# Copyright 2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Sparse linear algebra submodule"""
from ... import nn
from ... import numpy as mnp
from ...ops import functional as F
from ...ops import operations as P
from ...common import Tensor, CSRTensor, COOTensor, dtype as mstype
from ...ops.composite.multitype_ops.zeros_like_impl import zeros_like
from ..linalg import solve_triangular
from ..linalg import cho_factor, cho_solve
from ..utils import _normalize_matvec, _to_tensor, _safe_normalize, _eps, _norm, _type_check, _value_check, \
_sparse_check
from ..utils_const import _raise_value_error, _raise_type_error, is_within_graph
def gram_schmidt(Q, q):
"""Do Gram–Schmidt process to normalize vector v"""
h = mnp.dot(Q.T, q)
Qh = mnp.dot(Q, h)
q = q - Qh
return q, h
def arnoldi_iteration(k, A, M, V, H):
"""Performs a single (the k'th) step of the Arnoldi process."""
v_ = V[..., k]
v = M(A(v_))
v, h = gram_schmidt(V, v)
_, v_norm_0 = _safe_normalize(v)
tol = _eps(v) * v_norm_0
unit_v, v_norm_1 = _safe_normalize(v, tol)
V[..., k + 1] = unit_v
h[k + 1] = v_norm_1
H[k, :] = h
breakdown = v_norm_1 == 0
return V, H, breakdown
def rotate_vectors(H, i, cs, sn):
"""Rotate vectors."""
x1 = H[i]
y1 = H[i + 1]
x2 = cs * x1 - sn * y1
y2 = sn * x1 + cs * y1
H[i] = x2
H[i + 1] = y2
return H
def _high_precision_cho_solve(a, b, data_type=mstype.float64):
"""As a core computing module of gmres, cholesky solver must explicitly cast to double precision."""
a = a.astype(mstype.float64)
b = b.astype(mstype.float64)
a_a = mnp.dot(a, a.T)
a_b = mnp.dot(a, b)
c, lower = cho_factor(a_a, lower=False)
factor = (c, lower)
y = cho_solve(factor, a_b)
return y.astype(data_type)
def _batch_gmres(A, b, x0, tol, restart, maxiter, M, atol):
"""
batched gmres: solve the least squares problem from scratch at the end of each GMRES iteration.
It does not allow for early termination, but has much less overhead on GPUs.
"""
# Constant tensor which avoids loop unrolling
_INT_ZERO = _to_tensor(0)
dtype = b.dtype
_, b_norm = _safe_normalize(b)
atol = mnp.maximum(tol * b_norm, _to_tensor(atol), dtype=dtype)
residual = M(b - A(x0))
unit_residual, residual_norm = _safe_normalize(residual)
k = _INT_ZERO
x = x0
while k < maxiter and residual_norm > atol:
pad_width = ((0, 0),) * unit_residual.ndim + ((0, restart),)
V = mnp.pad(unit_residual[..., None], pad_width=pad_width)
H = mnp.eye(restart, restart + 1, dtype=dtype)
k_iter = _INT_ZERO
breakdown = _to_tensor(False)
while k_iter < restart and mnp.logical_not(breakdown):
V, H, breakdown = arnoldi_iteration(k_iter, A, M, V, H)
k_iter += 1
beta_vec = mnp.zeros((restart + 1,), dtype=dtype)
beta_vec[0] = residual_norm
y = _high_precision_cho_solve(H, beta_vec, data_type=dtype)
dx = mnp.dot(V[..., :-1], y)
x = x + dx
residual = M(b - A(x))
unit_residual, residual_norm = _safe_normalize(residual)
k += 1
return x, F.select(residual_norm > atol, k, _INT_ZERO)
def _incremental_gmres(A, b, x0, tol, restart, maxiter, M, atol):
"""
incremental gmres: builds a QR decomposition for the Krylov subspace incrementally during
the GMRES process using Givens rotations. This improves numerical stability and gives a free estimate of
the residual norm that allows for early termination within a single "restart".
