SVD

Singular Value Decomposition (SVD) is a fundamental technique in linear algebra used to factorize a matrix into three simpler matrices:

[ A = U \Sigma V^T ]

Where:

• U is an orthogonal matrix containing the left singular vectors.

• Σ is a diagonal matrix with singular values (square roots of eigenvalues).

• V^T is the transpose of an orthogonal matrix containing the right singular vectors.