def test_proximal_maps():
shape = 20
bound = 0.14
lagrange = 0.13
Z = np.random.standard_normal(shape) * 2
W = 0.02 * np.random.standard_normal(shape)
U = 0.02 * np.random.standard_normal(shape)
linq = rr.identity_quadratic(0, 0, W, 0)
basis = np.linalg.svd(np.random.standard_normal((4, 20)),
full_matrices=0)[2]
w1 = np.ones(20) * 0.5
w2 = w1 * 0
w2[:10] = 2.
for L, atom, q, offset, FISTA, coef_stop, w in itertools.product(
[0.5, 1, 0.1], sorted(WA.conjugate_weighted_pairs.keys()),
[None, linq], [None, U], [False, True], [False, True], [w1, w2]):
# we only have two weighted atoms,
# l1 in lagrange and supnorm in bound
print 'w: ', w.shape
if atom == WA.l1norm:
p = atom(shape, w, quadratic=q, offset=offset, lagrange=lagrange)
else:
p = atom(shape, w, quadratic=q, offset=offset, bound=bound)
for t in solveit(p, Z, W, U, linq, L, FISTA, coef_stop):
yield t
def test_proximal_maps():
shape = (5,4)
bound = 0.14
lagrange = 0.13
Z = np.random.standard_normal(shape) * 2
W = 0.02 * np.random.standard_normal(shape)
U = 0.02 * np.random.standard_normal(shape)
linq = rr.identity_quadratic(0,0,W,0)
basis = np.linalg.svd(np.random.standard_normal((4,20)), full_matrices=0)[2]
for L, atom, q, offset, FISTA, coef_stop in itertools.product([0.5,1,0.1],
sorted(B.conjugate_block_pairs.keys()),
[None, linq],
[None, U],
[False, True],
[False, True]):
if atom not in [B.block_max, B.block_sum]:
p = atom(shape, quadratic=q, lagrange=lagrange,
offset=offset)
d = p.conjugate
yield ac, p.lagrange_prox(Z, lipschitz=L), Z-d.bound_prox(Z*L, lipschitz=1./L)/L, 'testing lagrange_prox and bound_prox starting from atom %s ' % atom
# some arguments of the constructor
nt.assert_raises(AttributeError, setattr, p, 'bound', 4.)
nt.assert_raises(AttributeError, setattr, d, 'lagrange', 4.)
nt.assert_raises(AttributeError, setattr, p, 'bound', 4.)
nt.assert_raises(AttributeError, setattr, d, 'lagrange', 4.)
for t in solveit(p, Z, W, U, linq, L, FISTA, coef_stop):
yield t
b = atom(shape, bound=bound, quadratic=q,
offset=offset)
for t in solveit(b, Z, W, U, linq, L, FISTA, coef_stop):
yield t
def test_proximal_maps():
shape = (5, 4)
bound = 0.14
lagrange = 0.13
Z = np.random.standard_normal(shape) * 2
W = 0.02 * np.random.standard_normal(shape)
U = 0.02 * np.random.standard_normal(shape)
linq = rr.identity_quadratic(0, 0, W, 0)
basis = np.linalg.svd(np.random.standard_normal((4, 20)),
full_matrices=0)[2]
for L, atom, q, offset, FISTA, coef_stop in itertools.product(
[0.5, 1, 0.1], sorted(S.conjugate_svd_pairs.keys()), [None, linq],
[None, U], [False, True], [False, True]):
p = atom(shape, quadratic=q, lagrange=lagrange, offset=offset)
d = p.conjugate
yield ac, p.lagrange_prox(Z, lipschitz=L), Z - d.bound_prox(
Z * L, lipschitz=1. / L
) / L, 'testing lagrange_prox and bound_prox starting from atom %s ' % atom
# some arguments of the constructor
nt.assert_raises(AttributeError, setattr, p, 'bound', 4.)
nt.assert_raises(AttributeError, setattr, d, 'lagrange', 4.)
nt.assert_raises(AttributeError, setattr, p, 'bound', 4.)
nt.assert_raises(AttributeError, setattr, d, 'lagrange', 4.)
for t in solveit(p, Z, W, U, linq, L, FISTA, coef_stop):
yield t
b = atom(shape, bound=bound, quadratic=q, offset=offset)
for t in solveit(b, Z, W, U, linq, L, FISTA, coef_stop):
yield t
def test_proximal_maps():
shape = 20
Z = np.random.standard_normal(shape) * 2
W = 0.02 * np.random.standard_normal(shape)
U = 0.02 * np.random.standard_normal(shape)
linq = rr.identity_quadratic(0, 0, W, 0)
for L, atom, q, offset, FISTA, coef_stop in itertools.product(
[0.5, 1, 0.1], sorted(C.conjugate_cone_pairs.keys()), [None, linq],
[None, U], [False, True], [False, True]):
p = atom(shape, quadratic=q, offset=offset)
for t in solveit(p, Z, W, U, linq, L, FISTA, coef_stop):
yield t
def test_proximal_maps():
shape = 20
Z = np.random.standard_normal(shape) * 2
W = 0.02 * np.random.standard_normal(shape)
U = 0.02 * np.random.standard_normal(shape)
linq = rr.identity_quadratic(0, 0, W, 0)
basis = np.linalg.svd(np.random.standard_normal((4, 20)), full_matrices=0)[2]
for L, atom, q, offset, FISTA, coef_stop in itertools.product(
[0.5, 1, 0.1], sorted(LC.conjugate_cone_pairs.keys()), [None, linq], [None, U], [False, True], [False, True]
):
p = atom(shape, basis, quadratic=q, offset=offset)
for t in solveit(p, Z, W, U, linq, L, FISTA, coef_stop):
yield t
def test_proximal_maps():
shape = 20
Z = np.random.standard_normal(shape) * 2
W = 0.02 * np.random.standard_normal(shape)
U = 0.02 * np.random.standard_normal(shape)
linq = rr.identity_quadratic(0,0,W,0)
for L, atom, q, offset, FISTA, coef_stop in itertools.product(
[0.5,1,0.1],
[C.l2_epigraph, C.l2_epigraph_polar],#sorted(C.conjugate_cone_pairs.keys()),
[None, linq],
[None, U],
[False, True],
[False, True]):
p = atom(shape, quadratic=q,
offset=offset)
for t in solveit(p, Z, W, U, linq, L, FISTA, coef_stop):
yield t
def test_proximal_maps():
shape = 20
bound = 0.14
lagrange = 0.13
Z = np.random.standard_normal(shape) * 2
W = 0.02 * np.random.standard_normal(shape)
U = 0.02 * np.random.standard_normal(shape)
linq = rr.identity_quadratic(0,0,W,0)
basis = np.linalg.svd(np.random.standard_normal((4,20)), full_matrices=0)[2]
w1 = np.ones(20) * 0.5
w2 = w1 * 0
w2[:10] = 2.
for L, atom, q, offset, FISTA, coef_stop, w in itertools.product([0.5,1,0.1],
sorted(WA.conjugate_weighted_pairs.keys()),
[None, linq],
[None, U],
[False, True],
[False, True],
[w1, w2]):
# we only have two weighted atoms,
# l1 in lagrange and supnorm in bound
print 'w: ', w.shape
if atom == WA.l1norm:
p = atom(shape, w, quadratic=q,
offset=offset, lagrange=lagrange)
else:
p = atom(shape, w, quadratic=q,
offset=offset, bound=bound)
for t in solveit(p, Z, W, U, linq, L, FISTA, coef_stop):
yield t
