Impact of Singular Value Decomposition: A review study

Singular Value Decomposition (SVD) is a fundamental matrix factorization technique that provides deep insight into the structure of linear systems. It decomposes a given matrix into orthogonal and diagonal components, enabling the identification of key features such as rank, range, and noise characteristics. This paper discusses both the computational methods for obtaining the SVD and its wide-ranging applications across science and engineering.