mindspore.ops.cholesky
- mindspore.ops.cholesky(input_x, upper=False)[source]
Returns the Cholesky decomposition of zero or more batch dimensions consisting of symmetric positive-definite matrices.
If upper is True, returns an upper-triangular matrix, \(U\), and the decomposition has the form:
\[A = U^TU\]If upper is False, returns a lower-triangular matrix, \(L\), and the decomposition has the form:
\[A = LL^T\]where A is the symmetric positive-definite matrix.
- Parameters
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.
upper (bool) – If upper is True, returns an upper-triangular matrix. If upper is False, returns a lower-triangular matrix. Default:
False
.
- Returns
Tensor, has the same shape and data type as input_x.
- Raises
TypeError – If upper is not a bool.
TypeError – If dtype of input_x is not one of: float64, float32.
TypeError – If input_x is not a Tensor.
ValueError – If input_x is not a or a batch of square matrix.
ValueError – If input_x is not symmetric positive definite.
- 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) >>> output = ops.cholesky(input_x, upper=False) >>> print(output) [[1. 0.] [1. 1.]]