{"title":"An Exact Optimization Algorithm for Linear Decomposition of Index Generation Functions","authors":"Shinobu Nagayama, Tsutomu Sasao, J. T. Butler","doi":"10.1109/ISMVL.2017.56","DOIUrl":null,"url":null,"abstract":"This paper proposes an exact optimization algorithm based on a branch and bound method for linear decomposition of index generation functions. The proposed algorithm efficiently finds the optimum linear decomposition of an index generation function by pruning non-optimum solutions using effective branch and bound strategies. The branch strategy is based on our previous heuristic [2] using a balanced decision tree, and the bound is based on a lower bound on the number of variables needed for linear decomposition. Experimental results using a benchmark index generation function show its optimum linear decompositions and effectiveness of the strategies.","PeriodicalId":393724,"journal":{"name":"2017 IEEE 47th International Symposium on Multiple-Valued Logic (ISMVL)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE 47th International Symposium on Multiple-Valued Logic (ISMVL)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISMVL.2017.56","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6
Abstract
This paper proposes an exact optimization algorithm based on a branch and bound method for linear decomposition of index generation functions. The proposed algorithm efficiently finds the optimum linear decomposition of an index generation function by pruning non-optimum solutions using effective branch and bound strategies. The branch strategy is based on our previous heuristic [2] using a balanced decision tree, and the bound is based on a lower bound on the number of variables needed for linear decomposition. Experimental results using a benchmark index generation function show its optimum linear decompositions and effectiveness of the strategies.