Skip to contents

This function skips the (slow) error checking and error message construction of linalg_cholesky(), instead directly returning the LAPACK error codes as part of a named tuple (L, info). This makes this function a faster way to check if a matrix is positive-definite, and it provides an opportunity to handle decomposition errors more gracefully or performantly than linalg_cholesky() does. Supports input of float, double, cfloat and cdouble dtypes. Also supports batches of matrices, and if A is a batch of matrices then the output has the same batch dimensions. If A is not a Hermitian positive-definite matrix, or if it's a batch of matrices and one or more of them is not a Hermitian positive-definite matrix, then info stores a positive integer for the corresponding matrix. The positive integer indicates the order of the leading minor that is not positive-definite, and the decomposition could not be completed. info filled with zeros indicates that the decomposition was successful. If check_errors=TRUE and info contains positive integers, then a RuntimeError is thrown.


linalg_cholesky_ex(A, check_errors = FALSE)



(Tensor): the Hermitian n \times n matrix or the batch of such matrices of size (*, n, n) where * is one or more batch dimensions.


(bool, optional): controls whether to check the content of infos. Default: FALSE.


If A is on a CUDA device, this function may synchronize that device with the CPU.

This function is "experimental" and it may change in a future PyTorch release.


if (torch_is_installed()) {
A <- torch_randn(2, 2)
out <- linalg_cholesky_ex(A)
#> $L
#> torch_tensor
#> -0.8449  0.0000
#>  1.3567  1.3276
#> [ CPUFloatType{2,2} ]
#> $info
#> torch_tensor
#> 1
#> [ CPUIntType{} ]