Analysis of smoothing-type algorithms for the convex second-order cone programming

被引：0

作者：

Dong L. ^{[1
]}

Wang H. ^{[2
]}

机构：

[1] College of Mathematics and Information Science, Xinyang Normal University, Xinyang

[2] School of Technology, Yantai Research Institute of China Agricultural University, Yantai

来源：

Journal of Applied Mathematics and Computing | 2015年 / 48卷 / 1-2期

基金：

中国国家自然科学基金;

关键词：

Convergence; Convex second-order cone programming; Euclidean Jordan algebra; Smoothing algorithm;

D O I：

10.1007/s12190-014-0797-y

中图分类号：

学科分类号：

摘要：

There recently has been much interest in smoothing-type algorithms for solving the linear second-order cone programming (LSOCP). We extend such method to solve the convex second-order cone programming (CSOCP), which is an extension of the LSOCP. In this paper, we first propose a new smoothing function. Based on this function, we establish a smoothing Newton algorithm for solving the CSOCP and prove that the algorithm is globally and locally quadratically convergent under suitable assumptions. For the established algorithm, we use a generalized Armijo-type search rule to generate the step size. Some numerical results are reported which indicate the effectiveness of our algorithm. © 2014, Korean Society for Computational and Applied Mathematics.

引用

页码：173 / 185

页数：12

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