Ittai Abraham, D. Durfee, I. Koutis, Sebastian Krinninger, Richard Peng
{"title":"关于全动态图稀疏器","authors":"Ittai Abraham, D. Durfee, I. Koutis, Sebastian Krinninger, Richard Peng","doi":"10.1109/FOCS.2016.44","DOIUrl":null,"url":null,"abstract":"We initiate the study of fast dynamic algorithms for graph sparsification problems and obtain fully dynamic algorithms, allowing both edge insertions and edge deletions, that take polylogarithmic time after each update in the graph. Our three main results are as follows. First, we give a fully dynamic algorithm for maintaining a (1 ± ϵ)-spectral sparsifier with amortized update time poly(log n, ϵ<sup>-1</sup>). Second, we give a fully dynamic algorithm for maintaining a (1 ± ϵ)-cut sparsifier with worst-case update time poly(log n, ϵ<sup>-1</sup>). Both sparsifiers have size n · poly(log n, ϵ<sup>-1</sup>). Third, we apply our dynamic sparsifier algorithm to obtain a fully dynamic algorithm for maintaining a (1 - ϵ)-approximation to the value of the maximum flow in an unweighted, undirected, bipartite graph with amortized update time poly(log n, ϵ<sup>-1</sup>).","PeriodicalId":414001,"journal":{"name":"2016 IEEE 57th Annual Symposium on Foundations of Computer Science (FOCS)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"68","resultStr":"{\"title\":\"On Fully Dynamic Graph Sparsifiers\",\"authors\":\"Ittai Abraham, D. Durfee, I. Koutis, Sebastian Krinninger, Richard Peng\",\"doi\":\"10.1109/FOCS.2016.44\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We initiate the study of fast dynamic algorithms for graph sparsification problems and obtain fully dynamic algorithms, allowing both edge insertions and edge deletions, that take polylogarithmic time after each update in the graph. Our three main results are as follows. First, we give a fully dynamic algorithm for maintaining a (1 ± ϵ)-spectral sparsifier with amortized update time poly(log n, ϵ<sup>-1</sup>). Second, we give a fully dynamic algorithm for maintaining a (1 ± ϵ)-cut sparsifier with worst-case update time poly(log n, ϵ<sup>-1</sup>). Both sparsifiers have size n · poly(log n, ϵ<sup>-1</sup>). Third, we apply our dynamic sparsifier algorithm to obtain a fully dynamic algorithm for maintaining a (1 - ϵ)-approximation to the value of the maximum flow in an unweighted, undirected, bipartite graph with amortized update time poly(log n, ϵ<sup>-1</sup>).\",\"PeriodicalId\":414001,\"journal\":{\"name\":\"2016 IEEE 57th Annual Symposium on Foundations of Computer Science (FOCS)\",\"volume\":\"13 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-04-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"68\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 IEEE 57th Annual Symposium on Foundations of Computer Science (FOCS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/FOCS.2016.44\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE 57th Annual Symposium on Foundations of Computer Science (FOCS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/FOCS.2016.44","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
We initiate the study of fast dynamic algorithms for graph sparsification problems and obtain fully dynamic algorithms, allowing both edge insertions and edge deletions, that take polylogarithmic time after each update in the graph. Our three main results are as follows. First, we give a fully dynamic algorithm for maintaining a (1 ± ϵ)-spectral sparsifier with amortized update time poly(log n, ϵ-1). Second, we give a fully dynamic algorithm for maintaining a (1 ± ϵ)-cut sparsifier with worst-case update time poly(log n, ϵ-1). Both sparsifiers have size n · poly(log n, ϵ-1). Third, we apply our dynamic sparsifier algorithm to obtain a fully dynamic algorithm for maintaining a (1 - ϵ)-approximation to the value of the maximum flow in an unweighted, undirected, bipartite graph with amortized update time poly(log n, ϵ-1).