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Stochastic Optimization : Glauber Dynamics Versus Stochastic Cellular Automata

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Title: Stochastic Optimization : Glauber Dynamics Versus Stochastic Cellular Automata
Authors: Fukushima-Kimura, Bruno Hideki Browse this author
Kamijima, Yoshinori Browse this author
Kawamura, Kazushi Browse this author
Sakai, Akira Browse this author →KAKEN DB
Keywords: stochastic optimization
simulated annealing
Ising model
Markov chain Monte Carlo
Issue Date: 15-Jan-2023
Publisher: システム制御情報学会
Journal Title: Transactions of the Institute of Systems, Control and Information Engineers
Journal Title(alt): システム制御情報学会論文誌
Volume: 36
Issue: 1
Start Page: 9
End Page: 16
Publisher DOI: 10.5687/iscie.36.9
Abstract: The topic we address in this paper concerns the minimization of a Hamiltonian function for an Ising model through the application of simulated annealing algorithms based on (single-site) Glauber dynamics and stochastic cellular automata (SCA). Some rigorous results are presented in order to justify the application of simulated annealing for a particular kind of SCA. After that, we compare the SCA algorithm and its variation, namely the ε-SCA algorithm, studied in this paper with the Glauber dynamics by analyzing their accuracy in obtaining optimal solutions for the max-cut problem on Erdös-Rényi random graphs, the traveling salesman problem (TSP), and the minimization of Gaussian and Bernoulli spin glass Hamiltonians. We observed that the SCA performed better than the Glauber dynamics in some special cases, while the ε-SCA showed the highest performance in all scenarios.
Publisher URI: https://arxiv.org/abs/2211.06809
Type: article (author version)
URI: http://hdl.handle.net/2115/90806
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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