"""
_INT_ZERO = _to_tensor(0)
_, b_norm = _safe_normalize(b)
atol = mnp.maximum(tol * b_norm, atol)
Mb = M(b)
_, Mb_norm = _safe_normalize(Mb)
ptol = Mb_norm * mnp.minimum(1.0, atol / b_norm)
r = M(b - A(x0))
r, r_norm = _safe_normalize(r)
iters = _INT_ZERO
while iters < maxiter and r_norm > atol:
V = mnp.pad(r[..., None], ((0, 0),) * r.ndim + ((0, restart),))
dtype = mnp.result_type(b)
# Use eye() to avoid constructing a singular matrix in case of early
# Termination
R = mnp.eye(restart, restart + 1, dtype=dtype)
givens = mnp.zeros((restart, 2), dtype=dtype)
beta_vec = mnp.zeros((restart + 1), dtype=dtype)
beta_vec[0] = r_norm
k = _INT_ZERO
err = r_norm
while mnp.logical_and(mnp.less(k, restart), mnp.less(ptol, err)):
V, R, _ = arnoldi_iteration(k, A, M, V, R)
# Givens rotation
row_k = R[k, :]
i = _INT_ZERO
while i < k:
row_k = rotate_vectors(row_k, i, givens[i, 0], givens[i, 1])
i += 1
if row_k[k + 1] == 0:
givens[k, 0] = 1
givens[k, 1] = 0
else:
increase = mnp.absolute(row_k[k]) < mnp.absolute(row_k[k + 1])
t = mnp.where(increase, -row_k[k] / row_k[k + 1], -row_k[k + 1] / row_k[k])
r = 1 / F.sqrt(1 + mnp.absolute(t) ** 2)
givens[k, 0] = mnp.where(increase, r * t, r)
givens[k, 1] = mnp.where(increase, r, r * t)
R[k, :] = rotate_vectors(row_k, k, givens[k, 0], givens[k, 1])
beta_vec = rotate_vectors(beta_vec, k, givens[k, 0], givens[k, 1])
err = mnp.absolute(beta_vec[k + 1])
k += 1
y = solve_triangular(R[:, :-1], beta_vec[:-1], trans='T', lower=True)
dx = mnp.dot(V[:, :-1], y)
x = x0 + dx
r = M(b - A(x))
r, r_norm = _safe_normalize(r)
x0 = x
iters += 1
return x0, F.select(r_norm > atol, iters, _INT_ZERO)
class GMRES(nn.Cell):
"""
Given given A and b, GMRES solves the linear system:
.. math::
A x = b
"""
def __init__(self, A, M, solve_method):
super(GMRES, self).__init__()
self.A = A
self.M = M
self.solve_method = solve_method
def construct(self, b, x0, tol, restart, maxiter, atol):
# Constant tensor which avoids loop unrolling
A = _normalize_matvec(self.A)
M = _normalize_matvec(self.M)
x = x0
info = _to_tensor(0)
if self.solve_method == 'batched':
x, info = _batch_gmres(A, b, x0, tol, restart, maxiter, M, atol)
elif self.solve_method == "incremental":
x, info = _incremental_gmres(A, b, x0, tol, restart, maxiter, M, atol)
else:
_raise_value_error("solve_method should be in ('incremental' or 'batched'), but got ", self.solve_method,
".")
return x, info
class GMRESV2(nn.Cell):
"""
This is a new version of GMRES, which contains all parameters in a graph.
"""
def __init__(self, solve_method):
super(GMRESV2, self).__init__()
self.solve_method = solve_method
def transpose(self, a):
if isinstance(a, CSRTensor):
a_coo = a.to_coo()
row_indices = a_coo.indices[:, 0]
col_indices = a_coo.indices[:, 1]
coo_indices = P.Stack(1)([col_indices, row_indices])
a_t_coo = COOTensor(coo_indices, a_coo.values, a_coo.shape)
a_t_csr = a_t_coo.to_csr()
return a_t_csr
return a.T
def construct(self, A, b, x0, tol, restart, maxiter, M, atol):
A = _normalize_matvec(A)
M = _normalize_matvec(M)
x = x0
info = _to_tensor(0)
if self.solve_method == 'batched':
x, info = _batch_gmres(A, b, x0, tol, restart, maxiter, M, atol)
elif self.solve_method == "incremental":
x, info = _incremental_gmres(A, b, x0, tol, restart, maxiter, M, atol)
else:
_raise_value_error("solve_method should be in ('incremental' or 'batched'), but got ", self.solve_method,
".")
return x, info
def bprop(self, A, b, x0, tol, restart, maxiter, M, atol, out, dout):
"""
Derivatives of `gmres` are implemented via implicit differentiation with
another `gmres` solve, rather than by differentiating *through* the solver.
They will be accurate only if both solves converge.
