# tensorplay.cholesky

Source: https://www.tensorplay.cn/docs/generated/tensorplay.cholesky.html

```python
tensorplay.cholesky(input, upper=False, *, out=None) → Tensor
```

Computes the Cholesky decomposition of a symmetric positive-definite
matrix $A$ or for batches of symmetric positive-definite matrices.

If upper is True, the returned matrix U is upper-triangular, and
the decomposition has the form:

$$A = U^TU$$

If upper is False, the returned matrix L is lower-triangular, and
the decomposition has the form:

$$A = LL^T$$

If upper is True, and $A$ is a batch of symmetric positive-definite
matrices, then the returned tensor will be composed of upper-triangular Cholesky factors
of each of the individual matrices. Similarly, when upper is False, the returned
tensor will be composed of lower-triangular Cholesky factors of each of the individual
matrices.

> **Warning**
>
> tensorplay.cholesky() is deprecated in favor of tensorplay.linalg.cholesky() L = tensorplay.cholesky(A) should be replaced with @@TPBLOCK_0@@

U = tensorplay.cholesky(A, upper=True) should be replaced with

```
U = tensorplay.linalg.cholesky(A).mH
```

This transform will produce equivalent results for all valid (symmetric positive definite) inputs.

Parameters:

- input ([Tensor](/docs/generated/tensorplay.Tensor.html#tensorplay.Tensor)) – the input tensor $A$ of size $(*, n, n)$ where * is zero or more batch dimensions consisting of symmetric positive-definite matrices.

- upper ([bool](https://docs.python.org/3/builtins/functions.html#bool), optional) – flag that indicates whether to return a upper or lower triangular matrix. Default: False

Keyword Arguments:

out ([Tensor](/docs/generated/tensorplay.Tensor.html#tensorplay.Tensor), optional) – the output matrix

Example:

```
>>> a = tensorplay.randn(3, 3)
>>> a = a @ a.mT + 1e-3 # make symmetric positive-definite
>>> l = tensorplay.cholesky(a)
>>> a
tensor([[ 2.4112, -0.7486,  1.4551],
        [-0.7486,  1.3544,  0.1294],
        [ 1.4551,  0.1294,  1.6724]])
>>> l
tensor([[ 1.5528,  0.0000,  0.0000],
        [-0.4821,  1.0592,  0.0000],
        [ 0.9371,  0.5487,  0.7023]])
>>> l @ l.mT
tensor([[ 2.4112, -0.7486,  1.4551],
        [-0.7486,  1.3544,  0.1294],
        [ 1.4551,  0.1294,  1.6724]])
>>> a = tensorplay.randn(3, 2, 2) # Example for batched input
>>> a = a @ a.mT + 1e-03 # make symmetric positive-definite
>>> l = tensorplay.cholesky(a)
>>> z = l @ l.mT
>>> tensorplay.dist(z, a)
tensor(2.3842e-07)
```