def test_simple():
Z = np.random.standard_normal(100) * 4
p = rr.l1norm(100, lagrange=0.13)
L = 0.14
loss = rr.quadratic.shift(-Z, coef=L)
problem = rr.simple_problem(loss, p)
solver = rr.FISTA(problem)
solver.fit(tol=1.0e-10, debug=True)
simple_coef = solver.composite.coefs
prox_coef = p.proximal(rr.identity_quadratic(L, Z, 0, 0))
p2 = rr.l1norm(100, lagrange=0.13)
p2 = copy(p)
p2.quadratic = rr.identity_quadratic(L, Z, 0, 0)
problem = rr.simple_problem.nonsmooth(p2)
solver = rr.FISTA(problem)
solver.fit(tol=1.0e-14, debug=True)
simple_nonsmooth_coef = solver.composite.coefs
p = rr.l1norm(100, lagrange=0.13)
p.quadratic = rr.identity_quadratic(L, Z, 0, 0)
problem = rr.simple_problem.nonsmooth(p)
simple_nonsmooth_gengrad = gengrad(problem, L, tol=1.0e-10)
p = rr.l1norm(100, lagrange=0.13)
problem = rr.separable_problem.singleton(p, loss)
solver = rr.FISTA(problem)
solver.fit(tol=1.0e-10)
separable_coef = solver.composite.coefs
loss2 = rr.quadratic.shift(-Z, coef=0.6*L)
loss2.quadratic = rr.identity_quadratic(0.4*L, Z, 0, 0)
p.coefs *= 0
problem2 = rr.simple_problem(loss2, p)
loss2_coefs = problem2.solve(coef_stop=True)
solver2 = rr.FISTA(problem2)
solver2.fit(tol=1.0e-10, debug=True, coef_stop=True)
yield ac, prox_coef, simple_nonsmooth_gengrad, 'prox to nonsmooth gengrad'
yield ac, prox_coef, separable_coef, 'prox to separable'
yield ac, prox_coef, simple_nonsmooth_coef, 'prox to simple_nonsmooth'
yield ac, prox_coef, simple_coef, 'prox to simple'
yield ac, prox_coef, loss2_coefs, 'simple where loss has quadratic 1'
yield ac, prox_coef, solver2.composite.coefs, 'simple where loss has quadratic 2'
def test_simple():
Z = np.random.standard_normal(100) * 4
p = rr.l1norm(100, lagrange=0.13)
L = 0.14
loss = rr.quadratic.shift(Z, coef=L)
problem = rr.simple_problem(loss, p)
solver = rr.FISTA(problem)
solver.fit(tol=1.0e-10, debug=True)
simple_coef = solver.composite.coefs
prox_coef = p.proximal(rr.identity_quadratic(L, Z, 0, 0))
p2 = rr.l1norm(100, lagrange=0.13)
p2 = copy(p)
p2.quadratic = rr.identity_quadratic(L, Z, 0, 0)
problem = rr.simple_problem.nonsmooth(p2)
solver = rr.FISTA(problem)
solver.fit(tol=1.0e-14, debug=True)
simple_nonsmooth_coef = solver.composite.coefs
p = rr.l1norm(100, lagrange=0.13)
p.quadratic = rr.identity_quadratic(L, Z, 0, 0)
problem = rr.simple_problem.nonsmooth(p)
simple_nonsmooth_gengrad = gengrad(problem, L, tol=1.0e-10)
p = rr.l1norm(100, lagrange=0.13)
problem = rr.separable_problem.singleton(p, loss)
solver = rr.FISTA(problem)
solver.fit(tol=1.0e-10)
separable_coef = solver.composite.coefs
loss2 = rr.quadratic.shift(Z, coef=0.6 * L)
loss2.quadratic = rr.identity_quadratic(0.4 * L, Z, 0, 0)
p.coefs *= 0
problem2 = rr.simple_problem(loss2, p)
loss2_coefs = problem2.solve(coef_stop=True)
solver2 = rr.FISTA(problem2)
solver2.fit(tol=1.0e-10, debug=True, coef_stop=True)
