Module svd 

Singular Value Decomposition (SVD)

SVD factorizes a matrix A into the product U * Σ * V^T, where:

• U is an orthogonal matrix (left singular vectors)
• Σ is a diagonal matrix (singular values)
• V^T is an orthogonal matrix (right singular vectors)

SVD is used for:

• Matrix approximation and compression
• Principal Component Analysis (PCA)
• Solving linear least squares problems
• Pseudoinverse computation

Examples

use advanced_algorithms::numerical::svd::SVD;

let matrix = vec![
    vec![1.0, 2.0],
    vec![3.0, 4.0],
    vec![5.0, 6.0],
];

let svd = SVD::decompose(&matrix, 1e-10, 1000).unwrap();
let singular_values = svd.singular_values();

Structs

SVD

SVD decomposition result