A New Second-Order Mehrotra-Type Predictor-Corrector Algorithm for SDO
A New Second-Order Mehrotra-Type Predictor-Corrector Algorithm for SDO作者机构:College of ScienceChina Three Gorges University College of Economics and ManagementChina Three Gorges University
出 版 物:《Wuhan University Journal of Natural Sciences》 (武汉大学学报(自然科学英文版))
年 卷 期:2016年第21卷第2期
页 面:99-109页
核心收录:
学科分类:02[经济学] 0202[经济学-应用经济学] 020208[经济学-统计学] 07[理学] 0714[理学-统计学(可授理学、经济学学位)] 070103[理学-概率论与数理统计] 0701[理学-数学]
基 金:Supported by the National Natural Science Foundation of China(71471102)
主 题:Mehrotra-type algorithm predictor-corrector methods semidefinite optimization
摘 要:In Zhang’s recent works,a second-order Mehrotra-type predictor-corrector algorithm for linear optimization was extended to semidefinite optimization and derived that the algorithm for semidefinite optimization had3/2 0 T 0O(nlog(X)gS/e)iteration complexity based on the NT direction as Newton search *** this paper,we extend the second-order Mehrotra-type predictor-corrector algorithm for linear optimization to semidefinite optimization and discuss the polynomial convergence of the algorithm by modifying the corrector direction and new *** is proved that the iteration complexity is reduced to0 0O(nlog XgS/e),which coincides with the currently best iteration bound of Mehrotra-type predictor-corrector algorithm for semidefinite optimization.