mindspore.ops.lu_solve

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mindspore.ops.lu_solve(b, LU_data, LU_pivots)[source]

Computes the solution y to the system of linear equations \(Ay = b\) , given LU decomposition \(A\) and column vector \(b\).

LU decomposition of a matrix can be generated from mindspore.scipy.linalg.lu_factor() .

Warning

This is an experimental API that is subject to change or deletion.

Parameters

  • b (Tensor) – Column vector b in the above equation. It has shape \((*, m, k)\), where \(*\) is batch dimensions, with data type float32, float16.

  • LU_data (Tensor) – LU decomposition. It has shape \((*, m, m)\), where \(*\) is batch dimensions, that can be decomposed into an upper triangular matrix U and a lower triangular matrix L, with data type float32, float16.

  • LU_pivots (Tensor) – Permutation matrix P of LU decomposition. It has shape \((*, m)\), where \(*\) is batch dimensions, that can be converted to a permutation matrix P, with data type int32.

Returns

Tensor, the same data type as the b and LU_data.

Raises

  • TypeError – If dtype of b or LU_data is not one of: float32, float16.

  • TypeError – If dtype of LU_pivots is not: int32.

  • TypeError – If b, LU_data or LU_pivots is not Tensor.

  • TypeError – If dtype of b is not same as dtype of LU_data.

  • ValueError – If the batch dimensions of LU_pivots does not match the batch dimensions of LU_data.

  • ValueError – If b dimension less than 2, LU_data dimension less than 2 or LU_pivots dimension less than 1.

Supported Platforms:

Ascend GPU CPU

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> b = Tensor(np.array([[1], [3], [3]]), mindspore.float32)
>>> LU_data = Tensor(np.array([[2, 1, 1], [0.5, 1, 1.5], [0.5, 0, 2.5]]), mindspore.float32)
>>> LU_pivots = Tensor(np.array([2, 2, 3]), mindspore.int32)
>>> y = ops.lu_solve(b, LU_data, LU_pivots)
>>> print(y)
[[ 1.9000002]
 [-1.4000001]
 [ 0.6      ]]