On the comparison of optimization algorithms for the random-field Potts model

Manoj Kumar, Martin Weigel

Research output: Contribution to journalConference articlepeer-review

1 Citation (Scopus)
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Abstract: For many systems with quenched disorder the study of ground states can crucially contribute to a thorough understanding of the physics at play, be it for the critical behavior if that is governed by a zero-temperature fixed point or for uncovering properties of the ordered phase. While ground states can in principle be computed using general-purpose optimization algorithms such as simulated annealing or genetic algorithms, it is often much more efficient to use exact or approximate techniques specifically tailored to the problem at hand. For certain systems with discrete degrees of freedom such as the random-field Ising model, there are polynomial-time methods to compute exact ground states. But even as the number of states increases beyond two as in the random-field Potts model, the problem becomes NP hard and one cannot hope to find exact ground states for relevant system sizes. Here, we compare a number of approximate techniques for this problem and evaluate their performance.
Original languageEnglish
Article number012003
Number of pages6
JournalJournal of Physics: Conference Series
Issue number1
Publication statusPublished - 1 Mar 2022
Event33rd Annual CSP Workshop: Recent Developments in Computer Simulation Studies in Condensed Matter Physics - Virtual
Duration: 17 Feb 202121 Mar 2021

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Published under licence by IOP Publishing Ltd

ASJC Scopus subject areas

  • Physics and Astronomy(all)


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