Singular Value Decomposition Program
Application Background Singular Value Decomposition (SVD) is an important matrix factorization method in linear algebra, extending the unitary diagonalization of normal matrices in matrix analysis. It has significant applications in signal processing, statistics, and other fields. SVD shares some similarities with eigenvector-based diagonalization of symmetric or Hermitian matrices, but despite their correlation, these two matrix decompositions have distinct differences. Key Technology A non-negative real number σ is a singular value of matrix M if there exist unit vectors u in Km and v in Kn such that: M = uσv^T where vectors u and v are respectively