{"title":"求解金融组合设计问题的VNS元启发式方法","authors":"","doi":"10.4018/978-1-7998-1882-3.ch007","DOIUrl":null,"url":null,"abstract":"This chapter introduces a VNS-based local search for solving efficiently a financial portfolio design problem described in Chapter 1 and modeled in Chapter 3. The mathematical model tackled is a 0-1 quadratic model. It is well known that exact solving approaches on large instances of this kind of model are costly. The authors have proposed local search approaches to solve the problem, and the efficiency of this type of method has been proved. This chapter shows that the matricial 0-1 model of the problem enables specialized VNS algorithms by taking into account the particular structure of the financial problem considered. First experiments show that VNS with simulated annealing is effective on non-trivial instances of the problem.","PeriodicalId":274834,"journal":{"name":"Algorithms for Solving Financial Portfolio Design Problems","volume":"14 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"VNS Metaheuristics to Solve a Financial Portfolio Design Problem\",\"authors\":\"\",\"doi\":\"10.4018/978-1-7998-1882-3.ch007\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This chapter introduces a VNS-based local search for solving efficiently a financial portfolio design problem described in Chapter 1 and modeled in Chapter 3. The mathematical model tackled is a 0-1 quadratic model. It is well known that exact solving approaches on large instances of this kind of model are costly. The authors have proposed local search approaches to solve the problem, and the efficiency of this type of method has been proved. This chapter shows that the matricial 0-1 model of the problem enables specialized VNS algorithms by taking into account the particular structure of the financial problem considered. First experiments show that VNS with simulated annealing is effective on non-trivial instances of the problem.\",\"PeriodicalId\":274834,\"journal\":{\"name\":\"Algorithms for Solving Financial Portfolio Design Problems\",\"volume\":\"14 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Algorithms for Solving Financial Portfolio Design Problems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4018/978-1-7998-1882-3.ch007\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Algorithms for Solving Financial Portfolio Design Problems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4018/978-1-7998-1882-3.ch007","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
VNS Metaheuristics to Solve a Financial Portfolio Design Problem
This chapter introduces a VNS-based local search for solving efficiently a financial portfolio design problem described in Chapter 1 and modeled in Chapter 3. The mathematical model tackled is a 0-1 quadratic model. It is well known that exact solving approaches on large instances of this kind of model are costly. The authors have proposed local search approaches to solve the problem, and the efficiency of this type of method has been proved. This chapter shows that the matricial 0-1 model of the problem enables specialized VNS algorithms by taking into account the particular structure of the financial problem considered. First experiments show that VNS with simulated annealing is effective on non-trivial instances of the problem.