# Numerical methods challenge: Day 29

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.

## Cholesky decomposition

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

### Python

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)

### Julia

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

### Comparison Python/Julia

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.

For Python:

with result

For Julia:

with result

BenchmarkTools.Trial: memory estimate: 4.01 MiB allocs estimate: 20303 -------------- minimum time: 1.010 ms (0.00% GC) median time: 1.136 ms (0.00% GC) mean time: 1.370 ms (17.85% GC) maximum time: 4.652 ms (40.37% GC) -------------- samples: 3637 evals/sample: 1

In this case, we can say that the Python code is roughly 10 times slower than Julia code.

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