def test_posv(self):
if not cupy.cusolver.check_availability('potrsBatched'):
pytest.skip('potrsBatched is not available')
a = self._create_posdef_matrix(cupy, self.shape, self.dtype)
n = a.shape[-1]
identity_matrix = cupy.eye(n, dtype=a.dtype)
b = cupy.empty(a.shape, a.dtype)
b[...] = identity_matrix
with cupyx.errstate(linalg='ignore'):
with pytest.raises(cupy.cuda.cusolver.CUSOLVERError):
lapack.posv(a, b)
def invh(a):
"""Compute the inverse of a Hermitian matrix.
This function computes a inverse of a real symmetric or complex hermitian
positive-definite matrix using Cholesky factorization. If matrix ``a`` is
not positive definite, Cholesky factorization fails and it raises an error.
Args:
a (cupy.ndarray): Real symmetric or complex hermitian maxtix.
Returns:
cupy.ndarray: The inverse of matrix ``a``.
"""
_util._assert_cupy_array(a)
_util._assert_nd_squareness(a)
# TODO: Remove this assert once cusolver supports nrhs > 1 for potrsBatched
_util._assert_rank2(a)
n = a.shape[-1]
identity_matrix = cupy.eye(n, dtype=a.dtype)
b = cupy.empty(a.shape, a.dtype)
b[...] = identity_matrix
return lapack.posv(a, b)
def test_posv(self, xp, dtype):
# TODO: cusolver does not support nrhs > 1 for potrsBatched
if len(self.shape) > 2 and self.nrhs and self.nrhs > 1:
pytest.skip('cusolver does not support nrhs > 1 for potrsBatched')
a = self._create_posdef_matrix(xp, self.shape, dtype)
b_shape = list(self.shape[:-1])
if self.nrhs is not None:
b_shape.append(self.nrhs)
b = testing.shaped_random(b_shape, xp, dtype=dtype)
if xp == cupy:
return lapack.posv(a, b)
else:
return numpy.linalg.solve(a, b)
def invh(a):
"""Compute the inverse of a Hermitian matrix.
This function computes a inverse of a real symmetric or complex hermitian
positive-definite matrix using Cholesky factorization. If matrix ``a`` is
not positive definite, Cholesky factorization fails and it raises an error.
Args:
a (cupy.ndarray): Real symmetric or complex hermitian maxtix.
Returns:
cupy.ndarray: The inverse of matrix ``a``.
"""
_util._assert_cupy_array(a)
# TODO: Use `_assert_stacked_2d` instead, once cusolver supports nrhs > 1
# for potrsBatched
_util._assert_2d(a)
_util._assert_stacked_square(a)
b = _util.stacked_identity_like(a)
return lapack.posv(a, b)