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Mixing Time and Simulated Annealing for the Stochastic Cellular Automata

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Title: Mixing Time and Simulated Annealing for the Stochastic Cellular Automata
Authors: Fukushima-Kimura, Bruno Hideki Browse this author
Handa, Satoshi Browse this author
Kamakura, Katsuhiro Browse this author
Kamijima, Yoshinori Browse this author
Kawamura, Kazushi Browse this author
Sakai, Akira Browse this author →KAKEN DB
Keywords: Probabilistic cellular automata
stochastic cellular automata
simulated annealing
stochastic optimization
parallel dynamics
Markov chain Monte Carlo
ground states of Ising model
Ising model
Issue Date: 2023
Publisher: Springer
Journal Title: Journal of statistical physics
Volume: 190
Issue: 4
Start Page: 79
Publisher DOI: 10.1007/s10955-023-03090-x
Abstract: Finding a ground state of a given Hamiltonian of an Ising model on a graph G = (V, E) is an important but hard problem. The standard approach for this kind of problem is the application of algorithms that rely on single-spin-flip Markov chain Monte Carlo methods, such as the simulated annealing based on Glauber or Metropolis dynamics. In this paper, we investigate a particular kind of stochastic cellular automata, in which all spins are updated independently and simultaneously. We prove that (i) if the temperature is fixed sufficiently high, then the mixing time is at most of order log |V |, and that (ii) if the temperature drops in time n as 1/ log n, then the limiting measure is uniformly distributed over the ground states. We also provide some simulations of the algorithms studied in this paper implemented on a GPU and show their superior performance compared to the conventional simulated annealing.
Publisher URI: https://arxiv.org/abs/2007.11287
Type: article (author version)
URI: http://hdl.handle.net/2115/90778
Appears in Collections:理学院・理学研究院 (Graduate School of Science / Faculty of Science) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

Submitter: 坂井 哲

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