L-Discrepancy analysis of polynomial-time deterministic samplers emulating rapidly mixing chains

Takeharu Shiraga, Yukiko Yamauchi, Shuji Kijima, Masafumi Yamashita

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

4 被引用数 (Scopus)

抄録

Markov chain Monte Carlo (MCMC) is a standard technique to sample from a target distribution by simulating Markov chains. In an analogous fashion to MCMC, this paper proposes a deterministic sampling algorithm based on deterministic random walk, such as the rotor-router model (a.k.a. Propp machine). For the algorithm, we give an upper bound of the point-wise distance (i.e., infinity norm) between the "distributions" of a deterministic random walk and its corresponding Markov chain in terms of the mixing time of the Markov chain. As a result, for uniform sampling of #P-complete problems, such as 0-1 knapsack solutions, linear extensions, matchings, etc., for which rapidly mixing chains are known, our deterministic algorithm provides samples with a "distribution" with a point-wise distance at most ε from the target distribution, in time polynomial in the input size and ε-1.

本文言語英語
ホスト出版物のタイトルComputing and Combinatorics - 20th International Conference, COCOON 2014, Proceedings
出版社Springer Verlag
ページ25-36
ページ数12
ISBN(印刷版)9783319087825
DOI
出版ステータス出版済み - 1 1 2014
イベント20th International Computing and Combinatorics Conference, COCOON 2014 - Atlanta, GA, 米国
継続期間: 8 4 20148 6 2014

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)
8591 LNCS
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

その他

その他20th International Computing and Combinatorics Conference, COCOON 2014
Country米国
CityAtlanta, GA
Period8/4/148/6/14

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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