Approximation algorithm and perfect sampler for closed jackson networks with single servers

S. Kijima, T. Matsui

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

In this paper, we propose the first fully polynomial-time randomized approximation scheme (FPRAS) for closed Jackson networks with single servers. Our algorithm is based on the Markov chain Monte Carlo (MCMC) method, and our scheme returns an approximate solution, for which the size of error satisfies a given error rate. We propose two Markov chains: one is for approximate sampling, and the other is for perfect sampling based on the monotone coupling from the past algorithm.

Original languageEnglish
Pages (from-to)1484-1503
Number of pages20
JournalSIAM Journal on Computing
Volume38
Issue number4
DOIs
Publication statusPublished - Nov 7 2008
Externally publishedYes

Fingerprint

Jackson Networks
Single Server
Approximation algorithms
Markov processes
Approximation Algorithms
Servers
Perfect Sampling
Coupling from the Past
Sampling
Closed
Markov Chain Monte Carlo Methods
Approximation Scheme
Error Rate
Markov chain
Monotone
Polynomial time
Approximate Solution
Monte Carlo methods
Polynomials

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Mathematics(all)

Cite this

Approximation algorithm and perfect sampler for closed jackson networks with single servers. / Kijima, S.; Matsui, T.

In: SIAM Journal on Computing, Vol. 38, No. 4, 07.11.2008, p. 1484-1503.

Research output: Contribution to journalArticle

@article{a167a6e52de843c6bbdd5ebc20d695ca,
title = "Approximation algorithm and perfect sampler for closed jackson networks with single servers",
abstract = "In this paper, we propose the first fully polynomial-time randomized approximation scheme (FPRAS) for closed Jackson networks with single servers. Our algorithm is based on the Markov chain Monte Carlo (MCMC) method, and our scheme returns an approximate solution, for which the size of error satisfies a given error rate. We propose two Markov chains: one is for approximate sampling, and the other is for perfect sampling based on the monotone coupling from the past algorithm.",
author = "S. Kijima and T. Matsui",
year = "2008",
month = "11",
day = "7",
doi = "10.1137/06064980X",
language = "English",
volume = "38",
pages = "1484--1503",
journal = "SIAM Journal on Computing",
issn = "0097-5397",
publisher = "Society for Industrial and Applied Mathematics Publications",
number = "4",

}

TY - JOUR

T1 - Approximation algorithm and perfect sampler for closed jackson networks with single servers

AU - Kijima, S.

AU - Matsui, T.

PY - 2008/11/7

Y1 - 2008/11/7

N2 - In this paper, we propose the first fully polynomial-time randomized approximation scheme (FPRAS) for closed Jackson networks with single servers. Our algorithm is based on the Markov chain Monte Carlo (MCMC) method, and our scheme returns an approximate solution, for which the size of error satisfies a given error rate. We propose two Markov chains: one is for approximate sampling, and the other is for perfect sampling based on the monotone coupling from the past algorithm.

AB - In this paper, we propose the first fully polynomial-time randomized approximation scheme (FPRAS) for closed Jackson networks with single servers. Our algorithm is based on the Markov chain Monte Carlo (MCMC) method, and our scheme returns an approximate solution, for which the size of error satisfies a given error rate. We propose two Markov chains: one is for approximate sampling, and the other is for perfect sampling based on the monotone coupling from the past algorithm.

UR - http://www.scopus.com/inward/record.url?scp=55249109996&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=55249109996&partnerID=8YFLogxK

U2 - 10.1137/06064980X

DO - 10.1137/06064980X

M3 - Article

AN - SCOPUS:55249109996

VL - 38

SP - 1484

EP - 1503

JO - SIAM Journal on Computing

JF - SIAM Journal on Computing

SN - 0097-5397

IS - 4

ER -