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Specifications and Common Mistakes

- Specifications and Common Mistakes:

- Misspellings or punctuation mistakes,incorrect formulas, abnormal display.

- Incorrect links, empty cells, or wrong formats.

- Chinese characters in English context.

- Minor inconsistencies between the UI and descriptions.

- Low writing fluency that does not affect understanding.

- Incorrect version numbers, including software package names and version numbers on the UI.

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- Incorrect or missing key steps.

- Missing main function descriptions, keyword explanation, necessary prerequisites, or precautions.

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- Unclear logic, such as missing classifications, items, and steps.

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- Technical principles, function descriptions, supported platforms, parameter types, or exceptions inconsistent with that of software implementation.

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- Incorrect commands or command parameters.

- Incorrect code.

- Commands inconsistent with the functions.

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- Sample code running error, or running results inconsistent with the expectation.

Risk Warnings

- Risk Warnings:

- Lack of risk warnings for operations that may damage the system or important data.

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- Contents that may violate applicable laws and regulations or geo-cultural context-sensitive words and expressions.

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Problem description

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mindspore.ops.lu_solve

View Source On Gitee
mindspore.ops.lu_solve(b, LU_data, LU_pivots)[source]

Compute the solution to a system of linear equations Ay=b.

Note

  • b of shape (,m,k) , LU_data of shape (,m,m) , LU_pivots of shape (,m) , where means batch dimensions.

Warning

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

Parameters
  • b (Tensor) – Column vector b in the above equation.

  • LU_data (Tensor) – LU decomposition. The A in the formula above.

  • LU_pivots (Tensor) – Permutation matrix P of LU decomposition.

Returns

Tensor

Supported Platforms:

Ascend GPU CPU

Examples

>>> import mindspore
>>> b = mindspore.tensor([[1.], [3.], [3.]])
>>> LU_data = mindspore.tensor([[2., 1., 1.], [0.5, 1., 1.5], [0.5, 0., 2.5]])
>>> LU_pivots = mindspore.tensor(([2, 2, 3]), mindspore.int32)
>>> y = mindspore.ops.lu_solve(b, LU_data, LU_pivots)
>>> print(y)
[[ 1.9000001]
 [-1.4000001]
 [ 0.6      ]]