linalg_matrix_rank {torch} | R Documentation |
The matrix rank is computed as the number of singular values
(or eigenvalues in absolute value when hermitian = TRUE
)
that are greater than the specified tol
threshold.
linalg_matrix_rank(
A,
...,
atol = NULL,
rtol = NULL,
tol = NULL,
hermitian = FALSE
)
A |
(Tensor): tensor of shape |
... |
Not currently used. |
atol |
the absolute tolerance value. When |
rtol |
the relative tolerance value. See above for the value it takes when |
tol |
(float, Tensor, optional): the tolerance value. See above for
the value it takes when |
hermitian |
(bool, optional): indicates whether |
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 hermitian = TRUE
, A
is assumed to be Hermitian if complex or
symmetric if real, but this is not checked internally. Instead, just the lower
triangular part of the matrix is used in the computations.
If tol
is not specified and A
is a matrix of dimensions (m, n)
,
the tolerance is set to be
Math could not be displayed. Please visit the package website.
where \sigma_1
is the largest singular value
(or eigenvalue in absolute value when hermitian = TRUE
), and
\varepsilon
is the epsilon value for the dtype of A
(see torch_finfo()
).
If A
is a batch of matrices, tol
is computed this way for every element of
the batch.
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_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()
if (torch_is_installed()) {
a <- torch_eye(10)
linalg_matrix_rank(a)
}