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arXiv 2006.06257cond-mat.dis-nncond-mat.stat-mechphysics.comp-ph
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随机场Potts模型优化算法的比较

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

Manoj Kumar, Martin Weigel

摘要:

对于许多具有淬火无序的系统,基态的研究可以至关重要地有助于彻底理解所起作用的物理学,无论是临界行为,如果它是由零温度固定点或揭示有序相的性质。虽然基态原则上可以使用通用优化算法(例如模拟退火或遗传算法)来计算,但是使用专门针对手头问题定制的精确或近似技术通常要有效得多。对于某些具有离散自由度的系统,例如随机场伊辛模型,有多项式时间方法来计算精确的基态。但是,即使在随机场波茨模型中,状态的数量增加到两个以上,问题也变得NP困难,人们不能指望找到相关系统大小的精确基态。在这里,我们比较了一些近似的技术,这个问题,并评估其性能。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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