Penalty and Smoothing Methods for Convex Semi-Infinite Programming

• 成果类型: Article
• 署名作者: Auslender, Alfred; Goberna, Miguel A.; Lopez, Marco A.
• 署名单位: Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute for Mathematical Sciences (INSMI); Ecole Centrale de Lyon; Institut National des Sciences Appliquees de Lyon - INSA Lyon; Universite Claude Bernard Lyon 1; Universite Jean Monnet; Institut Polytechnique de Paris; Ecole Polytechnique; Universitat d'Alacant
• 刊物名称: MATHEMATICS OF OPERATIONS RESEARCH
• ISSN/ISSBN: 0364-765X
• DOI: 10.1287/moor.1080.0362
• 发表日期: 2009
• 页码: 303-319
• 关键词: min-max problems; Entropic Regularization; Finite; algorithms

摘要

In this paper we consider min-max convex semi-infinite programming. To solve these problems we introduce a unified framework concerning Remez-type algorithms and integral methods coupled with penalty and smoothing methods. This framework subsumes well-known classical algorithms, but also provides some new methods with interesting properties. Convergence of the primal and dual sequences are proved under minimal assumptions.