"""
n = b.shape[0]
if not isinstance(M, (Tensor, CSRTensor)):
M = F.eye(n, n, b.dtype)
A_T = self.transpose(A)
grad_b, _ = self.construct(A_T, dout[0], x0, tol, restart, maxiter, M, atol)
if isinstance(A, CSRTensor):
grad_a_dense = -1 * F.reshape(grad_b, (n, 1)) * F.reshape(out[0], (1, n))
values = F.csr_gather(A.indptr, A.indices, grad_a_dense, A.shape)
grad_a = CSRTensor(A.indptr, A.indices, values, A.shape)
else:
grad_a = -1 * F.reshape(grad_b, (n, 1)) * F.reshape(out[0], (1, n))
return grad_a, grad_b, zeros_like(x0), zeros_like(tol), zeros_like(atol), zeros_like(maxiter), zeros_like(M)
[文档]def gmres(A, b, x0=None, *, tol=1e-5, restart=20, maxiter=None,
M=None, callback=None, restrt=None, atol=0.0, callback_type=None, solve_method='batched'):
"""
Given given A and b, GMRES solves the linear system:
.. math::
A x = b
A is specified as a function performing A(vi) -> vf = A @ vi, and in principle
need not have any particular special properties, such as symmetry. However,
convergence is often slow for nearly symmetric operators.
Note:
- `gmres` is not supported on Windows platform yet.
Args:
A (Union[Tensor, function]): 2D Tensor or function that calculates the linear
map (matrix-vector product) :math:`Ax` when called like :math:`A(x)`.
As function, `A` must return Tensor with the same structure and shape as its input matrix.
b (Tensor): Right hand side of the linear system representing a single vector.
Can be stored as a Tensor.
x0 (Tensor, optional): Starting guess for the solution. Must have the same structure
as `b`. If this is unspecified, zeroes are used. Default: None.
tol (float, optional): Tolerances for convergence,
:math:`norm(residual) <= max(tol*norm(b), atol)`. We do not implement SciPy's
"legacy" behavior, so MindSpore's tolerance will differ from SciPy unless you
explicitly pass `atol` to SciPy's `gmres`. Default: 1e-5.
restart (integer, optional): Size of the Krylov subspace ("number of iterations")
built between restarts. GMRES works by approximating the true solution x as its
projection into a Krylov space of this dimension - this parameter
therefore bounds the maximum accuracy achievable from any guess
solution. Larger values increase both number of iterations and iteration
cost, but may be necessary for convergence. The algorithm terminates
early if convergence is achieved before the full subspace is built. Default: 20.
maxiter (int): Maximum number of times to rebuild the size-`restart`
Krylov space starting from the solution found at the last iteration. If GMRES
halts or is very slow, decreasing this parameter may help. Default: None.
M (Union[Tensor, function]): Preconditioner for A. The preconditioner should approximate the
inverse of A. Effective preconditioning dramatically improves the
rate of convergence, which implies that fewer iterations are needed
to reach a given error tolerance. Default: None.
callback (function): User-supplied function to call after each iteration. It is called as callback(args),
where args are selected by callback_type. Default: None.
restrt (int, optional): Deprecated, use restart instead. Default: None.
atol (float, optional): The same as `tol`. Default: 0.0.
callback_type (str, optional): Callback function argument requested:
Default: None.
- x: current iterate (ndarray), called on every restart
- pr_norm: relative (preconditioned) residual norm (float), called on every inner iteration
- legacy (default): same as pr_norm, but also changes the meaning of ‘maxiter’ to count inner
iterations instead of restart cycles.
solve_method (str): There are two kinds of solve methods,'incremental' or 'batched'. Default: "batched".
- incremental: builds a QR decomposition for the Krylov subspace incrementally during
the GMRES process using Givens rotations. This improves numerical stability and gives
a free estimate of the residual norm that allows for early termination within a single "restart".
- batched: solve the least squares problem from scratch at the end of each GMRES
iteration. It does not allow for early termination, but has much less overhead on GPUs.
Returns:
- Tensor, the converged solution. Has the same structure as `b`.
- Tensor, placeholder for convergence information: 0 : successful exit.
>0 : convergence to tolerance not achieved, number of iterations. <0 : illegal input or breakdown.
