linalg.cond
Description
Compute the condition number of a matrix.
This function is capable of returning the condition number using one of seven different norms, depending on the value of p For details about the norm values, see Table 1.
Mandatory Input Parameters
Parameter |
Type |
Description |
|---|---|---|
x |
(…,M,N) array_like |
A matrix whose condition number is to be sought. |
Optional Input Parameters
Parameter |
Type |
Default Value |
Description |
|---|---|---|---|
p |
{None, 1, -1, 2, -2, inf, -inf, 'fro'} |
None |
Calculates the condition number with norms for matrices. |
Return Value
Type |
Description |
|---|---|
ndarray |
Condition number of the matrix. |
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 | >>> import numpy as np >>> a = np.array([[1,0,-1], [0,1,0], [1,0,1]]) >>> a array([[ 1, 0, -1], [ 0, 1, 0], [ 1, 0, 1]]) >>> np.linalg.cond(a) 1.4142135623730951 >>> np.linalg.cond(a, 'fro') 3.1622776601683795 >>> np.linalg.cond(a, np.inf) 2.0 >>> np.linalg.cond(a, -np.inf) 1.0 >>> np.linalg.cond(a, 1) 2.0 >>> np.linalg.cond(a, -1) 1.0 >>> np.linalg.cond(a, 2) 1.4142135623730951 >>> np.linalg.cond(a, -2) 0.7071067811865475 >>> min(np.linalg.svd(a, compute_uv=False))*min(np.linalg.svd(np.linalg.inv(a), compute_uv=False)) 0.7071067811865475 >>> |
Parent topic: Linear Algebra Functions