Title:
Spectral Independence in High-Dimensional Expanders and Applications to the Hardcore Model

dc.contributor.author Liu, Kuikui
dc.contributor.corporatename Georgia Institute of Technology. Algorithms, Randomness and Complexity Center en_US
dc.contributor.corporatename University of Washington. School of Computer Science and Engineering en_US
dc.date.accessioned 2020-02-05T19:30:59Z
dc.date.available 2020-02-05T19:30:59Z
dc.date.issued 2020-01-27
dc.description Presented on January 27, 2020 at 11:00 a.m. in the Groseclose Building, Room 402. en_US
dc.description Kuikui Liu is a second-year Ph.D. student in the Theory Group at the Paul G. Allen School for Computer Science and Engineering (UW CSE). He is advised by Professor Shayan Oveis Gharan. His research interests are in the geometry of polynomials, spectral graph theory, and high-dimensional geometry. He uses mathematical tools from these areas to design and analyze novel algorithms for solving hard problems. en_US
dc.description Runtime: 57:11 minutes en_US
dc.description.abstract We say a probability distribution µ is spectrally independent if an associated correlation matrix has a bounded largest eigenvalue for the distribution and all of its conditional distributions. We prove that if µ is spectrally independent, then the corresponding high dimensional simplicial complex is a local spectral expander. Using a line of recent works on mixing time of high dimensional walks on simplicial complexes [KM17; DK17; KO18; AL19], this implies that the corresponding Glauber dynamics mixes rapidly and generates (approximate) samples from µ. As an application, we show that natural Glauber dynamics mixes rapidly (in polynomial time) to generate a random independent set from the hardcore model up to the uniqueness threshold. This improves the quasi-polynomial running time of Weitz’s deterministic correlation decay algorithm [Wei06] for estimating the hardcore partition function, also answering a long-standing open problem of mixing time of Glauber dynamics [LV97; LV99; DG00; Vig01; Eft+16]. Joint work with Nima Anari and Shayan Oveis Gharan. en_US
dc.format.extent 57:11 minutes
dc.identifier.uri http://hdl.handle.net/1853/62422
dc.language.iso en_US en_US
dc.relation.ispartofseries Algorithms and Randomness Center (ARC) Colloquium
dc.subject High dimensional en_US
dc.subject Spectral expander en_US
dc.title Spectral Independence in High-Dimensional Expanders and Applications to the Hardcore Model 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
relation.isOrgUnitOfPublication b53238c2-abff-4a83-89ff-3e7b4e7cba3d
relation.isOrgUnitOfPublication c8892b3c-8db6-4b7b-a33a-1b67f7db2021
relation.isSeriesOfPublication c933e0bc-0cb1-4791-abb4-ed23c5b3be7e
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