# Cholesky_solve

Source:`R/gen-namespace-docs.R`

, `R/gen-namespace-examples.R`

, `R/gen-namespace.R`

`torch_cholesky_solve.Rd`

Cholesky_solve

## Arguments

- self
(Tensor) input matrix \(b\) of size \((*, m, k)\), where \(*\) is zero or more batch dimensions

- input2
(Tensor) input matrix \(u\) of size \((*, m, m)\), where \(*\) is zero of more batch dimensions composed of upper or lower triangular Cholesky factor

- upper
(bool, optional) whether to consider the Cholesky factor as a lower or upper triangular matrix. Default:

`FALSE`

.

## cholesky_solve(input, input2, upper=False, out=NULL) -> Tensor

Solves a linear system of equations with a positive semidefinite matrix to be inverted given its Cholesky factor matrix \(u\).

If `upper`

is `FALSE`

, \(u\) is and lower triangular and `c`

is
returned such that:

$$
c = (u u^T)^{{-1}} b
$$
If `upper`

is `TRUE`

or not provided, \(u\) is upper triangular
and `c`

is returned such that:

$$
c = (u^T u)^{{-1}} b
$$
`torch_cholesky_solve(b, u)`

can take in 2D inputs `b, u`

or inputs that are
batches of 2D matrices. If the inputs are batches, then returns
batched outputs `c`

## Examples

```
if (torch_is_installed()) {
a = torch_randn(c(3, 3))
a = torch_mm(a, a$t()) # make symmetric positive definite
u = torch_cholesky(a)
a
b = torch_randn(c(3, 2))
b
torch_cholesky_solve(b, u)
torch_mm(a$inverse(), b)
}
#> torch_tensor
#> 5.7628 2.1661
#> 1.8217 0.5086
#> -6.3133 -1.1261
#> [ CPUFloatType{3,2} ]
```