In this article, we will use the cond() function of the NumPy package to calculate the condition number of a given matrix. cond() is a function of linear algebra module in NumPy package.
Syntax:
numpy.linalg.cond(x, p=None)
Example 1: Condition Number of 2X2 matrix
Python3
# Importing library import numpy as np # Creating a 2X2 matrix matrix = np.array([[ 4 , 2 ], [ 3 , 1 ]]) print ( "Original matrix:" ) print (matrix) # Output result = np.linalg.cond(matrix) print ( "Condition number of the matrix:" ) print (result) |
Output:
Original matrix: [[4 2] [3 1]] Condition number of the matrix: 14.933034373659256
Example 2: Condition Number of 3X3 matrix
Python3
# Importing library import numpy as np # Creating a 3X3 matrix matrix = np.array([[ 4 , 2 , 0 ], [ 3 , 1 , 2 ], [ 1 , 6 , 4 ]]) print ( "Original matrix:" ) print (matrix) # Output result = np.linalg.cond(matrix) print ( "Condition number of the matrix:" ) print (result) |
Output:
Original matrix: [[4 2 0] [3 1 2] [1 6 4]] Condition number of the matrix: 5.347703616656448
Example 3: Condition Number of 4X4 matrix
Python3
# Importing library import numpy as np # Creating a 4X4 matrix matrix = np.array([[ 4 , 1 , 4 , 2 ], [ 3 , 1 , 2 , 0 ], [ 3 , 5 , 7 , 1 ], [ 0 , 6 , 8 , 4 ]]) print ( "Original matrix:" ) print (matrix) # Output result = np.linalg.cond(matrix) print ( "Condition number of the matrix:" ) print (result) |
Output:
Original matrix: [[4 1 4 2] [3 1 2 0] [3 5 7 1] [0 6 8 4]] Condition number of the matrix: 57.34043866386226
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