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import numpy as npdef gaussElim(A, b):

"""

This function solves system of linear equations Ax = b using Gauss elimination with

partial pivoting. Input parameters are:

A - square matrix

b - column vector

We will assume that A is singular matrix so that sysyem has unique solution. Output is:

solution - solution to the system, row vector

"""

# number of equations

A_rows = len(A)

# append the column b to the right side of the matrix A so we obtain n x n+1 matrix A_tilde

A_tilde = np.c_[A, b]

"""

Find the index of the entry in each colum with the largest absolute value. This entry is called the pivot.

We assumed that pivot it is not equal to 0, othervise the matrix A is singular.

Bring the row with pivot to the diagonal i.e interchange rows k and P_k

"""

for k in range(A_rows):

for P_k in range(k, A_rows):

if abs(A_tilde[P_k, k]) > abs(A_tilde[k, k]):

A_tilde[[k, P_k]] = A_tilde[[P_k, k]]

"""

Iterate over rows below the pivot and calculate the factor temp_ in order to make the k-th entry zero.

Further, subtract the temp-th multiple of corresponding element in k-th row for every...

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