{"title":"具有凸约束的二次规划的最优性。传感器数据融合的应用","authors":"C. Musso, P. Dodin","doi":"10.1109/ICIF.2002.1020991","DOIUrl":null,"url":null,"abstract":"We analyse theoretically a maximisation quadratic program which can arise in multi-target/multi-sensor area. The goal is to find the point x which minimizes the quadratic distance between x and a given point y. This optimum must lie in a convex constrained region defined by linear inequalities. We present a characterisation of this optimum in a compact dual form. This optimisation framework can be helpful, for example, in muti-objective programming like decentralized resource allocation.","PeriodicalId":399150,"journal":{"name":"Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2002-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Characterization of the optimum of a quadratic program with convex constraints. Application to sensor data fusion\",\"authors\":\"C. Musso, P. Dodin\",\"doi\":\"10.1109/ICIF.2002.1020991\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We analyse theoretically a maximisation quadratic program which can arise in multi-target/multi-sensor area. The goal is to find the point x which minimizes the quadratic distance between x and a given point y. This optimum must lie in a convex constrained region defined by linear inequalities. We present a characterisation of this optimum in a compact dual form. This optimisation framework can be helpful, for example, in muti-objective programming like decentralized resource allocation.\",\"PeriodicalId\":399150,\"journal\":{\"name\":\"Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2002-07-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICIF.2002.1020991\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIF.2002.1020991","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Characterization of the optimum of a quadratic program with convex constraints. Application to sensor data fusion
We analyse theoretically a maximisation quadratic program which can arise in multi-target/multi-sensor area. The goal is to find the point x which minimizes the quadratic distance between x and a given point y. This optimum must lie in a convex constrained region defined by linear inequalities. We present a characterisation of this optimum in a compact dual form. This optimisation framework can be helpful, for example, in muti-objective programming like decentralized resource allocation.
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