A Predictor-corrector algorithm with multiple corrections for convex quadratic programming

被引：1
作者：
Liu, Zhongyi [1 ]
Chen, Yue [2 ]
Sun, Wenyu [3 ]
Wei, Zhihui [4 ]
机构：
[1] Hohai Univ, Coll Sci, Nanjing 210098, Jiangsu, Peoples R China
[2] Nanjing Univ Aeronaut & Astronaut, Jincheng Coll, Nanjing 211156, Jiangsu, Peoples R China
[3] Nanjing Normal Univ, Sch Math Sci, Nanjing 210097, Jiangsu, Peoples R China
[4] Nanjing Univ Sci & Technol, Sch Comp Sci & Technol, Nanjing 210094, Jiangsu, Peoples R China
来源：
COMPUTATIONAL OPTIMIZATION AND APPLICATIONS | 2012年 / 52卷 / 02期
基金：
中国国家自然科学基金;
关键词：
Convex quadratic programming; Primal-dual interior-point method; Predictor-corrector; Polynomial complexity; INTERIOR-POINT ALGORITHM; LINEAR OPTIMIZATION; SOLVER; STEP;
D O I：
10.1007/s10589-011-9421-3
中图分类号：
C93 [管理学]; O22 [运筹学];
学科分类号：
070105 ; 12 ; 1201 ; 1202 ; 120202 ;
摘要：
Recently an infeasible interior-point algorithm for linear programming (LP) was presented by Liu and Sun. By using similar predictor steps, we give a (feasible) predictor-corrector algorithm for convex quadratic programming (QP). We introduce a (scaled) proximity measure and a dynamical forcing factor (centering parameter). The latter is used to force the duality gap to decrease. The algorithm can decrease the duality gap monotonically. Polynomial complexity can be proved and the result coincides with the best one for LP, namely, .
引用
收藏
页码：373 / 391
页数：19
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