Article ID Journal Published Year Pages File Type
1143473 Operations Research Letters 2008 4 Pages PDF
Abstract
Markov chain methods for Boltzmann sampling work in phases with decreasing temperatures. The number of transitions in each phase crucially affects terminal state distribution. We employ dynamic programming to allocate iterations to phases to improve guarantees on sample quality. Numerical experiments on the Ising model are presented.
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Related Topics
Physical Sciences and Engineering Mathematics Discrete Mathematics and Combinatorics
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A dynamic programming approach to efficient sampling from Boltzmann distributions
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