{"title":"Why there is no need to use a big-M in linear bilevel optimization: a computational study of two ready-to-use approaches","authors":"Thomas Kleinert, Martin Schmidt","doi":"10.1007/s10287-023-00435-5","DOIUrl":null,"url":null,"abstract":"","PeriodicalId":46743,"journal":{"name":"Computational Management Science","volume":"20 1","pages":"1-12"},"PeriodicalIF":1.3000,"publicationDate":"2023-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"13","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computational Management Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/s10287-023-00435-5","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"SOCIAL SCIENCES, MATHEMATICAL METHODS","Score":null,"Total":0}
期刊介绍：
Computational Management Science (CMS) is an international journal focusing on all computational aspects of management science. These include theoretical and empirical analysis of computational models; computational statistics; analysis and applications of constrained, unconstrained, robust, stochastic and combinatorial optimisation algorithms; dynamic models, such as dynamic programming and decision trees; new search tools and algorithms for global optimisation, modelling, learning and forecasting; models and tools of knowledge acquisition.
The emphasis on computational paradigms is an intended feature of CMS, distinguishing it from more classical operations research journals.
Officially cited as: Comput Manag Sci
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
