Anderson Accelerated Operator Splitting Methods for Convex-nonconvex Regularized Problems
Fuente:
arXiv
Salvato in:
| Autori principali: | , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866916622498791424 |
|---|---|
| author | Heng, Qiang Liu, Xiaoqian Chi, Eric C. |
| author_facet | Heng, Qiang Liu, Xiaoqian Chi, Eric C. |
| contents | Convex-nonconvex (CNC) regularization is a novel paradigm that employs a nonconvex penalty function while maintaining the convexity of the entire objective function. It has been successfully applied to problems in signal processing, statistics, and machine learning. Despite its wide application, the computation of CNC regularized problems remains challenging and under-investigated. To fill the gap, we study several operator splitting methods and their Anderson accelerated counterparts for solving least squares problems with CNC regularization. We establish the global convergence of the proposed algorithm to an optimal point and demonstrate its practical speed-ups in various applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_14269 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Anderson Accelerated Operator Splitting Methods for Convex-nonconvex Regularized Problems Heng, Qiang Liu, Xiaoqian Chi, Eric C. Optimization and Control Computation Convex-nonconvex (CNC) regularization is a novel paradigm that employs a nonconvex penalty function while maintaining the convexity of the entire objective function. It has been successfully applied to problems in signal processing, statistics, and machine learning. Despite its wide application, the computation of CNC regularized problems remains challenging and under-investigated. To fill the gap, we study several operator splitting methods and their Anderson accelerated counterparts for solving least squares problems with CNC regularization. We establish the global convergence of the proposed algorithm to an optimal point and demonstrate its practical speed-ups in various applications. |
| title | Anderson Accelerated Operator Splitting Methods for Convex-nonconvex Regularized Problems |
| topic | Optimization and Control Computation |
| url | https://arxiv.org/abs/2502.14269 |