Result: Global Minimum Depth in Edwards-Anderson Model
Title:
Global Minimum Depth in Edwards-Anderson Model
Authors:
Source:
Communications in Computer and Information Science ISBN: 9783030202569
Publication Status:
Preprint
Publisher Information:
Springer International Publishing, 2019.
Publication Year:
2019
Subject Terms:
Global minimum, Statistical Mechanics (cond-mat.stat-mech), FOS: Physical sciences, 02 engineering and technology, Disordered Systems and Neural Networks (cond-mat.dis-nn), Condensed Matter - Disordered Systems and Neural Networks, Exact polynomial algorithm, 01 natural sciences, Minimization, Spin system, Planar Ising model, Edwards-Anderson model, Spectrum, 0103 physical sciences, 0202 electrical engineering, electronic engineering, information engineering, Local minimum, Spin glass system, Condensed Matter - Statistical Mechanics
Document Type:
Book
Part of book or chapter of book<br />Article<br />Other literature type
Language:
English
DOI:
10.1007/978-3-030-20257-6_33
DOI:
10.48550/arxiv.2002.00607
Access URL:
http://arxiv.org/pdf/2002.00607
http://arxiv.org/abs/2002.00607
https://arxiv.org/abs/2002.00607
http://ui.adsabs.harvard.edu/abs/2020arXiv200200607K/abstract
http://arxiv.org/pdf/2002.00607.pdf
https://link.springer.com/chapter/10.1007%2F978-3-030-20257-6_33
https://rd.springer.com/chapter/10.1007/978-3-030-20257-6_33
https://dblp.uni-trier.de/db/conf/eann/eann2019.html#KarandashevK19
http://arxiv.org/abs/2002.00607
https://arxiv.org/abs/2002.00607
http://ui.adsabs.harvard.edu/abs/2020arXiv200200607K/abstract
http://arxiv.org/pdf/2002.00607.pdf
https://link.springer.com/chapter/10.1007%2F978-3-030-20257-6_33
https://rd.springer.com/chapter/10.1007/978-3-030-20257-6_33
https://dblp.uni-trier.de/db/conf/eann/eann2019.html#KarandashevK19
Rights:
Springer TDM
arXiv Non-Exclusive Distribution
arXiv Non-Exclusive Distribution
Accession Number:
edsair.doi.dedup.....8343eed8bde1b873e3bc55b5925f3cc2
Database:
OpenAIRE
Further Information
In the literature the most frequently cited data are quite contradictory, and there is no consensus on the global minimum value of 2D Edwards-Anderson (2D EA) Ising model. By means of computer simulations, with the help of exact polynomial Schraudolph-Kamenetsky algorithm, we examined the global minimum depth in 2D EA-type models. We found a dependence of the global minimum depth on the dimension of the problem N and obtained its asymptotic value in the limit $N\to\infty$. We believe these evaluations can be further used for examining the behavior of 2D Bayesian models often used in machine learning and image processing.
10 pages, 4 figures, 2 tables, submitted to conference on Engineering Applications of Neural Networks (EANN 2019)