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- #!/usr/bin/env python
- """Tests for the linalg.isolve.gcrotmk module
- """
- from __future__ import division, print_function, absolute_import
- from numpy.testing import assert_, assert_allclose, assert_equal
- from scipy._lib._numpy_compat import suppress_warnings
- import numpy as np
- from numpy import zeros, array, allclose
- from scipy.linalg import norm
- from scipy.sparse import csr_matrix, eye, rand
- from scipy.sparse.linalg.interface import LinearOperator
- from scipy.sparse.linalg import splu
- from scipy.sparse.linalg.isolve import gcrotmk, gmres
- Am = csr_matrix(array([[-2,1,0,0,0,9],
- [1,-2,1,0,5,0],
- [0,1,-2,1,0,0],
- [0,0,1,-2,1,0],
- [0,3,0,1,-2,1],
- [1,0,0,0,1,-2]]))
- b = array([1,2,3,4,5,6])
- count = [0]
- def matvec(v):
- count[0] += 1
- return Am*v
- A = LinearOperator(matvec=matvec, shape=Am.shape, dtype=Am.dtype)
- def do_solve(**kw):
- count[0] = 0
- with suppress_warnings() as sup:
- sup.filter(DeprecationWarning, ".*called without specifying.*")
- x0, flag = gcrotmk(A, b, x0=zeros(A.shape[0]), tol=1e-14, **kw)
- count_0 = count[0]
- assert_(allclose(A*x0, b, rtol=1e-12, atol=1e-12), norm(A*x0-b))
- return x0, count_0
- class TestGCROTMK(object):
- def test_preconditioner(self):
- # Check that preconditioning works
- pc = splu(Am.tocsc())
- M = LinearOperator(matvec=pc.solve, shape=A.shape, dtype=A.dtype)
- x0, count_0 = do_solve()
- x1, count_1 = do_solve(M=M)
- assert_equal(count_1, 3)
- assert_(count_1 < count_0/2)
- assert_(allclose(x1, x0, rtol=1e-14))
- def test_arnoldi(self):
- np.random.rand(1234)
- A = eye(2000) + rand(2000, 2000, density=5e-4)
- b = np.random.rand(2000)
- # The inner arnoldi should be equivalent to gmres
- with suppress_warnings() as sup:
- sup.filter(DeprecationWarning, ".*called without specifying.*")
- x0, flag0 = gcrotmk(A, b, x0=zeros(A.shape[0]), m=15, k=0, maxiter=1)
- x1, flag1 = gmres(A, b, x0=zeros(A.shape[0]), restart=15, maxiter=1)
- assert_equal(flag0, 1)
- assert_equal(flag1, 1)
- assert_(np.linalg.norm(A.dot(x0) - b) > 1e-3)
- assert_allclose(x0, x1)
- def test_cornercase(self):
- np.random.seed(1234)
- # Rounding error may prevent convergence with tol=0 --- ensure
- # that the return values in this case are correct, and no
- # exceptions are raised
- for n in [3, 5, 10, 100]:
- A = 2*eye(n)
- with suppress_warnings() as sup:
- sup.filter(DeprecationWarning, ".*called without specifying.*")
- b = np.ones(n)
- x, info = gcrotmk(A, b, maxiter=10)
- assert_equal(info, 0)
- assert_allclose(A.dot(x) - b, 0, atol=1e-14)
- x, info = gcrotmk(A, b, tol=0, maxiter=10)
- if info == 0:
- assert_allclose(A.dot(x) - b, 0, atol=1e-14)
- b = np.random.rand(n)
- x, info = gcrotmk(A, b, maxiter=10)
- assert_equal(info, 0)
- assert_allclose(A.dot(x) - b, 0, atol=1e-14)
- x, info = gcrotmk(A, b, tol=0, maxiter=10)
- if info == 0:
- assert_allclose(A.dot(x) - b, 0, atol=1e-14)
- def test_nans(self):
- A = eye(3, format='lil')
- A[1,1] = np.nan
- b = np.ones(3)
- with suppress_warnings() as sup:
- sup.filter(DeprecationWarning, ".*called without specifying.*")
- x, info = gcrotmk(A, b, tol=0, maxiter=10)
- assert_equal(info, 1)
- def test_truncate(self):
- np.random.seed(1234)
- A = np.random.rand(30, 30) + np.eye(30)
- b = np.random.rand(30)
- for truncate in ['oldest', 'smallest']:
- with suppress_warnings() as sup:
- sup.filter(DeprecationWarning, ".*called without specifying.*")
- x, info = gcrotmk(A, b, m=10, k=10, truncate=truncate, tol=1e-4,
- maxiter=200)
- assert_equal(info, 0)
- assert_allclose(A.dot(x) - b, 0, atol=1e-3)
- def test_CU(self):
- for discard_C in (True, False):
- # Check that C,U behave as expected
- CU = []
- x0, count_0 = do_solve(CU=CU, discard_C=discard_C)
- assert_(len(CU) > 0)
- assert_(len(CU) <= 6)
- if discard_C:
- for c, u in CU:
- assert_(c is None)
- # should converge immediately
- x1, count_1 = do_solve(CU=CU, discard_C=discard_C)
- if discard_C:
- assert_equal(count_1, 2 + len(CU))
- else:
- assert_equal(count_1, 3)
- assert_(count_1 <= count_0/2)
- assert_allclose(x1, x0, atol=1e-14)
- def test_denormals(self):
- # Check that no warnings are emitted if the matrix contains
- # numbers for which 1/x has no float representation, and that
- # the solver behaves properly.
- A = np.array([[1, 2], [3, 4]], dtype=float)
- A *= 100 * np.nextafter(0, 1)
- b = np.array([1, 1])
- with suppress_warnings() as sup:
- sup.filter(DeprecationWarning, ".*called without specifying.*")
- xp, info = gcrotmk(A, b)
- if info == 0:
- assert_allclose(A.dot(xp), b)
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