{"title":"Performance comparison of two recently proposed copositivity tests","authors":"Bo Peng","doi":"10.1016/j.ejco.2022.100037","DOIUrl":null,"url":null,"abstract":"<div><p>Recently and simultaneously, two MILP-based approaches to copositivity testing were proposed. This note tries a performance comparison, using a group of test sets containing a large number of designed instances. According to the numerical results, we find that one copositivity detection approach performs better when the function value of the defined function <em>h</em> of a matrix is large while the other one performs better when the dimension of problems is increasing moderately. A problem set that is hard for both approaches is also presented, which may be used as a test bed for future competing approaches. An improved variant of one of the approaches is also proposed to handle those hard instances more efficiently.</p></div>","PeriodicalId":51880,"journal":{"name":"EURO Journal on Computational Optimization","volume":"10 ","pages":"Article 100037"},"PeriodicalIF":2.6000,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2192440622000132/pdfft?md5=abbd19fbc87e563c0963318349831747&pid=1-s2.0-S2192440622000132-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"EURO Journal on Computational Optimization","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2192440622000132","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"OPERATIONS RESEARCH & MANAGEMENT SCIENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Recently and simultaneously, two MILP-based approaches to copositivity testing were proposed. This note tries a performance comparison, using a group of test sets containing a large number of designed instances. According to the numerical results, we find that one copositivity detection approach performs better when the function value of the defined function h of a matrix is large while the other one performs better when the dimension of problems is increasing moderately. A problem set that is hard for both approaches is also presented, which may be used as a test bed for future competing approaches. An improved variant of one of the approaches is also proposed to handle those hard instances more efficiently.
期刊介绍:
The aim of this journal is to contribute to the many areas in which Operations Research and Computer Science are tightly connected with each other. More precisely, the common element in all contributions to this journal is the use of computers for the solution of optimization problems. Both methodological contributions and innovative applications are considered, but validation through convincing computational experiments is desirable. The journal publishes three types of articles (i) research articles, (ii) tutorials, and (iii) surveys. A research article presents original methodological contributions. A tutorial provides an introduction to an advanced topic designed to ease the use of the relevant methodology. A survey provides a wide overview of a given subject by summarizing and organizing research results.