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Polynomial-time approximation algorithms for the antiferromagnetic Ising model on line graphs

Authors: Dyer, M; Heinrich, M; Jerrum, M; Müller, H;

Polynomial-time approximation algorithms for the antiferromagnetic Ising model on line graphs

Abstract

AbstractWe present a polynomial-time Markov chain Monte Carlo algorithm for estimating the partition function of the antiferromagnetic Ising model on any line graph. The analysis of the algorithm exploits the ‘winding’ technology devised by McQuillan [CoRR abs/1301.2880 (2013)] and developed by Huang, Lu and Zhang [Proc. 27th Symp. on Disc. Algorithms (SODA16), 514–527]. We show that exact computation of the partition function is #P-hard, even for line graphs, indicating that an approximation algorithm is the best that can be expected. We also show that Glauber dynamics for the Ising model is rapidly mixing on line graphs, an example being the kagome lattice.

Country
United Kingdom
Keywords

FOS: Computer and information sciences, Graph operations (line graphs, products, etc.), Randomized algorithms, Probability (math.PR), 68Q25 (Primary) 68Q17, 68Q87, 82B20 (Secondary), Computational Complexity (cs.CC), Lattice systems (Ising, dimer, Potts, etc.) and systems on graphs arising in equilibrium statistical mechanics, Approximation algorithms, Computer Science - Computational Complexity, Computer Science - Data Structures and Algorithms, FOS: Mathematics, Computational difficulty of problems (lower bounds, completeness, difficulty of approximation, etc.), Analysis of algorithms, Data Structures and Algorithms (cs.DS), Mathematics - Probability

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
2
Average
Average
Average
Green
bronze