Primal-dual nonlinear rescaling method with dynamic scaling parameter update

• 成果类型: Article
• 署名作者: Griva, I; Polyak, RA
• 署名单位: George Mason University; George Mason University; George Mason University
• 刊物名称: MATHEMATICAL PROGRAMMING
• ISSN/ISSBN: 0025-5610
• DOI: 10.1007/s10107-005-0603-6
• 发表日期: 2006
• 页码: 237-259
• 关键词: barrier methods; Penalty; convex; CONVERGENCE; algorithms

摘要

In this paper we developed a general primal-dual nonlinear rescaling method with dynamic scaling parameter update (PDNRD) for convex optimization. We proved the global convergence, established 1.5-Q-superlinear rate of convergence under the standard second order optimality conditions. The PDNRD was numerically implemented and tested on a number of nonlinear problems from COPS and CUTE sets. We present numerical results, which strongly corroborate the theory.