Supported Platforms:
``CPU`` ``GPU``
Examples:
>>> import numpy as onp
>>> import mindspore.numpy as mnp
>>> from mindspore.common import Tensor
>>> from mindspore.scipy.sparse.linalg import gmres
>>> A = Tensor(mnp.array([[3, 2, 0], [1, -1, 0], [0, 5, 1]], dtype=mnp.float32))
>>> b = Tensor(mnp.array([2, 4, -1], dtype=mnp.float32))
>>> x, exitCode = gmres(A, b)
>>> print(exitCode) # 0 indicates successful convergence
0
>>> print(onp.allclose(mnp.dot(A,x).asnumpy(), b.asnumpy()))
True
"""
func_name = "gmres"
A, M, b, x0 = _sparse_check(func_name, A, M, b, x0)
size = b.size
if maxiter is None:
maxiter = 10 * size # copied from scipy
_type_check(func_name, tol, float, 'tol')
_type_check(func_name, restart, int, 'restart')
_type_check(func_name, maxiter, int, 'maxiter')
_type_check(func_name, solve_method, str, 'solve_method')
_value_check(func_name, callback, None, 'callback', op='is', fmt='todo')
_value_check(func_name, restrt, None, 'restrt', op='is', fmt='todo')
_value_check(func_name, callback_type, None, 'callback_type', op='is', fmt='todo')
if restart > size:
restart = size
if not is_within_graph(A):
x, info = GMRES(A, M, solve_method)(b, x0, tol, restart, maxiter, atol)
else:
x, info = GMRESV2(solve_method)(A, b, x0, tol, restart, maxiter, M, atol)
return x, info
def _cg(A, b, x0, tol, atol, maxiter, M):
"""
Figure 2.5 from Barrett R, et al. 'Templates for the sulution of linear systems:
building blocks for iterative methods', 1994, pg. 12-14
"""
# Constant tensor which avoids loop unrolling
_INT_ZERO = _to_tensor(0)
atol_ = mnp.maximum(atol, tol * _norm(b))
r = b - A(x0)
z = p = M(r)
rho = mnp.dot(r, z)
k = _INT_ZERO
x = x0
while k < maxiter and _norm(r) > atol_:
q = A(p)
alpha = rho / mnp.dot(p, q)
x = x + alpha * p
r = r - alpha * q
z = M(r)
rho_ = mnp.dot(r, z)
beta = rho_ / rho
p = z + beta * p
rho = rho_
k += 1
return x, F.select(_norm(r) > atol_, k, _INT_ZERO)
class CG(nn.Cell):
"""Use Conjugate Gradient iteration to solve the linear system:
.. math::
A x = b
"""
def __init__(self, A, M):
super(CG, self).__init__()
self.A = A
self.M = M
def construct(self, b, x0, tol, atol, maxiter):
A = _normalize_matvec(self.A)
M = _normalize_matvec(self.M)
return _cg(A, b, x0, tol, atol, maxiter, M)
class CGv2(nn.Cell):
"""
This is a new version of CG, which contains all parameters in a graph.
"""
def __init__(self):
super(CGv2, self).__init__()
def construct(self, A, b, x0, tol, atol, maxiter, M):
A = _normalize_matvec(A)
M = _normalize_matvec(M)
return _cg(A, b, x0, tol, atol, maxiter, M)
def bprop(self, A, b, x0, tol, atol, maxiter, M, out, dout):
"""
Derivatives of `cg` are implemented via implicit differentiation with
another `cg` solve, rather than by differentiating *through* the solver.
They will be accurate only if both solves converge.
"""
n = b.shape[0]
if not isinstance(M, (Tensor, CSRTensor)):
M = F.eye(n, n, b.dtype)
grad_b, _ = self.construct(A, dout[0], x0, tol, atol, maxiter, M)
if isinstance(A, CSRTensor):
grad_a_dense = -1 * F.reshape(grad_b, (n, 1)) * F.reshape(out[0], (1, n))
values = F.csr_gather(A.indptr, A.indices, grad_a_dense, A.shape)
grad_a = CSRTensor(A.indptr, A.indices, values, A.shape)
else:
grad_a = -1 * F.reshape(grad_b, (n, 1)) * F.reshape(out[0], (1, n))
return grad_a, grad_b, zeros_like(x0), zeros_like(tol), zeros_like(atol), zeros_like(maxiter), zeros_like(M)
[文档]def cg(A, b, x0=None, *, tol=1e-5, atol=0.0, maxiter=None, M=None, callback=None):
"""Use Conjugate Gradient iteration to solve the linear system:
.. math::
A x = b
The numerics of MindSpore's `cg` should exact match SciPy's `cg` (up to
numerical precision).
