How to implement cholesky decomposition using numpy efficiently?

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I want to implement efficient realization of cholesky decomposition. Naive code looks like

import numpy as np
def cholesky(A):
    n = A.shape[0]
    L = np.zeros_like(A)
    for i in range(n):
        for j in range(i+1):
            s = 0
            for k in range(j):
                s += L[i][k] * L[j][k]

            if (i == j):
                L[i][j] = (A[i][i] - s) ** 0.5
            else:
                L[i][j] = (1.0 / L[j][j] * (A[i][j] - s))
    return L

I wonder if there is a way to make it more efficient. E.g vectorize it?

numpy python
1 Answers

This is not vectorized, but for matrix of size 100 it is ~1000x faster:

import numba as nb

@nb.njit('float64[:, :](float64[:, :])')
def cholesky_numba(A):
    n = A.shape[0]
    L = np.zeros_like(A)
    for i in range(n):
        for j in range(i+1):
            s = 0
            for k in range(j):
                s += L[i][k] * L[j][k]

            if (i == j):
                L[i][j] = (A[i][i] - s) ** 0.5
            else:
                L[i][j] = (1.0 / L[j][j] * (A[i][j] - s))
    return L
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