If m is the product of the first B\ .ndim dimensions of A and n is the product of the rest of the dimensions, this function expects m and n to be equal. The returned tensor x satisfies tensordot(A, x, dims=x$ndim) == B. ## Usage linalg_tensorsolve(A, B, dims = NULL) ## Arguments A (Tensor): tensor to solve for. B (Tensor): the solution dims (Tupleint, optional): dimensions of A to be moved. If NULL, no dimensions are moved. Default: NULL. ## Details If dims is specified, A will be reshaped as A = movedim(A, dims, seq(len(dims) - A$ndim + 1, 0))

Supports inputs of float, double, cfloat and cdouble dtypes.

• linalg_tensorinv() computes the multiplicative inverse of torch_tensordot().

Other linalg: linalg_cholesky_ex(), linalg_cholesky(), linalg_det(), linalg_eigh(), linalg_eigvalsh(), linalg_eigvals(), linalg_eig(), linalg_householder_product(), linalg_inv_ex(), 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_triangular(), linalg_solve(), linalg_svdvals(), linalg_svd(), linalg_tensorinv(), linalg_vector_norm()

## Examples

if (torch_is_installed()) {
A <- torch_eye(2 * 3 * 4)$reshape(c(2 * 3, 4, 2, 3, 4)) B <- torch_randn(2 * 3, 4) X <- linalg_tensorsolve(A, B) X$shape
torch_allclose(torch_tensordot(A, X, dims = X$ndim), B) A <- torch_randn(6, 4, 4, 3, 2) B <- torch_randn(4, 3, 2) X <- linalg_tensorsolve(A, B, dims = c(1, 3)) A <- A$permute(c(2, 4, 5, 1, 3))
torch_allclose(torch_tensordot(A, X, dims = X\$ndim), B, atol = 1e-6)
}
#> [1] TRUE