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Statements

Subject Item
n2:RIV%2F68407700%3A21230%2F14%3A00223600%21RIV15-MSM-21230___
rdf:type
skos:Concept n13:Vysledek
rdfs:seeAlso
ftp://cmp.felk.cvut.cz/pub/cmp/articles/werner/ZivWerPru-LP-MIT2014.pdf
dcterms:description
Minimization of a partially separable function of many discrete variables is ubiquitous in machine learning and computer vision, in tasks like maximum a posteriori (MAP) inference in graphical models, or structured prediction. Among successful approaches to this problem is linear programming (LP) relaxation. We discuss this LP relaxation from two aspects. First, we review recent results which characterize languages (classes of functions permitted to form the objective function) for which the problem is solved by the relaxation exactly. Second, we show that solving the LP relaxation is not easier than solving any linear program, which makes a discovery of an efficient algorithm for the LP relaxation unlikely. Minimization of a partially separable function of many discrete variables is ubiquitous in machine learning and computer vision, in tasks like maximum a posteriori (MAP) inference in graphical models, or structured prediction. Among successful approaches to this problem is linear programming (LP) relaxation. We discuss this LP relaxation from two aspects. First, we review recent results which characterize languages (classes of functions permitted to form the objective function) for which the problem is solved by the relaxation exactly. Second, we show that solving the LP relaxation is not easier than solving any linear program, which makes a discovery of an efficient algorithm for the LP relaxation unlikely.
dcterms:title
The Power of LP Relaxation for MAP Inference The Power of LP Relaxation for MAP Inference
skos:prefLabel
The Power of LP Relaxation for MAP Inference The Power of LP Relaxation for MAP Inference
skos:notation
RIV/68407700:21230/14:00223600!RIV15-MSM-21230___
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n4:aktivity
P(7E10047), P(7E11036), P(GAP202/12/2071)
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n4:idSjednocenehoVysledku
38289
n4:idVysledku
RIV/68407700:21230/14:00223600
n4:jazykVysledku
n18:eng
n4:klicovaSlova
graphical model; Markov random field; discrete energy minimization; valued constraint satisfaction; linear programming relaxation
n4:klicoveSlovo
n10:graphical%20model n10:valued%20constraint%20satisfaction n10:discrete%20energy%20minimization n10:linear%20programming%20relaxation n10:Markov%20random%20field
n4:kontrolniKodProRIV
[19DBB84DEF2C]
n4:mistoVydani
Boston
n4:nazevZdroje
Advanced Structured Prediction
n4:obor
n9:JD
n4:pocetDomacichTvurcuVysledku
2
n4:pocetStranKnihy
456
n4:pocetTvurcuVysledku
3
n4:projekt
n7:GAP202%2F12%2F2071 n7:7E11036 n7:7E10047
n4:rokUplatneniVysledku
n5:2014
n4:tvurceVysledku
Průša, Daniel Živný, S. Werner, Tomáš
s:numberOfPages
24
n21:hasPublisher
MIT PRESS
n17:isbn
978-0-262-02837-0
n11:organizacniJednotka
21230