A polynomial interior-point algorithm with improved iteration bounds for linear optimization

被引：0
|
作者：
Liu, Liying [1 ]
Hua, Tao [2 ]
机构：
[1] Liaocheng Univ, Sch Math Sci, Liaocheng 252059, Peoples R China
[2] Liaocheng Univ, Lab Management Ctr, Liaocheng 252059, Peoples R China
来源：
JAPAN JOURNAL OF INDUSTRIAL AND APPLIED MATHEMATICS | 2024年 / 41卷 / 02期
关键词：
Linear programming; Interior-point methods; Kernel function; Polynomial complexity; KERNEL FUNCTIONS; LARGE-UPDATE;
D O I：
10.1007/s13160-023-00630-6
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
In this paper, we present a polynomial primal-dual interior-point algorithm for linear optimization based on a modified logarithmic barrier kernel function. Iteration bounds for the large-update interior-point method and the small-update interior-point method are derived. It is shown that the large-update interior-point method has the same polynomial complexity as the small-update interior-point method, which is the best known iteration bounds. Our result closes a long-existing gap in the theoretical complexity bounds for large-update interior-point method and small-update interior-point method.
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页码：739 / 756
页数：18
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