gaussian_elim.py

import numpy as np

def GEPP(A, b, doPricing = True):
    '''
    Gaussian elimination with partial pivoting.
    
    input: A is an n x n numpy matrix
           b is an n x 1 numpy array
    output: x is the solution of Ax=b 
            with the entries permuted in 
            accordance with the pivoting 
            done by the algorithm
    post-condition: A and b have been modified.
    '''
    n = len(A)
    if b.size != n:
        raise ValueError("Invalid argument: incompatible sizes between"+
                         "A & b.", b.size, n)
    # k represents the current pivot row. Since GE traverses the matrix in the 
    # upper right triangle, we also use k for indicating the k-th diagonal 
    # column index.
    
    # Elimination
    for k in range(n-1):
        if doPricing:
            # Pivot
            maxindex = abs(A[k:,k]).argmax() + k
            if A[maxindex, k] == 0:
                raise ValueError("Matrix is singular.")
            # Swap
            if maxindex != k:
                A[[k,maxindex]] = A[[maxindex, k]]
                b[[k,maxindex]] = b[[maxindex, k]]
        else:
            if A[k, k] == 0:
                raise ValueError("Pivot element is zero. Try setting doPricing to True.")
        #Eliminate
        for row in range(k+1, n):
            multiplier = A[row,k]/A[k,k]
            A[row, k:] = A[row, k:] - multiplier*A[k, k:]
            b[row] = b[row] - multiplier*b[k]
    # Back Substitution
    x = np.zeros(n)
    for k in range(n-1, -1, -1):
        x[k] = (b[k] - np.dot(A[k,k+1:],x[k+1:]))/A[k,k]
    return x

if __name__ == "__main__":
    A = np.array([[1.,-1.,1.,-1.],[1.,0.,0.,0.],[1.,1.,1.,1.],[1.,2.,4.,8.]])
    b =  np.array([[14.],[4.],[2.],[2.]])
    print GEPP(np.copy(A), np.copy(b), doPricing = False)
    print GEPP(A,b)

Awesome job! I have forked your code and modularized it. I needed to import it and apply it to an existing project. https://gist.github.com/jgcastro89/49090cc69a499a129413597433b9baab