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.

引用

页码：739 / 756

页数：18

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