Title	Sum-of-squares $\equiv_{avg}$ Spectral Algorithms
Creator	Schramm, Tselil
Publisher	Banff International Research Station for Mathematical Innovation and Discovery
Date Issued	2017-11-16T16:32
Description	It is well-known that semidefinite programs (such as the Sum-of-Squares or SoS semidefinite programming relaxation) capture spectral arguments. A recent line of work has shown that the reverse is also true for many average-case problems: spectral algorithms are just as powerful as SoS for planted clique, refuting random CSPs, spiked random tensors, and more. In this talk, I will discuss a recent result which shows the equivalence of SoS and spectral algorithms is not a coincidence, and can be shown in a black-box fashion for a broad class of average-case problems. Based on joint work with Sam Hopkins, Pravesh Kothari, Aaron Potechin, Prasad Raghavendra and David Steurer.
Extent	30 minutes
Subject	Mathematics; Computer science; Operations research, mathematical programming; Theoretical computer science
Type	Moving Image
File Format	video/mp4
Language	eng
Notes	Author affiliation: Simons Institute/UC Berkeley
Series	BIRS Workshop Lecture Videos (Banff, Alta)
Date Available	2018-05-16
Provider	Vancouver : University of British Columbia Library
Rights	Attribution-NonCommercial-NoDerivatives 4.0 International
DOI	10.14288/1.0366863
URI	http://hdl.handle.net/2429/65915
Affiliation	Non UBC
Peer Review Status	Unreviewed
Scholarly Level	Postdoctoral
Rights URI	http://creativecommons.org/licenses/by-nc-nd/4.0/
Aggregated Source Repository	DSpace

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Sum-of-squares $\equiv_{avg}$ Spectral Algorithms Schramm, Tselil

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