yield all_close, prox_coef, simple_nonsmooth_gengrad, 'prox to nonsmooth gengrad', None
yield all_close, prox_coef, separable_coef, 'prox to separable', None
yield all_close, prox_coef, simple_nonsmooth_coef, 'prox to simple_nonsmooth', None
yield all_close, prox_coef, simple_coef, 'prox to simple', None
yield all_close, prox_coef, loss2_coefs, 'simple where loss has quadratic 1', None
yield all_close, prox_coef, solver2.composite.coefs, 'simple where loss has quadratic 2', None
def test_simple():
Z = np.random.standard_normal((10, 10)) * 4
p = rr.l1_l2((10, 10), lagrange=0.13)
dual = p.conjugate
L = 0.23
loss = rr.quadratic.shift(Z, coef=L)
problem = rr.simple_problem(loss, p)
solver = rr.FISTA(problem)
solver.fit(tol=1.0e-10, debug=True)
simple_coef = solver.composite.coefs
q = rr.identity_quadratic(L, Z, 0, 0)
prox_coef = p.proximal(q)
p2 = copy(p)
p2.quadratic = rr.identity_quadratic(L, Z, 0, 0)
problem = rr.simple_problem.nonsmooth(p2)
solver = rr.FISTA(problem)
solver.fit(tol=1.0e-14, debug=True)
simple_nonsmooth_coef = solver.composite.coefs
p = rr.l1_l2((10, 10), lagrange=0.13)
p.quadratic = rr.identity_quadratic(L, Z, 0, 0)
problem = rr.simple_problem.nonsmooth(p)
simple_nonsmooth_gengrad = gengrad(problem, L, tol=1.0e-10)
p = rr.l1_l2((10, 10), lagrange=0.13)
problem = rr.separable_problem.singleton(p, loss)
solver = rr.FISTA(problem)
solver.fit(tol=1.0e-10)
separable_coef = solver.composite.coefs
yield (all_close, prox_coef, simple_coef, 'prox to simple', None)
yield (all_close, prox_coef, simple_nonsmooth_gengrad,
'prox to nonsmooth gengrad', None)
yield (all_close, prox_coef, separable_coef, 'prox to separable', None)
yield (all_close, prox_coef, simple_nonsmooth_coef,
'prox to simple_nonsmooth', None)
def test_simple():
Z = np.random.standard_normal((10,10)) * 4
p = rr.l1_l2((10,10), lagrange=0.13)
dual = p.conjugate
L = 0.23
loss = rr.quadratic.shift(-Z, coef=L)
problem = rr.simple_problem(loss, p)
solver = rr.FISTA(problem)
solver.fit(tol=1.0e-10, debug=True)
simple_coef = solver.composite.coefs
q = rr.identity_quadratic(L, Z, 0, 0)
prox_coef = p.proximal(q)
p2 = copy(p)
p2.quadratic = rr.identity_quadratic(L, Z, 0, 0)
problem = rr.simple_problem.nonsmooth(p2)
solver = rr.FISTA(problem)
solver.fit(tol=1.0e-14, debug=True)
simple_nonsmooth_coef = solver.composite.coefs
p = rr.l1_l2((10,10), lagrange=0.13)
p.quadratic = rr.identity_quadratic(L, Z, 0, 0)
problem = rr.simple_problem.nonsmooth(p)
simple_nonsmooth_gengrad = gengrad(problem, L, tol=1.0e-10)
p = rr.l1_l2((10,10), lagrange=0.13)
problem = rr.separable_problem.singleton(p, loss)
solver = rr.FISTA(problem)
solver.fit(tol=1.0e-10)
separable_coef = solver.composite.coefs
ac(prox_coef, Z-simple_coef, 'prox to simple')
ac(prox_coef, simple_nonsmooth_gengrad, 'prox to nonsmooth gengrad')
ac(prox_coef, separable_coef, 'prox to separable')
ac(prox_coef, simple_nonsmooth_coef, 'prox to simple_nonsmooth')
