A full-Newton step interior-point algorithm for linear optimization based on a finite barrier

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• 作者：Weiwei Wang^a ; ^b ; ^{weiwei.wang2007@163.com" class="auth_mail" title="E-mail the corresponding author} ; Hongmei Bi^c ; ^{bihongmei2007@163.com" class="auth_mail" title="E-mail the corresponding author} ; Hongwei Liu^a ; ^{hwliu@mail.xidian.edu.cn" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Linear optimization ; Interior-point methods ; Kernel function ; Full-Newton step
• 刊名：Operations Research Letters
• 出版年：2016
• 出版时间：November 2016
• 年：2016
• 卷：44
• 期：6
• 页码：750-753
• 全文大小：378 K

文摘

In this paper, we propose a finite barrier kernel function for primal–dual interior-point algorithm in linear optimization with a full-Newton step. To our best knowledge, it is the first time that the property of exponential convexity is used for full-Newton step interior-point methods(IPMs). Moreover, the analysis is simplified and the complexity of the algorithm coincides with the currently best iteration bound for linear optimization problems.