Derivatives of `cg` are implemented via implicit differentiation with
another `cg` solve, rather than by differentiating *through* the solver.
They will be accurate only if both solves converge.
Note:
- Input `A` must represent a hermitian, positive definite matrix. If not,
the output is wrong and inconsistent with scipy.
- `cg` is not supported on Windows platform yet.
Args:
A (Union[Tensor, CSRTensor, function]): 2D Tensor, CSRTensor or function that calculates the linear
map (matrix-vector product) :math:`Ax` when called like :math:`A(x)`.
As function, `A` must return Tensor with the same structure and shape as its input matrix.
b (Tensor): Right hand side of the linear system representing a single vector. Can be
stored as a Tensor.
x0 (Tensor): Starting guess for the solution. Must have the same structure as `b`. Default: None.
tol (float, optional): Tolerances for convergence, :math:`norm(residual) <= max(tol*norm(b), atol)`.
We do not implement SciPy's "legacy" behavior, so MindSpore's tolerance will
differ from SciPy unless you explicitly pass `atol` to SciPy's `cg`. Default: 1e-5.
atol (float, optional): The same as `tol`. Default: 0.0.
maxiter (int): Maximum number of iterations. Iteration will stop after maxiter
steps even if the specified tolerance has not been achieved. Default: None.
M (Union[Tensor, function]): Preconditioner for A. The preconditioner should approximate the
inverse of A. Effective preconditioning dramatically improves the
rate of convergence, which implies that fewer iterations are needed
to reach a given error tolerance. Default: None.
callback (function, optional): User-supplied function to call after each iteration.
It is called as callback(xk), where xk is the current solution vector. Default: None.
Returns:
- Tensor, the converged solution. Has the same structure as `b`.
- Tensor, placeholder for convergence information: 0 : successful exit.
>0 : convergence to tolerance not achieved, number of iterations. <0 : illegal input or breakdown.
Raises:
TypeError: If `tol` is not float.
TypeError: If `atol` is not float.
TypeError: If `maxiter` is not int.
ValueError: If `callback` is not None.
TypeError: If `A` is not Tensor, CSRTensor, or Function.
TypeError: If `M` is not None, Tensor, CSRTensor, or Function.
TypeError: If `b` is not Tensor.
TypeError: If `x0` is not None or Tensor.
ValueError: If `b` is not 1 or 2 dimension.
ValueError: If `x0` and `b` don't have the same structure and type.
ValueError: If `A` is a square matrix.
ValueError: If `M` is a square matrix when `M` is not a function.
TypeError: If `A` and `b` don't have the same data types.
Supported Platforms:
``CPU`` ``GPU``
Examples:
>>> import numpy as onp
>>> from mindspore.common import Tensor
>>> from mindspore.scipy.sparse.linalg import cg
>>> A = Tensor(onp.array([[1, 2], [2, 1]], dtype='float32'))
>>> b = Tensor(onp.array([1, -1], dtype='float32'))
>>> result, info = cg(A, b)
>>> print(result)
[-1. 1.]
