{"title":"Interior point methods for placement","authors":"P. Chin, A. Vannelli","doi":"10.1109/ISCAS.1994.408782","DOIUrl":null,"url":null,"abstract":"In VLSI layout optimization, the placement problem is usually solved with simulated annealing or heuristic algorithms. These procedures often begin with random initial configurations but may benefit greatly (in terms of execution time or quality of solution) when good initial relative placements are provided. Weis and Mlynski [1987] have presented a linear programming formulation for generating relative placements. In this paper, we show how efficient interior point and preconditioned conjugate gradient methods can be applied to solve large sparse linear programs based on Weis and Mlynski's approach.<<ETX>>","PeriodicalId":140999,"journal":{"name":"Proceedings of IEEE International Symposium on Circuits and Systems - ISCAS '94","volume":"20 S2","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1994-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of IEEE International Symposium on Circuits and Systems - ISCAS '94","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISCAS.1994.408782","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
In VLSI layout optimization, the placement problem is usually solved with simulated annealing or heuristic algorithms. These procedures often begin with random initial configurations but may benefit greatly (in terms of execution time or quality of solution) when good initial relative placements are provided. Weis and Mlynski [1987] have presented a linear programming formulation for generating relative placements. In this paper, we show how efficient interior point and preconditioned conjugate gradient methods can be applied to solve large sparse linear programs based on Weis and Mlynski's approach.<>