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Fast mixing via polymers for random graphs with unbounded degree

Abstract:

The polymer model framework is a classical tool from statistical mechanics that has recently been used to obtain approximation algorithms for spin systems on classes of bounded-degree graphs; examples include the ferromagnetic Potts model on expanders and on the grid. One of the key ingredients in the analysis of polymer models is controlling the growth rate of the number of polymers, which has been typically achieved so far by invoking the bounded-degree assumption. Nevertheless, this ass...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.4230/LIPIcs.APPROX/RANDOM.2021.36

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Division:
MPLS
Department:
Computer Science
Role:
Author
ORCID:
0000-0003-1879-6089
Publisher:
Schloss Dagstuhl Publisher's website
Host title:
Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2021)
Series:
Leibniz International Proceedings in Informatics
Volume:
207
Pages:
36:1--36:13
Publication date:
2021-09-15
Acceptance date:
2021-06-23
Event title:
25th International Conference on Randomization and Computation (RANDOM)
Event location:
Virtual Event
Event website:
https://randomconference.com/random-2021-home/
Event start date:
2021-08-16
Event end date:
2021-08-18
DOI:
ISSN:
1868-8969
ISBN:
978-3-95977-207-5
Language:
English
Keywords:
Pubs id:
1183220
Local pid:
pubs:1183220
Deposit date:
2021-06-24

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