>>> print(info)
0
"""
func_name = 'cg'
A, M, b, x0 = _sparse_check(func_name, A, M, b, x0)
if maxiter is None:
maxiter = 10 * b.size # copied from scipy
_type_check(func_name, tol, float, 'tol')
_type_check(func_name, atol, float, 'atol')
_type_check(func_name, maxiter, int, 'maxiter')
_value_check(func_name, callback, None, 'callback', op='is', fmt='todo')
if not is_within_graph(A):
x, info = CG(A, M)(b, x0, tol, atol, maxiter)
else:
x, info = CGv2()(A, b, x0, tol, atol, maxiter, M)
return x, info
class BiCGStab(nn.Cell):
"""Figure 2.10 from Barrett R, et al. 'Templates for the sulution of linear systems:
building blocks for iterative methods', 1994, pg. 24-25
"""
def __init__(self, A, M):
super(BiCGStab, self).__init__()
self.A = A
self.M = M
def construct(self, b, x0, tol, atol, maxiter):
# Constant tensors which avoid loop unrolling
_INT_ZERO = _to_tensor(0)
_INT_NEG_ONE = _to_tensor(-1)
A = _normalize_matvec(self.A)
M = _normalize_matvec(self.M)
_FLOAT_ONE = _to_tensor(1., dtype=b.dtype)
atol_ = mnp.maximum(atol, tol * _norm(b))
r = r_tilde = v = p = b - A(x0)
rho = alpha = omega = _FLOAT_ONE
k = _INT_ZERO
x = x0
while k < maxiter:
rho_ = mnp.dot(r_tilde, r)
if rho_ == 0. or omega == 0.:
k = _INT_NEG_ONE
break
beta = rho_ / rho * (alpha / omega)
p = r + beta * (p - omega * v)
p_hat = M(p)
v = A(p_hat)
alpha = rho_ / mnp.dot(r_tilde, v)
s = r - alpha * v
x = x + alpha * p_hat
if _norm(s) <= atol_:
break
s_hat = M(s)
t = A(s_hat)
omega = mnp.dot(t, s) / mnp.dot(t, t)
x = x + omega * s_hat
r = s - omega * t
if _norm(r) <= atol_:
break
rho = rho_
k += 1
return x, F.select(k == _INT_NEG_ONE or k >= maxiter, k, _INT_ZERO)
def bicgstab(A, b, x0=None, *, tol=1e-5, atol=0.0, maxiter=None, M=None):
"""Use Bi-Conjugate Gradient Stable iteration to solve :math:`Ax = b`.
The numerics of MindSpore's `bicgstab` should exact match SciPy's
`bicgstab` (up to numerical precision).
As with `cg`, derivatives of `bicgstab` are implemented via implicit
differentiation with another `bicgstab` solve, rather than by
differentiating *through* the solver. They will be accurate only if
both solves converge.
Note:
- `bicgstab` is not supported on Windows platform yet.
Args:
A (Union[Tensor, function]): 2D Tensor or function that calculates the linear
map (matrix-vector product) :math:`Ax` when called like :math:`A(x)`.
As function, `A` must return Tensor with the same structure and shape as its input matrix.
b (Tensor): Right hand side of the linear system representing a single vector. Can be
stored as a Tensor.
x0 (Tensor): Starting guess for the solution. Must have the same structure as `b`. Default: None.
tol (float, optional): Tolerances for convergence, :math:`norm(residual) <= max(tol*norm(b), atol)`.
We do not implement SciPy's "legacy" behavior, so MindSpore's tolerance will
differ from SciPy unless you explicitly pass `atol` to SciPy's `bicgstab`. Default: 1e-5.
atol (float, optional): The same as `tol`. Default: 0.0.
maxiter (int): Maximum number of iterations. Iteration will stop after maxiter
steps even if the specified tolerance has not been achieved. Default: None.
M (Union[Tensor, function]): Preconditioner for A. The preconditioner should approximate the
inverse of A. Effective preconditioning dramatically improves the
rate of convergence, which implies that fewer iterations are needed
to reach a given error tolerance. Default: None.
Returns:
- Tensor, the converged solution. Has the same structure as `b`.
- Tensor, placeholder for convergence information: 0 : successful exit.
>0 : convergence to tolerance not achieved, number of iterations. <0 : illegal input or breakdown.
Raises:
ValueError: If `x0` and `b` don't have the same structure.
TypeError: If `A`, `x0` and `b` don't have the same float types(`mstype.float32` or `mstype.float64`).
Supported Platforms:
``CPU`` ``GPU``
Examples:
>>> import numpy as onp
>>> from mindspore.common import Tensor
>>> from mindspore.scipy.sparse.linalg import bicgstab
>>> A = Tensor(onp.array([[1, 2], [2, 1]], dtype='float32'))
>>> b = Tensor(onp.array([1, -1], dtype='float32'))
>>> result, info = bicgstab(A, b)
>>> print(result)
[-1. 1.]
>>> print(info)
0
"""
if x0 is None:
x0 = mnp.zeros_like(b)
if maxiter is None:
maxiter = 10 * b.shape[0]
if M is None:
M = lambda x: x
if x0.shape != b.shape:
_raise_value_error(
'Input x0 and b must have matching shapes: ', x0.shape, ' vs ', b.shape)
if (F.dtype(b) not in (mstype.float32, mstype.float64)) or (F.dtype(b) != F.dtype(x0)) or (
F.dtype(b) != F.dtype(A)):
_raise_type_error('Input A, x0 and b must have same float types')
x, info = BiCGStab(A, M)(b, x0, tol, atol, maxiter)
return x, info