{"title":"Solving the simple offset assignment problem as a traveling salesman","authors":"M. Jünger, Sven Mallach","doi":"10.1145/2463596.2463601","DOIUrl":null,"url":null,"abstract":"In this paper, we present an exact approach to the Simple Offset Assignment problem arising in the domain of address code generation for digital signal processors. It is based on transformations to weighted Hamiltonian cycle problems and integer linear programming. To the best of our knowledge, it is the rst approach capable to solve all instances of the established OffsetStone benchmark set to optimality within reasonable time. It therefore enables the rst evaluation of the quality of several heuristics relative to the optimum solutions. Further, using the same transformations, we present a novel improvement heuristic that provides a well-tunable trade-off between running time and solution quality.","PeriodicalId":344517,"journal":{"name":"M-SCOPES","volume":"202 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"M-SCOPES","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2463596.2463601","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5
Abstract
In this paper, we present an exact approach to the Simple Offset Assignment problem arising in the domain of address code generation for digital signal processors. It is based on transformations to weighted Hamiltonian cycle problems and integer linear programming. To the best of our knowledge, it is the rst approach capable to solve all instances of the established OffsetStone benchmark set to optimality within reasonable time. It therefore enables the rst evaluation of the quality of several heuristics relative to the optimum solutions. Further, using the same transformations, we present a novel improvement heuristic that provides a well-tunable trade-off between running time and solution quality.