["hits_boundary"], True)
assert_array_almost_equal(np.linalg.norm(x), trust_radius)
def test_negative_curvature(self):
H = csc_matrix([[1, 2, 1, 3], [2, 0, 2, 4], [1, 2, 0, 2], [3, 4, 2,
0]])
A = csc_matrix([[1, 0, 1, 0], [0, 1, 0, 1]])
c = np.array([-2, -3, -3, 1])
b = -np.array([3, 0])
Z, _, Y = projections(A)
trust_radius = 1000
x, info = projected_cg(H, c, Z, Y, b, tol=0, trust_radius=trust_radius)
assert_equal(info["stop_cond"], 3)
assert_equal(info["hits_boundary"], True)
assert_array_almost_equal(np.linalg.norm(x), trust_radius)
def test_nocedal_example(self):
H = csc_matrix([[6, 2, 1],
[2, 5, 2],
[1, 2, 4]])
A = csc_matrix([[1, 0, 1],
[0, 1, 1]])
c = np.array([-8, -3, -3])
b = -np.array([3, 0])
Z, _, Y = projections(A)
x, info = projected_cg(H, c, Z, Y, b)
assert_equal(info["stop_cond"], 4)
assert_equal(info["hits_boundary"], False)
assert_array_almost_equal(x, [2, -1, 1])
def test_trust_region_barely_feasible(self):
H = csc_matrix([[6, 2, 1, 3], [2, 5, 2, 4], [1, 2, 4, 5], [3, 4, 5,
7]])
A = csc_matrix([[1, 0, 1, 0], [0, 1, 1, 1]])
c = np.array([-2, -3, -3, 1])
b = -np.array([3, 0])
trust_radius = 2.32379000772445021283
Z, _, Y = projections(A)
x, info = projected_cg(H, c, Z, Y, b, tol=0, trust_radius=trust_radius)
assert_equal(info["stop_cond"], 2)
assert_equal(info["hits_boundary"], True)
assert_array_almost_equal(np.linalg.norm(x), trust_radius)
assert_array_almost_equal(x, -Y.dot(b))
def test_trust_region_infeasible(self):
H = csc_matrix([[6, 2, 1, 3],
[2, 5, 2, 4],
[1, 2, 4, 5],
[3, 4, 5, 7]])
A = csc_matrix([[1, 0, 1, 0],
[0, 1, 1, 1]])
c = np.array([-2, -3, -3, 1])
b = -np.array([3, 0])
trust_radius = 1
Z, _, Y = projections(A)
with pytest.raises(ValueError):
projected_cg(H, c, Z, Y, b, trust_radius=trust_radius)
def test_compare_with_direct_fact(self):
H = csc_matrix([[6, 2, 1, 3],
[2, 5, 2, 4],
[1, 2, 4, 5],
[3, 4, 5, 7]])
A = csc_matrix([[1, 0, 1, 0],
[0, 1, 1, 1]])
c = np.array([-2, -3, -3, 1])
b = -np.array([3, 0])
Z, _, Y = projections(A)
x, info = projected_cg(H, c, Z, Y, b, tol=0)
x_kkt, _ = eqp_kktfact(H, c, A, b)
assert_equal(info["stop_cond"], 1)
assert_equal(info["hits_boundary"], False)
assert_array_almost_equal(x, x_kkt)
def test_iterative_refinements_dense(self):
A = np.array([[1, 2, 3, 4, 0, 5, 0, 7], [0, 8, 7, 0, 1, 5, 9, 0],
[1, 0, 0, 0, 0, 1, 2, 3]])
test_points = ([1, 2, 3, 4, 5, 6, 7,
8], [1, 10, 3, 0, 1, 6, 7,
8], [1, 0, 0, 0, 0, 1, 2, 3 + 1e-10])
for method in available_dense_methods:
Z, LS, _ = projections(A, method, orth_tol=1e-18, max_refin=10)
for z in test_points:
# Test if x is in the null_space
x = Z.matvec(z)
assert_array_almost_equal(A.dot(x), 0, decimal=14)
# Test orthogonality
assert_array_almost_equal(orthogonality(A, x), 0, decimal=16)
def test_rowspace_dense(self):
A = np.array([[1, 2, 3, 4, 0, 5, 0, 7], [0, 8, 7, 0, 1, 5, 9, 0],
[1, 0, 0, 0, 0, 1, 2, 3]])
test_points = ([1, 2, 3], [1, 10, 3], [1.12, 10, 0])
for method in available_dense_methods:
_, _, Y = projections(A, method)
for z in test_points:
# Test if x is solution of A x = z
x = Y.matvec(z)
assert_array_almost_equal(A.dot(x), z)
# Test if x is in the return row space of A
A_ext = np.vstack((A, x))
