During October (2017) I will write a program per day for some well-known numerical methods in both Python and Julia. It is intended to be an exercise then don't expect the code to be good enough for real use. Also, I should mention that I have almost no experience with Julia, so it probably won't be idiomatic Julia but more Python-like Julia.
Today we have Cholesky decomposition. It is a factorization of a Hermitian, positive-definite matrix into a lower and upper matrix, the main difference with LU decomposition is that it the lower matrix is the Hermitian transpose of the upper one.
Following are the codes
from __future__ import division, print_function import numpy as np def cholesky(mat): m, _ = mat.shape mat = mat.copy() for col in range(m): print(mat[col, col] - mat[col, 0:col].dot(mat[col, 0:col])) mat[col, col] = np.sqrt(mat[col, col] - mat[col, 0:col].dot(mat[col, 0:col])) for row in range(col + 1, m): mat[row, col] = (mat[row, col] - mat[row, 0:col].dot(mat[col, 0:col]))/mat[col, col] for row in range(1, m): mat[0:row, row] = 0 return mat A = np.array([ [4, -1, 1], [-1, 4.25, 2.75], [1, 2.75, 3.5]], dtype=float) B = cholesky(A)
function cholesky(mat) m, _ = size(mat) mat = copy(mat) for col = 1:m mat[col, col] = sqrt(mat[col, col] - dot(mat[col, 1:col-1], mat[col, 1:col-1])) for row = col + 1:m mat[row, col] = (mat[row, col] - dot(mat[row, 1:col-1], mat[col, 1:col-1]))/mat[col, col] end end for row = 2:m mat[1:row-1, row] = 0 end return mat end A = [4 -1 1; -1 4.25 2.75; 1 2.75 3.5] B = cholesky(A)
As an example we have the matrix
And, the answer of both codes is
Regarding number of lines we have: 23 in Python and 22 in Julia. The comparison
in execution time is done with
%timeit magic command in IPython and
@benchmark in Julia.
In this case, we can say that the Python code is roughly 10 times slower than Julia code.