Module svd 

Singular value decomposition by one-sided Jacobi rotations — simple, accurate for the small-to-moderate matrices of quant workflows, and rank-revealing. A = U diag(S) V^T with orthonormal U (m x n, columns for nonzero singular values), non-negative S sorted descending, and orthogonal V (n x n).

Functions

pseudo_solve

Minimum-norm least-squares solve A x ~ b through the SVD pseudo-inverse, dropping singular values below tol * s_max — the robust choice for rank-deficient or ill-conditioned systems.

svd

SVD of an m x n matrix (any shape; internally transposes when m < n). Returns (u, s, v) with A = U diag(S) V^T.