- Title Information
- Title
- Applications of randomized algorithms to counting problems
- Name:
Personal
- Name Part
- Sukurdeep, Yashil
- Role
- Role Term:
Text
- creator
- Name:
Personal
- Name Part
- Dupuis, Paul
- Role
- Role Term:
Text
- advisor
- affiliation
- Brown University. Department of Applied Mathematics
- Name:
Corporate
- Name Part
- Brown University. Karen T. Romer Undergraduate Teaching and Research Awards
- Role
- Role Term:
Text
- research program
- Type of Resource
- text
- Genre (aat)
- posters
- Origin Information
- Place
- Place Term:
Code (MARC Country Code)
- riu
- Place
- Place Term:
Text
- Providence, RI
- Publisher
- Brown University
- Date Created
(keyDate="yes", encoding="w3cdtf")
- 2017
- Physical Description
- Extent
- 1 poster
- digitalOrigin
- reformatted digital
- Abstract
- We study the binary contingency table problem, where our goal is to count the number of n x m binary tables ({0,1}-valued matrices) that satisfy certain given row and column sums. We present a straightforward Markov Chain Monte Carlo (MCMC) algorithm that gives robust estimates for the number of binary contingency tables when the dimension of the matrices is relatively low. We then present the parallel tempering method, which makes use of coupled Markov Chains running at different "temperatures", for approximately counting the number of binary contingency tables. We then discuss the qualitative properties of the parallel tempering method and its advantages with regards to other randomized algorithms such as the splitting algorithm.
- Subject (LCSH)
- Topic
- Markov processes
- Subject (LCSH)
- Topic
- Monte Carlo method
- Subject (LCSH)
- Topic
- Algorithms
- Identifier:
DOI
- 10.26300/zsx9-v648