numpy scipy gaussian elimination using LU decomposition with pivoting

Gaussian_elimination.py

#! /usr/bin/env python

"""
Solve linear system using LU decomposition and Gaussian elimination
"""

import numpy as np
from scipy.linalg import lu, inv

def gausselim(A,B):
    """
    Solve Ax = B using Gaussian elimination and LU decomposition.

    A = LU   decompose A into lower and upper triangular matrices
    LUx = B  substitute into original equation for A

    Let y = Ux and solve:
    Ly = B --> y = (L^-1)B  solve for y using "forward" substitution
    Ux = y --> x = (U^-1)y  solve for x using "backward" substitution

    :param A: coefficients in Ax = B
    :type A: numpy.ndarray of size (m, n)
    :param B: dependent variable in Ax = B
    :type B: numpy.ndarray of size (m, 1)
    """
    # LU decomposition with pivot
    pl, u = lu(A, permute_l=True)
    # forward substitution to solve for Ly = B
    y = np.zeros(B.size)
    for m, b in enumerate(B.flatten()):
        y[m] = b
        # skip for loop if m == 0
        if m:
            for n in xrange(m):
                y[m] -= y[n] * pl[m,n]
        y[m] /= pl[m, m]

    # backward substitution to solve for y = Ux
    x = np.zeros(B.size)
    lastidx = B.size - 1  # last index
    for midx in xrange(B.size):
        m = B.size - 1 - midx  # backwards index
        x[m] = y[m]
        if midx:
            for nidx in xrange(midx):
                n = B.size - 1  - nidx
                x[m] -= x[n] * u[m,n]
        x[m] /= u[m, m]
    return x

if __name__ == '__main__':
    x = gausselim(np.array([[3, 2], [1, -4]]), np.array([[5], [10]]))
    print x

Hi @sbykl , I think what you're looking for is either numpy.loadtxt or pandas.read_csv. Read the docs, hopefully you'll find usage straightforward. 😃