jax.numpy.linalg.matrix_rank#
- jax.numpy.linalg.matrix_rank(M, tol=None)[source]#
Return matrix rank of array using SVD method
LAX-backend implementation of
numpy.linalg.matrix_rank()
.Original docstring below.
Rank of the array is the number of singular values of the array that are greater than tol.
Changed in version 1.14: Can now operate on stacks of matrices
- Parameters:
tol ((...) array_like, float, optional) –
Threshold below which SVD values are considered zero. If tol is None, and
S
is an array with singular values for M, andeps
is the epsilon value for datatype ofS
, then tol is set toS.max() * max(M, N) * eps
.Changed in version 1.14: Broadcasted against the stack of matrices
- Returns:
rank – Rank of A.
- Return type:
(…) array_like
References