| linalg_eigvals {torch} | R Documentation |
Computes the eigenvalues of a square matrix.
Description
Letting \mathbb{K} be \mathbb{R} or \mathbb{C},
the eigenvalues of a square matrix A \in \mathbb{K}^{n \times n} are defined
as the roots (counted with multiplicity) of the polynomial p of degree n given by
Usage
linalg_eigvals(A)
Arguments
A |
(Tensor): tensor of shape |
Details
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where \mathrm{I}_n is the n-dimensional identity matrix.
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.
Note
The eigenvalues of a real matrix may be complex, as the roots of a real polynomial may be complex. The eigenvalues of a matrix are always well-defined, even when the matrix is not diagonalizable.
See Also
linalg_eig() computes the full eigenvalue decomposition.
Other linalg:
linalg_cholesky_ex(),
linalg_cholesky(),
linalg_det(),
linalg_eigh(),
linalg_eigvalsh(),
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_tensorsolve(),
linalg_vector_norm()
Examples
if (torch_is_installed()) {
a <- torch_randn(2, 2)
w <- linalg_eigvals(a)
}