A first-order interior-point method for linearly constrained smooth optimization

• 成果类型: Article
• 署名作者: Tseng, Paul; Bomze, Immanuel M.; Schachinger, Werner
• 署名单位: University of Vienna; University of Washington; University of Washington Seattle
• 刊物名称: MATHEMATICAL PROGRAMMING
• ISSN/ISSBN: 0025-5610
• DOI: 10.1007/s10107-009-0292-7
• 发表日期: 2011
• 页码: 399-424
• 关键词: affine-scaling algorithm; GLOBAL CONVERGENCE; programming problems; maximal cliques; convex; DYNAMICS

摘要

We propose a first-order interior-point method for linearly constrained smooth optimization that unifies and extends first-order affine-scaling method and replicator dynamics method for standard quadratic programming. Global convergence and, in the case of quadratic program, (sub)linear convergence rate and iterate convergence results are derived. Numerical experience on simplex constrained problems with 1000 variables is reported.