Fast and Effective Computation of Generalized Symmetric Matrix Factorization
In this paper, we study a nonconvex, nonsmooth, and non-Lipschitz generalized symmetric matrix factorization model that unifies a broad class of matrix factorization formulations arising in machine learning, image science, engineering, and related areas. We first establish two exactness properties. On the modeling side, we prove an exact penalty property showing that, under suitable conditions, the symmetry-inducing quadratic penalty enforces symmetry whenever the penalty parameter is sufficiently large but finite, thereby exactly recovering the associated symmetric formulation. On the algorithmic side, we introduce an auxiliary-variable splitting formulation and establish an exact relaxa...
| Authors | Lei Yang, Han Wan, Min Zhang et al. |
|---|---|
| Primary category | math.OC |
| Published | 19 March 2026 |
| Last updated at source | 19 March 2026 |
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