A bundle-filter method for nonsmooth convex constrained optimization

• 成果类型: Article; Proceedings Paper
• 署名作者: Karas, Elizabeth; Ribeiro, Ademir; Sagastizabal, Claudia; Solodov, Mikhail
• 署名单位: Universidade Federal do Parana
• 刊物名称: MATHEMATICAL PROGRAMMING
• ISSN/ISSBN: 0025-5610
• DOI: 10.1007/s10107-007-0123-7
• 发表日期: 2009
• 页码: 297-320
• 关键词: penalty-function; algorithm

摘要

For solving nonsmooth convex constrained optimization problems, we propose an algorithm which combines the ideas of the proximal bundle methods with the filter strategy for evaluating candidate points. The resulting algorithm inherits some attractive features from both approaches. On the one hand, it allows effective control of the size of quadratic programming subproblems via the compression and aggregation techniques of proximal bundle methods. On the other hand, the filter criterion for accepting a candidate point as the new iterate is sometimes easier to satisfy than the usual descent condition in bundle methods. Some encouraging preliminary computational results are also reported.