{"title":"用拉格朗日松弛法设计GMPLS网络的最优路径","authors":"T. Fukumoto, N. Komoda","doi":"10.1109/SCIS.2007.367697","DOIUrl":null,"url":null,"abstract":"We describe an optimal path design for a GMPLS network that uses the Lagrangian relaxation method, which can estimate the lower bounds of the solution to a problem. This feature helps the designer of the problem to take the accuracy of the solution obtained by the calculation into consideration when he makes a decision to assign the solution to a real network in critical situations. A formulation of the problem and how to solve it using the Lagrangian relaxation method is described, and the results obtained by a prototype and considerations are shown in this paper.","PeriodicalId":184726,"journal":{"name":"2007 IEEE Symposium on Computational Intelligence in Scheduling","volume":"17 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Optimal Paths Design for a GMPLS Network using the Lagrangian Relaxation Method\",\"authors\":\"T. Fukumoto, N. Komoda\",\"doi\":\"10.1109/SCIS.2007.367697\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We describe an optimal path design for a GMPLS network that uses the Lagrangian relaxation method, which can estimate the lower bounds of the solution to a problem. This feature helps the designer of the problem to take the accuracy of the solution obtained by the calculation into consideration when he makes a decision to assign the solution to a real network in critical situations. A formulation of the problem and how to solve it using the Lagrangian relaxation method is described, and the results obtained by a prototype and considerations are shown in this paper.\",\"PeriodicalId\":184726,\"journal\":{\"name\":\"2007 IEEE Symposium on Computational Intelligence in Scheduling\",\"volume\":\"17 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2007-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2007 IEEE Symposium on Computational Intelligence in Scheduling\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SCIS.2007.367697\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2007 IEEE Symposium on Computational Intelligence in Scheduling","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SCIS.2007.367697","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Optimal Paths Design for a GMPLS Network using the Lagrangian Relaxation Method
We describe an optimal path design for a GMPLS network that uses the Lagrangian relaxation method, which can estimate the lower bounds of the solution to a problem. This feature helps the designer of the problem to take the accuracy of the solution obtained by the calculation into consideration when he makes a decision to assign the solution to a real network in critical situations. A formulation of the problem and how to solve it using the Lagrangian relaxation method is described, and the results obtained by a prototype and considerations are shown in this paper.