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Reply to: Deep reinforced learning heuristic tested on spin-glass ground states: The larger picture.

作者信息

Fan Changjun, Shen Mutian, Nussinov Zohar, Liu Zhong, Sun Yizhou, Liu Yang-Yu

机构信息

College of Systems Engineering, National University of Defense Technology, Changsha, 410073, China.

Department of Physics, Washington University in St. Louis, Campus Box 1105, 1 Brookings Drive, St. Louis, MO, 63130, USA.

出版信息

Nat Commun. 2023 Sep 14;14(1):5659. doi: 10.1038/s41467-023-41108-w.

DOI:10.1038/s41467-023-41108-w
PMID:37709759
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10502092/
Abstract
摘要
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dd24/10502092/3acdba4892bb/41467_2023_41108_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dd24/10502092/3acdba4892bb/41467_2023_41108_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dd24/10502092/3acdba4892bb/41467_2023_41108_Fig1_HTML.jpg

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本文引用的文献

1
Deep reinforced learning heuristic tested on spin-glass ground states: The larger picture.在自旋玻璃基态上测试的深度强化学习启发式方法：宏观图景。
Nat Commun. 2023 Sep 14;14(1):5658. doi: 10.1038/s41467-023-41106-y.
2
Searching for spin glass ground states through deep reinforcement learning.通过深度强化学习搜索自旋玻璃基态。
Nat Commun. 2023 Feb 9;14(1):725. doi: 10.1038/s41467-023-36363-w.
3
Comparing Monte Carlo methods for finding ground states of Ising spin glasses: Population annealing, simulated annealing, and parallel tempering.
比较用于寻找伊辛自旋玻璃基态的蒙特卡罗方法：种群退火、模拟退火和平行回火。
Phys Rev E Stat Nonlin Soft Matter Phys. 2015 Jul;92(1):013303. doi: 10.1103/PhysRevE.92.013303. Epub 2015 Jul 6.
4
Improved extremal optimization for the Ising spin glass.
Phys Rev E Stat Nonlin Soft Matter Phys. 2004 May;69(5 Pt 2):055701. doi: 10.1103/PhysRevE.69.055701. Epub 2004 May 14.
5
Optimization with extremal dynamics.基于极值动力学的优化
Phys Rev Lett. 2001 Jun 4;86(23):5211-4. doi: 10.1103/PhysRevLett.86.5211.