{"title":"A finite termination mehrotra-type predictor-corrector algorithm for Semidefinite Optimization","authors":"Mingwang Zhang, Huaping Chen, Weihua Li","doi":"10.1109/BICTA.2010.5645134","DOIUrl":null,"url":null,"abstract":"Mehrotra-type predictor-corrector algorithm is one of the most remarkable interior-point methods for linear optimization, and it is also the base of many interior-point methods software packages. This paper presents an extension of the recent variant of Mehrotra's predictor-corrector algorithm that was proposed by Salahi (2007) for linear optimization problems. Based on the NT direction as Newton search direction, the finite termination of the algorithm for Semidefinite Optimization is proved, that is analogous to the linear case.","PeriodicalId":302619,"journal":{"name":"2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/BICTA.2010.5645134","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Mehrotra-type predictor-corrector algorithm is one of the most remarkable interior-point methods for linear optimization, and it is also the base of many interior-point methods software packages. This paper presents an extension of the recent variant of Mehrotra's predictor-corrector algorithm that was proposed by Salahi (2007) for linear optimization problems. Based on the NT direction as Newton search direction, the finite termination of the algorithm for Semidefinite Optimization is proved, that is analogous to the linear case.