mindspore.ops.matrix_solve

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

Solves systems of linear equations.

\[\begin{split}\begin{aligned} &matrix[..., M, M] * x[..., M, K] = rhs[..., M, K]\\ &adjoint(matrix[..., M, M]) * x[..., M, K] = rhs[..., M, K] \end{aligned}\end{split}\]

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

>>> 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 ]]