linalg_inv_ex: Computes the inverse of a square matrix if it is invertible.

Description Usage Arguments Note See Also Examples

View source: R/linalg.R

Description

Returns a namedtuple (inverse, info). inverse contains the result of inverting A and info stores the LAPACK error codes. If A is not an invertible matrix, or if it's a batch of matrices and one or more of them is not an invertible matrix, then info stores a positive integer for the corresponding matrix. The positive integer indicates the diagonal element of the LU decomposition of the input matrix that is exactly zero. info filled with zeros indicates that the inversion was successful. If check_errors=TRUE and info contains positive integers, then a RuntimeError is thrown. 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.

Usage

1
linalg_inv_ex(A, check_errors = FALSE)

Arguments

A

(Tensor): tensor of shape (*, n, n) where * is zero or more batch dimensions consisting of square matrices.

check_errors

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

Note

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

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

See Also

linalg_inv() is a NumPy compatible variant that always checks for errors.

Other linalg: linalg_cholesky_ex(), linalg_cholesky(), linalg_det(), linalg_eigh(), linalg_eigvalsh(), linalg_eigvals(), linalg_eig(), linalg_householder_product(), linalg_inv(), linalg_lstsq(), linalg_matrix_norm(), linalg_matrix_power(), linalg_matrix_rank(), linalg_multi_dot(), linalg_norm(), linalg_pinv(), linalg_qr(), linalg_slogdet(), linalg_solve(), linalg_svdvals(), linalg_svd(), linalg_tensorinv(), linalg_tensorsolve(), linalg_vector_norm()

Examples

1
2
3
4
5
if (torch_is_installed()) {
A <- torch_randn(3, 3)
out <- linalg_inv_ex(A)

}

torch documentation built on Oct. 7, 2021, 9:22 a.m.