assert_equal(np.linalg.matrix_rank(A),
np.linalg.matrix_rank(A_ext))
def test_iterative_refinements_dense(self):
A = np.array([[1, 2, 3, 4, 0, 5, 0, 7],
[0, 8, 7, 0, 1, 5, 9, 0],
[1, 0, 0, 0, 0, 1, 2, 3]])
test_points = ([1, 2, 3, 4, 5, 6, 7, 8],
[1, 10, 3, 0, 1, 6, 7, 8],
[1, 0, 0, 0, 0, 1, 2, 3+1e-10])
for method in available_dense_methods:
Z, LS, _ = projections(A, method, orth_tol=1e-18, max_refin=10)
for z in test_points:
# Test if x is in the null_space
x = Z.matvec(z)
assert_array_almost_equal(A.dot(x), 0, decimal=14)
# Test orthogonality
assert_array_almost_equal(orthogonality(A, x), 0, decimal=16)
def test_cauchypoint_equalsto_newtonpoint(self):
A = np.array([[1, 8]])
b = np.array([-16])
_, _, Y = projections(A)
newton_point = np.array([0.24615385, 1.96923077])
# Newton point inside boundaries
x = modified_dogleg(A, Y, b, 2, [-np.inf, -np.inf], [np.inf, np.inf])
assert_array_almost_equal(x, newton_point)
# Spherical constraint active
x = modified_dogleg(A, Y, b, 1, [-np.inf, -np.inf], [np.inf, np.inf])
assert_array_almost_equal(x, newton_point/np.linalg.norm(newton_point))
# Box Constraints active
x = modified_dogleg(A, Y, b, 2, [-np.inf, -np.inf], [0.1, np.inf])
assert_array_almost_equal(x, (newton_point/newton_point[0]) * 0.1)
def test_negative_curvature(self):
H = csc_matrix([[1, 2, 1, 3],
[2, 0, 2, 4],
[1, 2, 0, 2],
[3, 4, 2, 0]])
A = csc_matrix([[1, 0, 1, 0],
[0, 1, 0, 1]])
c = np.array([-2, -3, -3, 1])
b = -np.array([3, 0])
Z, _, Y = projections(A)
trust_radius = 1000
x, info = projected_cg(H, c, Z, Y, b,
tol=0,
trust_radius=trust_radius)
assert_equal(info["stop_cond"], 3)
assert_equal(info["hits_boundary"], True)
assert_array_almost_equal(np.linalg.norm(x), trust_radius)
def test_hits_boundary(self):
H = csc_matrix([[6, 2, 1, 3],
[2, 5, 2, 4],
[1, 2, 4, 5],
[3, 4, 5, 7]])
A = csc_matrix([[1, 0, 1, 0],
[0, 1, 1, 1]])
c = np.array([-2, -3, -3, 1])
b = -np.array([3, 0])
trust_radius = 3
Z, _, Y = projections(A)
x, info = projected_cg(H, c, Z, Y, b,
tol=0,
trust_radius=trust_radius)
assert_equal(info["stop_cond"], 2)
assert_equal(info["hits_boundary"], True)
assert_array_almost_equal(np.linalg.norm(x), trust_radius)
def test_cauchypoint_equalsto_newtonpoint(self):
A = np.array([[1, 8]])
b = np.array([-16])
_, _, Y = projections(A)
newton_point = np.array([0.24615385, 1.96923077])
# Newton point inside boundaries
x = modified_dogleg(A, Y, b, 2, [-np.inf, -np.inf], [np.inf, np.inf])
assert_array_almost_equal(x, newton_point)
# Spherical constraint active
x = modified_dogleg(A, Y, b, 1, [-np.inf, -np.inf], [np.inf, np.inf])
assert_array_almost_equal(x, newton_point/np.linalg.norm(newton_point))
# Box Constraints active
x = modified_dogleg(A, Y, b, 2, [-np.inf, -np.inf], [0.1, np.inf])
assert_array_almost_equal(x, (newton_point/newton_point[0]) * 0.1)
def test_rowspace_dense(self):
A = np.array([[1, 2, 3, 4, 0, 5, 0, 7],
[0, 8, 7, 0, 1, 5, 9, 0],
[1, 0, 0, 0, 0, 1, 2, 3]])
test_points = ([1, 2, 3],
[1, 10, 3],
[1.12, 10, 0])
for method in available_dense_methods:
_, _, Y = projections(A, method)
for z in test_points:
# Test if x is solution of A x = z
x = Y.matvec(z)
