一种新的基于参数核函数的凸二次对称锥优化的原对偶内点算法

2012 Fifth International Joint Conference on Computational Sciences and Optimization Pub Date : 2012-06-23 DOI:10.1109/CSO.2012.52

Guoqiang Wang, Fayan Wang

{"title":"一种新的基于参数核函数的凸二次对称锥优化的原对偶内点算法","authors":"Guoqiang Wang, Fayan Wang","doi":"10.1109/CSO.2012.52","DOIUrl":null,"url":null,"abstract":"In this paper, we present a class of primal-dual interior-point algorithms for convex quadratic symmetric cone optimization based on a parametric kernel function, which has been introduced for linear optimization. By using Euclidean Jordan algebras, we derive the iteration bounds that match the currently best known iteration bounds for large- and small-update methods, which are as good as the linear optimization analogue.","PeriodicalId":170543,"journal":{"name":"2012 Fifth International Joint Conference on Computational Sciences and Optimization","volume":"30 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A New Primal-Dual Interior-Point Algorithm for Convex Quadratic Symmetric Cone Optimization Based on a Parametric Kernel Function\",\"authors\":\"Guoqiang Wang, Fayan Wang\",\"doi\":\"10.1109/CSO.2012.52\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we present a class of primal-dual interior-point algorithms for convex quadratic symmetric cone optimization based on a parametric kernel function, which has been introduced for linear optimization. By using Euclidean Jordan algebras, we derive the iteration bounds that match the currently best known iteration bounds for large- and small-update methods, which are as good as the linear optimization analogue.\",\"PeriodicalId\":170543,\"journal\":{\"name\":\"2012 Fifth International Joint Conference on Computational Sciences and Optimization\",\"volume\":\"30 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-06-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 Fifth International Joint Conference on Computational Sciences and Optimization\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CSO.2012.52\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 Fifth International Joint Conference on Computational Sciences and Optimization","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CSO.2012.52","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 1

摘要

本文提出了一类基于参数核函数的凸二次对称锥优化的原对偶内点算法。通过使用欧几里得约当代数，我们推导出与当前已知的大型和小型更新方法的迭代边界相匹配的迭代边界，其效果与线性优化模拟一样好。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

A New Primal-Dual Interior-Point Algorithm for Convex Quadratic Symmetric Cone Optimization Based on a Parametric Kernel Function

In this paper, we present a class of primal-dual interior-point algorithms for convex quadratic symmetric cone optimization based on a parametric kernel function, which has been introduced for linear optimization. By using Euclidean Jordan algebras, we derive the iteration bounds that match the currently best known iteration bounds for large- and small-update methods, which are as good as the linear optimization analogue.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

2012 Fifth International Joint Conference on Computational Sciences and Optimization

自引率

0.00%

发文量