bundles / numpy latest / numpy / linalg / cond
_ArrayFunctionDispatcher
numpy.linalg:cond
source: build-install/usr/lib/python3.14/site-packages/numpy/linalg/_linalg.py :1914
Signature
def cond ( x , p = None ) Summary
Compute the condition number of a matrix.
Extended Summary
This function is capable of returning the condition number using one of seven different norms, depending on the value of p (see Parameters below).
Parameters
x: (..., M, N) array_likeThe matrix whose condition number is sought.
p: {None, 1, -1, 2, -2, inf, -inf, 'fro'}, optionalOrder of the norm used in the condition number computation:
===== ============================ p norm for matrices ===== ============================ None 2-norm, computed directly using the ``SVD`` 'fro' Frobenius norm inf max(sum(abs(x), axis=1)) -inf min(sum(abs(x), axis=1)) 1 max(sum(abs(x), axis=0)) -1 min(sum(abs(x), axis=0)) 2 2-norm (largest sing. value) -2 smallest singular value ===== ============================
inf means the numpy.inf object, and the Frobenius norm is the root-of-sum-of-squares norm.
Returns
c: {float, inf}The condition number of the matrix. May be infinite.
Notes
The condition number of x is defined as the norm of x times the norm of the inverse of x [1]; the norm can be the usual L2-norm (root-of-sum-of-squares) or one of a number of other matrix norms.
Examples
import numpy as np from numpy import linalg as LA a = np.array([[1, 0, -1], [0, 1, 0], [1, 0, 1]]) a✓
LA.cond(a) LA.cond(a, 'fro') LA.cond(a, np.inf) LA.cond(a, -np.inf) LA.cond(a, 1) LA.cond(a, -1) LA.cond(a, 2) LA.cond(a, -2) (min(LA.svd(a, compute_uv=False)) * min(LA.svd(LA.inv(a), compute_uv=False)))✗
See also
Aliases
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numpy.linalg.cond