assert_array_almost_equal(A.dot(x), z)
# Test if x is in the return row space of A
A_ext = np.vstack((A, x))
assert_equal(np.linalg.matrix_rank(A),
np.linalg.matrix_rank(A_ext))
def test_trust_region_barely_feasible(self):
H = csc_matrix([[6, 2, 1, 3],
[2, 5, 2, 4],
[1, 2, 4, 5],
[3, 4, 5, 7]])
A = csc_matrix([[1, 0, 1, 0],
[0, 1, 1, 1]])
c = np.array([-2, -3, -3, 1])
b = -np.array([3, 0])
trust_radius = 2.32379000772445021283
Z, _, Y = projections(A)
x, info = projected_cg(H, c, Z, Y, b,
tol=0,
trust_radius=trust_radius)
assert_equal(info["stop_cond"], 2)
assert_equal(info["hits_boundary"], True)
assert_array_almost_equal(np.linalg.norm(x), trust_radius)
assert_array_almost_equal(x, -Y.dot(b))
def test_iterative_refinements_sparse(self):
A_dense = np.array([[1, 2, 3, 4, 0, 5, 0, 7], [0, 8, 7, 0, 1, 5, 9, 0],
[1, 0, 0, 0, 0, 1, 2, 3]])
A = csc_matrix(A_dense)
test_points = ([1, 2, 3, 4, 5, 6, 7,
8], [1, 10, 3, 0, 1, 6, 7,
8], [1.12, 10, 0, 0, 100000, 6, 0.7,
8], [1, 0, 0, 0, 0, 1, 2, 3 + 1e-10])
for method in available_sparse_methods:
Z, LS, _ = projections(A, method, orth_tol=1e-18, max_refin=100)
for z in test_points:
# Test if x is in the null_space
x = Z.matvec(z)
atol = 1e-13 * abs(x).max()
assert_allclose(A.dot(x), 0, atol=atol)
# Test orthogonality
assert_allclose(orthogonality(A, x), 0, atol=1e-13)
def test_active_box_constraints_hits_boundaries_infeasible_iter(self):
H = csc_matrix([[6, 2, 1, 3],
[2, 5, 2, 4],
[1, 2, 4, 5],
[3, 4, 5, 7]])
A = csc_matrix([[1, 0, 1, 0],
[0, 1, 1, 1]])
c = np.array([-2, -3, -3, 1])
b = -np.array([3, 0])
trust_radius = 4
Z, _, Y = projections(A)
x, info = projected_cg(H, c, Z, Y, b,
tol=0,
ub=[np.inf, 0.1, np.inf, np.inf],
trust_radius=trust_radius,
return_all=True)
assert_equal(info["stop_cond"], 2)
assert_equal(info["hits_boundary"], True)
assert_array_almost_equal(x[1], 0.1)
def test_active_box_constraints_maximum_iterations_reached(self):
H = csc_matrix([[6, 2, 1, 3],
[2, 5, 2, 4],
[1, 2, 4, 5],
[3, 4, 5, 7]])
A = csc_matrix([[1, 0, 1, 0],
[0, 1, 1, 1]])
c = np.array([-2, -3, -3, 1])
b = -np.array([3, 0])
Z, _, Y = projections(A)
x, info = projected_cg(H, c, Z, Y, b,
tol=0,
lb=[0.8, -np.inf,
-np.inf, -np.inf],
return_all=True)
assert_equal(info["stop_cond"], 1)
assert_equal(info["hits_boundary"], True)
assert_array_almost_equal(A.dot(x), -b)
assert_array_almost_equal(x[0], 0.8)
def test_inactive_box_constraints(self):
H = csc_matrix([[6, 2, 1, 3],
[2, 5, 2, 4],
[1, 2, 4, 5],
[3, 4, 5, 7]])
A = csc_matrix([[1, 0, 1, 0],
[0, 1, 1, 1]])
c = np.array([-2, -3, -3, 1])
b = -np.array([3, 0])
Z, _, Y = projections(A)
x, info = projected_cg(H, c, Z, Y, b,
tol=0,
lb=[0.5, -np.inf,
-np.inf, -np.inf],
return_all=True)
x_kkt, _ = eqp_kktfact(H, c, A, b)
assert_equal(info["stop_cond"], 1)
assert_equal(info["hits_boundary"], False)
assert_array_almost_equal(x, x_kkt)
def test_inactive_box_constraints(self):
H = csc_matrix([[6, 2, 1, 3],
[2, 5, 2, 4],
[1, 2, 4, 5],
[3, 4, 5, 7]])
A = csc_matrix([[1, 0, 1, 0],
[0, 1, 1, 1]])
c = np.array([-2, -3, -3, 1])
b = -np.array([3, 0])
Z, _, Y = projections(A)
x, info = projected_cg(H, c, Z, Y, b,
tol=0,
lb=[0.5, -np.inf,
