A primal-dual interior point algorithm for convex quadratic programming based on a new parametric kernel function

被引：8
|
作者：
Boudjellal, N. [1 ]
Roumili, H. [1 ]
Benterki, D. J. [1 ]
机构：
[1] Univ Ferhat Abbas Setif 1, Fac Sci, Lab Fundamental & Numer Math, Dept Math, Setif, Algeria
来源：
OPTIMIZATION | 2021年 / 70卷 / 08期
关键词：
Convex quadratic programming; interior point methods; kernel function; COMPLEXITY ANALYSIS;
D O I：
10.1080/02331934.2020.1751156
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：
In this paper, we deal with a polynomial primal-dual interior-point algorithm for solving convex quadratic programming based on a new parametric kernel function with an exponential barrier term. The proposed kernel function is not logarithmic and not self-regular. We analyze a class of large and small-update versions which are based on our new kernel function. The complexity obtained generalizes the result given by Bai et al. This result is the first to reach this goal. Finally, some numerical results are provided to show the efficiency of the proposed algorithm and to compare it with an available method.
引用
收藏
页码：1703 / 1724
页数：22
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