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dc.contributor.authorPerkins, Will
dc.date.accessioned2018-11-14T03:06:52Z
dc.date.available2018-11-14T03:06:52Z
dc.date.issued2018-11-05
dc.identifier.urihttp://hdl.handle.net/1853/60534
dc.descriptionPresented on November 5, 2018 at 11:00 a.m. in the Klaus Advanced Computing Building, Room 1116E.en_US
dc.descriptionWill 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.descriptionRuntime: 53:17 minutesen_US
dc.description.abstractWe 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.extent53:17 minutes
dc.language.isoen_USen_US
dc.relation.ispartofseriesAlgorithms and Randomness Center (ARC) Colloquium
dc.subjectHard-core modelen_US
dc.subjectPotts modelen_US
dc.subjectSampling algorithmsen_US
dc.titleAlgorithmic Pirogov-Sinai theoryen_US
dc.typeMoving Image
dc.contributor.corporatenameGeorgia Institute of Technology. Algorithms, Randomness and Complexity Centeren_US
dc.contributor.corporatenameUniversity of Illinois at Chicago. Dept. of Mathematics, Statistics, and Computer Scienceen_US
dc.type.genreLecture


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