mindspore.ops.MatrixSolve
- class mindspore.ops.MatrixSolve(adjoint=False)[source]
Solves systems of linear equations.
- Parameters
adjoint (bool, optional) – Indicates whether the adjoint of the matrix is used during the computation. Default:
False
, use its transpose instead.
- Inputs:
matrix (Tensor) - A tensor of shape \((..., M, M)\), is a matrix of coefficients for a system of linear equations.
rhs (Tensor) - A tensor of shape \((..., M, K)\), is a matrix of the resulting values of a system of linear equations. rhs must have the same type as matrix.
- Outputs:
Tensor, a matrix composed of solutions to a system of linear equations, which has the same type and shape 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 .
- Supported Platforms:
Ascend
CPU
Examples
>>> import mindspore >>> import numpy as np >>> from mindspore import Tensor, ops >>> matrix = Tensor(np.array([[1.0 , 4.0], ... [2.0 , 7.0]]), mindspore.float32) >>> rhs = Tensor(np.array([[1.0] , [3.0]]), mindspore.float32) >>> matrix_solve = ops.MatrixSolve(adjoint = False) >>> output = matrix_solve(matrix, rhs) >>> print(output) [[5.0] [-1.0]]