SVD
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Singular Value Decomposition: factorizes any m x n matrix A into U * Sigma * V^T, where U and V are orthogonal and Sigma is diagonal with non-negative singular values. The most versatile matrix decomposition. Used in dimensionality reduction, recommender systems, image compression, and pseudoinverse computation.