mindspore.ops.Cholesky

class mindspore.ops.Cholesky(upper=False)[source]

Performs the Cholesky decomposition on a single or a batch of symmetric positive-definite matrices.

Warning

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

Refer to mindspore.ops.cholesky() for more details.

Parameters: upper (bool, optional) – Flag that indicates whether to return a upper or lower triangular matrix. Default: False .

Inputs:

input_x (Tensor) - Tensor of shape \((*, N, N)\), where \(*\) is zero or more batch dimensions consisting of symmetric positive-definite matrices, with float32 or float64 data type.

Outputs:

Tensor, has the same shape and data type as input_x.

Supported Platforms:

GPU CPU

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> input_x = Tensor(np.array([[1.0, 1.0], [1.0, 2.0]]), mindspore.float32)
>>> cholesky = ops.Cholesky(upper=False)
>>> output = cholesky(input_x)
>>> print(output)
[[1. 0.]
 [1. 1.]]