{"title":"设计概率支持映射熵域","authors":"J. Walsh, Alexander Erick Trofimoff","doi":"10.1109/ITW44776.2019.8989076","DOIUrl":null,"url":null,"abstract":"The boundary of the entropy region has been shown to determine fundamental inequalities and limits in key problems in network coding, streaming, distributed storage, and coded caching. The unknown part of this boundary requires nonlinear constructions, which can, in turn, be parameterized by the support of their underlying probability distributions. Recognizing that the terms in entropy are submodular enables the design of such supports to maximally push out towards this boundary.","PeriodicalId":214379,"journal":{"name":"2019 IEEE Information Theory Workshop (ITW)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"On Designing Probabilistic Supports to Map the Entropy Region\",\"authors\":\"J. Walsh, Alexander Erick Trofimoff\",\"doi\":\"10.1109/ITW44776.2019.8989076\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The boundary of the entropy region has been shown to determine fundamental inequalities and limits in key problems in network coding, streaming, distributed storage, and coded caching. The unknown part of this boundary requires nonlinear constructions, which can, in turn, be parameterized by the support of their underlying probability distributions. Recognizing that the terms in entropy are submodular enables the design of such supports to maximally push out towards this boundary.\",\"PeriodicalId\":214379,\"journal\":{\"name\":\"2019 IEEE Information Theory Workshop (ITW)\",\"volume\":\"71 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 IEEE Information Theory Workshop (ITW)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ITW44776.2019.8989076\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE Information Theory Workshop (ITW)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ITW44776.2019.8989076","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
On Designing Probabilistic Supports to Map the Entropy Region
The boundary of the entropy region has been shown to determine fundamental inequalities and limits in key problems in network coding, streaming, distributed storage, and coded caching. The unknown part of this boundary requires nonlinear constructions, which can, in turn, be parameterized by the support of their underlying probability distributions. Recognizing that the terms in entropy are submodular enables the design of such supports to maximally push out towards this boundary.