In linear algebra, the singular value decomposition (SVD) is a factorization of a real or complex matrix into a rotation, followed by a scaling, followed by another rotation. It generalizes the eigendecomposition of a square normal matrix with an orthonormal eigenbasis to any ⁠⁠ matrix. It is related to the polar decomposition, and is a common means for implementing low-rank approximation for matrices.