linalg_chol

C = [
1 0 0 0
1 2 0 0
1 2 3 0
1 2 3 4
];
A = C*C';
L=linalg_chol(A)
ulperror = (L(L<>0)-C(C<>0))./C(C<>0)/%eps

// The Cholesky decomposition can fail if the
// matrix is not positive definite.
A = matrix(1:16,4,4)
L=linalg_chol(A)

// Using the decomposition to solve Ax=b
// Source: https://ece.uwaterloo.ca/~dwharder/NumericalAnalysis/04LinearAlgebra/cholesky/
A = [
0.9 0.06 -0.39 -0.24;
0.06 1.604 0.134 0.464;
-0.39 0.134 2.685 0.802;
-0.24 0.464 0.802 1.977
];
L = linalg_chol( A )
b = [0.063, -0.6358, 0.5937, -0.1907]'
y = L \ b
x = L' \ y
// Compare with backslash
e = A\b
ulperror = (x-e)./e/%eps

// A Cholesky decomposition
// Source: https://ece.uwaterloo.ca/~dwharder/NumericalAnalysis/04LinearAlgebra/cholesky/
A = [
5 1.2 0.3 -0.6;
1.2 6 -0.4 0.9;
0.3 -0.4 8 1.7;
-0.6 0.9 1.7 10
];
L = linalg_chol( A )

// See the algorithm
edit linalg_chol