jax.numpy.linalg.matrix_rank
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
- 1
MATLAB reference documentation, “Rank” https://www.mathworks.com/help/techdoc/ref/rank.html
- 2
W. H. Press, S. A. Teukolsky, W. T. Vetterling and B. P. Flannery, “Numerical Recipes (3rd edition)”, Cambridge University Press, 2007, page 795.