{"title":"用于反转排序的基于列生成的分支定界算法","authors":"A. Caprara, G. Lancia, See-Kiong Ng","doi":"10.1090/dimacs/047/10","DOIUrl":null,"url":null,"abstract":"We consider the problem of sorting a permutation by reversals (SBR), calling for the minimum number of reversals transforming a given permutation of f1; : : : ; ng into the identity permutation. SBR was inspired by computational biology applications , in particular genome rearrangement. We propose an exact branch-and-bound algorithm for SBR. A lower bound is computed by solving a linear program with a possibly exponential (in n) number of variables, by using column generation techniques. An eeective branching scheme is described, which is combined with a greedy algorithm capable of producing near{optimal solutions. The algorithm presented can solve to optimality SBR instances of considerably larger size with respect to previous existing methods.","PeriodicalId":175691,"journal":{"name":"Mathematical Support for Molecular Biology","volume":"52 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"30","resultStr":"{\"title\":\"A column-generation based branch-and-bound algorithm for sorting by reversals\",\"authors\":\"A. Caprara, G. Lancia, See-Kiong Ng\",\"doi\":\"10.1090/dimacs/047/10\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We consider the problem of sorting a permutation by reversals (SBR), calling for the minimum number of reversals transforming a given permutation of f1; : : : ; ng into the identity permutation. SBR was inspired by computational biology applications , in particular genome rearrangement. We propose an exact branch-and-bound algorithm for SBR. A lower bound is computed by solving a linear program with a possibly exponential (in n) number of variables, by using column generation techniques. An eeective branching scheme is described, which is combined with a greedy algorithm capable of producing near{optimal solutions. The algorithm presented can solve to optimality SBR instances of considerably larger size with respect to previous existing methods.\",\"PeriodicalId\":175691,\"journal\":{\"name\":\"Mathematical Support for Molecular Biology\",\"volume\":\"52 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"30\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Mathematical Support for Molecular Biology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1090/dimacs/047/10\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Mathematical Support for Molecular Biology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1090/dimacs/047/10","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A column-generation based branch-and-bound algorithm for sorting by reversals
We consider the problem of sorting a permutation by reversals (SBR), calling for the minimum number of reversals transforming a given permutation of f1; : : : ; ng into the identity permutation. SBR was inspired by computational biology applications , in particular genome rearrangement. We propose an exact branch-and-bound algorithm for SBR. A lower bound is computed by solving a linear program with a possibly exponential (in n) number of variables, by using column generation techniques. An eeective branching scheme is described, which is combined with a greedy algorithm capable of producing near{optimal solutions. The algorithm presented can solve to optimality SBR instances of considerably larger size with respect to previous existing methods.