mindspore.ops.matrix_solve

mindspore.ops.matrix_solve(matrix, rhs, adjoint=False)

Solves systems of linear equations.

$$\begin{array}{r}\begin{array}{rl}& matrix[...,M,M]\ast x[...,M,K]=rhs[...,M,K]\\ & adjoint(matrix[...,M,M])\ast x[...,M,K]=rhs[...,M,K]\end{array}\end{array}$$

Warning

On GPU, if the matrix is irreversible, an error may be reported or an unknown result may be returned.

Parameters

• matrix (Tensor) – The shape of tensor is $(...,M,M)$ .

• rhs (Tensor) – The shape of tensor is $(...,M,K)$ . rhs must have the same dtype as matrix.

• adjoint (bool) – Indicating whether to solve with matrix or its (block-wise) adjoint. Default: False .

Returns

x (Tensor), The dtype and shape is the same as ‘rhs’.

Raises

• TypeError – If adjoint is not the type of bool.

• TypeError – If the type of matrix is not one of the following dtype: mstype.float16, mstype.float32, mstype.float64, mstype.complex64, mstype.complex128.

• TypeError – If the type of matrix is not the same as that of rhs.

• ValueError – If the rank of matrix less than 2.

• ValueError – If the dimension of matrix is not the same as rhs.

• ValueError – If the inner-most 2 dimension of matrix is not the same.

• ValueError – If the inner-most 2 dimension of rhs does not match matrix.

• ValueError – If the matrix is irreversible.

Supported Platforms:

Ascend CPU

Examples

>>> import mindspore
>>> from mindspore import Tensor, ops
>>> matrix = Tensor([[5, 4], [3, 1]], mindspore.float32)
>>> rhs = Tensor([[7], [2]], mindspore.float32)
>>> result = ops.matrix_solve(matrix, rhs)
>>> print(result)
[[0.14285707]
 [1.5714287 ]]