RustyQLib 0.0.3

//! Matrix decompositions, one per file — general-purpose kernels for
//! future use across the library (regression, calibration, PCA, factor
//! models), independent of any finance semantics.
//!
//! - [`cholesky`]: `A = L L^T` for symmetric PSD matrices, plus the SPD
//!   linear solve through the factor — the fast path for normal
//!   equations and covariance sampling;
//! - [`qr`]: Householder QR (`A = Q R`, thin form), plus numerically
//!   stable linear least squares — the right tool for regression
//!   (e.g. Longstaff-Schwartz bases) without forming `A^T A`;
//! - [`svd`]: one-sided Jacobi singular value decomposition
//!   (`A = U S V^T`), plus the minimum-norm pseudo-inverse solve for
//!   rank-deficient problems;
//! - [`eigen`]: cyclic Jacobi eigendecomposition of symmetric matrices —
//!   the engine behind the PSD projection in
//!   [`nearest_correlation`](super::nearest_correlation).
//!
//! All matrices are `Vec<Vec<f64>>` row-major, matching the rest of the
//! crate; the implementations favor clarity and robustness on the
//! small-to-moderate sizes quant workflows use.

pub mod cholesky;
pub mod eigen;
pub mod qr;
pub mod svd;

pub use cholesky::{cholesky_factor, cholesky_solve};
pub use eigen::symmetric_eigen;
pub use qr::{least_squares, qr};
pub use svd::{pseudo_solve, svd};