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SUMMARY:Tyler Helmuth (University of Durham)
DTSTART;VALUE=DATE-TIME:20210223T140000Z
DTEND;VALUE=DATE-TIME:20210223T150000Z
DTSTAMP;VALUE=DATE-TIME:20210228T183122Z
UID:IAMP_seminars/34
DESCRIPTION:Title: Efficient algorithms for low-temperature spin systems\nby Tyler
Helmuth (University of Durham) as part of One world IAMP mathematical phy
sics seminar\n\n\nAbstract\nTwo fundamental algorithmic tasks associated t
o discrete statistical mechanics models are approximate counting and appro
ximate sampling. At high temperatures Markov chains give efficient algorit
hms\, but at low temperatures mixing times can become impractically large\
, and Markov chain methods may fail to be efficient. Recently\, expansion
methods (cluster expansions\, Pirogov--Sinai theory) have been put to use
to develop provably efficient low-temperature algorithms for some discrete
statistical mechanics models. I’ll introduce these algorithmic tasks\,
outline how expansion algorithms work\, and indicate some open directions.
\n
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