{"title":"对学习分支定界积分策略的探讨","authors":"Mohamed Mustapha Kabbaj, A. E. Afia","doi":"10.1109/ICMCS.2016.7905626","DOIUrl":null,"url":null,"abstract":"Branch and bound is the preferred algorithm used for solving MILP problems. It involves two fundamental strategies that are node selection strategy and branching strategy. Whereas the learning literature has been focused in dealing with just one strategy on the same time, we design a two-in-one strategy of branch and bound algorithm regarding the fact that are intuitively dependent. To do so, we apply the well-known SVM algorithm to the well-known set of problems MIPLIP.","PeriodicalId":345854,"journal":{"name":"2016 5th International Conference on Multimedia Computing and Systems (ICMCS)","volume":"153 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":"{\"title\":\"Towards learning integral strategy of branch and bound\",\"authors\":\"Mohamed Mustapha Kabbaj, A. E. Afia\",\"doi\":\"10.1109/ICMCS.2016.7905626\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Branch and bound is the preferred algorithm used for solving MILP problems. It involves two fundamental strategies that are node selection strategy and branching strategy. Whereas the learning literature has been focused in dealing with just one strategy on the same time, we design a two-in-one strategy of branch and bound algorithm regarding the fact that are intuitively dependent. To do so, we apply the well-known SVM algorithm to the well-known set of problems MIPLIP.\",\"PeriodicalId\":345854,\"journal\":{\"name\":\"2016 5th International Conference on Multimedia Computing and Systems (ICMCS)\",\"volume\":\"153 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 5th International Conference on Multimedia Computing and Systems (ICMCS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICMCS.2016.7905626\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 5th International Conference on Multimedia Computing and Systems (ICMCS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICMCS.2016.7905626","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Towards learning integral strategy of branch and bound
Branch and bound is the preferred algorithm used for solving MILP problems. It involves two fundamental strategies that are node selection strategy and branching strategy. Whereas the learning literature has been focused in dealing with just one strategy on the same time, we design a two-in-one strategy of branch and bound algorithm regarding the fact that are intuitively dependent. To do so, we apply the well-known SVM algorithm to the well-known set of problems MIPLIP.
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