Impact of Singular Value Decomposition: A review study
Year: 2025
Type: article
Abstract: 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.
Authors: Thomas, Atul, Chandra, Mithila
Institution: Osmania University
Language: English
Topic: Matrix Theory and Algorithms
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7108683705
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