-np.inf, -np.inf],
return_all=True)
x_kkt, _ = eqp_kktfact(H, c, A, b)
assert_equal(info["stop_cond"], 1)
assert_equal(info["hits_boundary"], False)
assert_array_almost_equal(x, x_kkt)
def test_active_box_constraints_maximum_iterations_reached(self):
H = csc_matrix([[6, 2, 1, 3],
[2, 5, 2, 4],
[1, 2, 4, 5],
[3, 4, 5, 7]])
A = csc_matrix([[1, 0, 1, 0],
[0, 1, 1, 1]])
c = np.array([-2, -3, -3, 1])
b = -np.array([3, 0])
Z, _, Y = projections(A)
x, info = projected_cg(H, c, Z, Y, b,
tol=0,
lb=[0.8, -np.inf,
-np.inf, -np.inf],
return_all=True)
assert_equal(info["stop_cond"], 1)
assert_equal(info["hits_boundary"], True)
assert_array_almost_equal(A.dot(x), -b)
assert_array_almost_equal(x[0], 0.8)
def test_active_box_constraints_hits_boundaries_infeasible_iter(self):
H = csc_matrix([[6, 2, 1, 3],
[2, 5, 2, 4],
[1, 2, 4, 5],
[3, 4, 5, 7]])
A = csc_matrix([[1, 0, 1, 0],
[0, 1, 1, 1]])
c = np.array([-2, -3, -3, 1])
b = -np.array([3, 0])
trust_radius = 4
Z, _, Y = projections(A)
x, info = projected_cg(H, c, Z, Y, b,
tol=0,
ub=[np.inf, 0.1, np.inf, np.inf],
trust_radius=trust_radius,
return_all=True)
assert_equal(info["stop_cond"], 2)
assert_equal(info["hits_boundary"], True)
assert_array_almost_equal(x[1], 0.1)
def test_iterative_refinements_sparse(self):
A_dense = np.array([[1, 2, 3, 4, 0, 5, 0, 7],
[0, 8, 7, 0, 1, 5, 9, 0],
[1, 0, 0, 0, 0, 1, 2, 3]])
A = csc_matrix(A_dense)
test_points = ([1, 2, 3, 4, 5, 6, 7, 8],
[1, 10, 3, 0, 1, 6, 7, 8],
[1.12, 10, 0, 0, 100000, 6, 0.7, 8],
[1, 0, 0, 0, 0, 1, 2, 3+1e-10])
for method in available_sparse_methods:
Z, LS, _ = projections(A, method, orth_tol=1e-18, max_refin=100)
for z in test_points:
# Test if x is in the null_space
x = Z.matvec(z)
atol = 1e-13 * abs(x).max()
err = abs(A.dot(x)).max()
assert_allclose(A.dot(x), 0, atol=atol)
# Test orthogonality
assert_allclose(orthogonality(A, x), 0, atol=1e-13)
def test_nullspace_and_least_squares_dense(self):
A = np.array([[1, 2, 3, 4, 0, 5, 0, 7], [0, 8, 7, 0, 1, 5, 9, 0],
[1, 0, 0, 0, 0, 1, 2, 3]])
At = A.T
test_points = ([1, 2, 3, 4, 5, 6, 7,
8], [1, 10, 3, 0, 1, 6, 7,
8], [1.12, 10, 0, 0, 100000, 6, 0.7, 8])
for method in available_dense_methods:
Z, LS, _ = projections(A, method)
for z in test_points:
# Test if x is in the null_space
x = Z.matvec(z)
assert_array_almost_equal(A.dot(x), 0)
# Test orthogonality
assert_array_almost_equal(orthogonality(A, x), 0)
# Test if x is the least square solution
x = LS.matvec(z)
x2 = scipy.linalg.lstsq(At, z)[0]
assert_array_almost_equal(x, x2)
def test_nullspace_and_least_squares_dense(self):
A = np.array([[1, 2, 3, 4, 0, 5, 0, 7],
[0, 8, 7, 0, 1, 5, 9, 0],
[1, 0, 0, 0, 0, 1, 2, 3]])
At = A.T
test_points = ([1, 2, 3, 4, 5, 6, 7, 8],
[1, 10, 3, 0, 1, 6, 7, 8],
[1.12, 10, 0, 0, 100000, 6, 0.7, 8])
for method in available_dense_methods:
Z, LS, _ = projections(A, method)
for z in test_points:
# Test if x is in the null_space
x = Z.matvec(z)
assert_array_almost_equal(A.dot(x), 0)
# Test orthogonality
assert_array_almost_equal(orthogonality(A, x), 0)
# Test if x is the least square solution
x = LS.matvec(z)
x2 = scipy.linalg.lstsq(At, z)[0]
assert_array_almost_equal(x, x2)
