The paper provides both theoretical foundations (exactness properties) and a practical algorithm (A-NAUM) for symmetric matrix factorization problems, with proven convergence rates—useful for practitioners implementing matrix factorization in applications.
This paper develops a fast algorithm for symmetric matrix factorization, a mathematical technique used across machine learning and image processing. The authors prove theoretical guarantees about when their method finds exact solutions and propose A-NAUM, an efficient algorithm that alternates between updating matrix factors, with convergence guarantees.