Title:
Algorithmic Pirogov-Sinai theory

dc.contributor.author Perkins, Will
dc.contributor.corporatename Georgia Institute of Technology. Algorithms, Randomness and Complexity Center en_US
dc.contributor.corporatename University of Illinois at Chicago. Dept. of Mathematics, Statistics, and Computer Science en_US
dc.date.accessioned 2018-11-14T03:06:52Z
dc.date.available 2018-11-14T03:06:52Z
dc.date.issued 2018-11-05
dc.description Presented on November 5, 2018 at 11:00 a.m. in the Klaus Advanced Computing Building, Room 1116E. en_US
dc.description Will Perkins is an assistant professor in the department of Mathematics, Statistics and Computer Science at the University of Illinois at Chicago. My research is a mix of probability, statistics, computer science, and combinatorics. en_US
dc.description Runtime: 53:17 minutes en_US
dc.description.abstract We develop efficient algorithms to approximate the partition function and sample from the hard-core and Potts models on lattices at sufficiently low temperatures in the phase coexistence regime. In contrast, the Glauber dynamics are known to take exponential time to mix in this regime. Our algorithms are based on the cluster expansion and Pirogov-Sinai theory, classical tools from statistical physics for understanding phase transitions, as well as Barvinok's approach to polynomial approximation. Joint work with Tyler Helmuth and Guus Regts. en_US
dc.format.extent 53:17 minutes
dc.identifier.uri http://hdl.handle.net/1853/60534
dc.language.iso en_US en_US
dc.relation.ispartofseries Algorithms and Randomness Center (ARC) Colloquium
dc.subject Hard-core model en_US
dc.subject Potts model en_US
dc.subject Sampling algorithms en_US
dc.title Algorithmic Pirogov-Sinai theory en_US
dc.type Moving Image
dc.type.genre Lecture
dspace.entity.type Publication
local.contributor.corporatename Algorithms and Randomness Center
local.contributor.corporatename College of Computing
local.relation.ispartofseries ARC Colloquium
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relation.isOrgUnitOfPublication c8892b3c-8db6-4b7b-a33a-1b67f7db2021
relation.isSeriesOfPublication c933e0bc-0cb1-4791-abb4-ed23c5b3be7e
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