A generic algorithm for approximately solving stochastic graph optimization problems

Ei Ando, Hirotaka Ono, Masafumi Yamashita

研究成果: 書籍/レポート タイプへの寄稿会議への寄与

抄録

Given a (directed or undirected) graph G = (V,E), a mutually independent random variable Xe obeying a normal distribution for each edge e ∈ E that represents its edge weight, and a property P on graph, a stochastic graph maximization problem asks the distribution function F MAX(x) of random variable XMAX = maxP∈P Σe∈A Xe, where property P is identified with the set of subgraphs P = (U,A) of G having P. This paper proposes a generic algorithm for computing an elementary function F̃(x) that approximates FMAX(x). It is applicable to any P and runs in time O(T AMAX(P)+TACNT(P)), provided the existence of an algorithm AMAX that solves the (deterministic) graph maximization problem for P in time TAMAX(P) and an algorithm ACNT that outputs an upper bound on |P| in time TACNT(P).We analyze the approximation ratio and apply it to three graph maximization problems. In case no efficient algorithms are known for solving the graph maximization problem for P, an approximation algorithm AAPR can be used instead of AMAX to reduce the time complexity, at the expense of increase of approximation ratio. Our algorithm can be modified to handle minimization problems.

本文言語英語
ホスト出版物のタイトルStochastic Algorithms
ホスト出版物のサブタイトルFoundations and Applications - 5th International Symposium, SAGA 2009, Proceedings
ページ89-103
ページ数15
DOI
出版ステータス出版済み - 2009
イベント5th Symposium on Stochastic Algorithms, Foundations and Applications, SAGA 2009 - Sapporo, 日本
継続期間: 10月 26 200910月 28 2009

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
5792 LNCS
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

その他

その他5th Symposium on Stochastic Algorithms, Foundations and Applications, SAGA 2009
国/地域日本
CitySapporo
Period10/26/0910/28/09

!!!All Science Journal Classification (ASJC) codes

  • 理論的コンピュータサイエンス
  • コンピュータ サイエンス(全般)

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