首页 | 本学科首页   官方微博 | 高级检索  
     检索      


Primal-dual Interior-point Algorithms for Second-order Cone Optimization Based on a New Parametric Kernel Function
Authors:Yan Qin Bai  Guo Qiang Wang
Institution:(1) Department of Mathematics, College of Sciences, Shanghai University, Shanghai, 200444, P. R. China;(2) College of Vocational Technology, Shanghai University of Engineering Science, Shanghai, 200437, P. R. China
Abstract:A class of polynomial primal-dual interior-point algorithms for second-order cone optimization based on a new parametric kernel function, with parameters p and q, is presented. Its growth term is between linear and quadratic. Some new tools for the analysis of the algorithms are proposed. The complexity bounds of $$
O{\left( {{\sqrt N }\log N\log \frac{N}
{\varepsilon }} \right)}
$$ for large-update methods and $$
O{\left( {{\sqrt N }\log \frac{N}
{\varepsilon }} \right)}
$$ for small-update methods match the best known complexity bounds obtained for these methods. Numerical tests demonstrate the behavior of the algorithms for different results of the parameters p and q. Project sponsored by Shanghai Pujiang Program (Grant No. 06PJ14039) and Shanghai Educational Committee Foundation (Grant No. 06NS031)
Keywords:second-order cone optimization  linear optimization  interior-point methods  large- and small-update methods  polynomial-time complexity
本文献已被 维普 SpringerLink 等数据库收录!
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号