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| Description
| - Inference tasks in Markov random fields (MRFs) are closely related to the constraint satisfaction problem (CSP) and its soft generalizations. In particular, MAP inference in MRF is equivalent to the weighted (max-sum) CSP. A well-known tool to tackle CSPs are arc consistency algorithms, a.k.a. relaxation labeling. A promising approach to MAP inference in MRFs is linear programming relaxation solved by sequential tree-reweighted message passing (TRW-S). There is a not widely known algorithm equivalent to TRW-S, max-sum diffusion, which is slower but very simple. We give two theoretical results. First, we show that arc consistency algorithms and max-sum diffusion become the same thing if formulated in an abstract-algebraic way. Thus, we argue that max-sum arc consistency algorithm or max-sum relaxation labeling is a more suitable name for max-sum diffusion. Second, we give a criterion that strictly decreases during these algorithms.
- Inference tasks in Markov random fields (MRFs) are closely related to the constraint satisfaction problem (CSP) and its soft generalizations. In particular, MAP inference in MRF is equivalent to the weighted (max-sum) CSP. A well-known tool to tackle CSPs are arc consistency algorithms, a.k.a. relaxation labeling. A promising approach to MAP inference in MRFs is linear programming relaxation solved by sequential tree-reweighted message passing (TRW-S). There is a not widely known algorithm equivalent to TRW-S, max-sum diffusion, which is slower but very simple. We give two theoretical results. First, we show that arc consistency algorithms and max-sum diffusion become the same thing if formulated in an abstract-algebraic way. Thus, we argue that max-sum arc consistency algorithm or max-sum relaxation labeling is a more suitable name for max-sum diffusion. Second, we give a criterion that strictly decreases during these algorithms. (en)
- Inference tasks in Markov random fields (MRFs) are closely related to the constraint satisfaction problem (CSP) and its soft generalizations. In particular, MAP inference in MRF is equivalent to the weighted (max-sum) CSP. A well-known tool to tackle CSPs are arc consistency algorithms, a.k.a. relaxation labeling. A promising approach to MAP inference in MRFs is linear programming relaxation solved by sequential tree-reweighted message passing (TRW-S). There is a not widely known algorithm equivalent to TRW-S, max-sum diffusion, which is slower but very simple. We give two theoretical results. First, we show that arc consistency algorithms and max-sum diffusion become the same thing if formulated in an abstract-algebraic way. Thus, we argue that max-sum arc consistency algorithm or max-sum relaxation labeling is a more suitable name for max-sum diffusion. Second, we give a criterion that strictly decreases during these algorithms. (cs)
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| Title
| - What Is Decreased by the Max-sum Arc Consistency Algorithm?
- What Is Decreased by the Max-sum Arc Consistency Algorithm? (en)
- What Is Decreased by the Max-sum Arc Consistency Algorithm? (cs)
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| skos:prefLabel
| - What Is Decreased by the Max-sum Arc Consistency Algorithm?
- What Is Decreased by the Max-sum Arc Consistency Algorithm? (en)
- What Is Decreased by the Max-sum Arc Consistency Algorithm? (cs)
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| skos:notation
| - RIV/68407700:21230/07:03134582!RIV09-MSM-21230___
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| http://linked.open...avai/riv/aktivita
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| http://linked.open...avai/riv/aktivity
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| http://linked.open...vai/riv/dodaniDat
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| http://linked.open...aciTvurceVysledku
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| http://linked.open.../riv/druhVysledku
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| http://linked.open...iv/duvernostUdaju
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| http://linked.open...titaPredkladatele
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| http://linked.open...dnocenehoVysledku
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| http://linked.open...ai/riv/idVysledku
| - RIV/68407700:21230/07:03134582
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| http://linked.open...riv/jazykVysledku
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| http://linked.open.../riv/klicovaSlova
| - Markov random field; arc consistency; constraint satisfaction and optimisation; undirected graphical model (en)
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| http://linked.open.../riv/klicoveSlovo
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| http://linked.open...ontrolniKodProRIV
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| http://linked.open...v/mistoKonaniAkce
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| http://linked.open...i/riv/mistoVydani
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| http://linked.open...i/riv/nazevZdroje
| - ICML 2007: Proceedings of the 24th international conference on Machine learning
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| http://linked.open...in/vavai/riv/obor
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| http://linked.open...ichTvurcuVysledku
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| http://linked.open...cetTvurcuVysledku
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| http://linked.open...UplatneniVysledku
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| http://linked.open...iv/tvurceVysledku
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| http://linked.open...vavai/riv/typAkce
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| http://linked.open.../riv/zahajeniAkce
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| http://linked.open...n/vavai/riv/zamer
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| issn
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| number of pages
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| http://purl.org/ne...btex#hasPublisher
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| https://schema.org/isbn
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| http://localhost/t...ganizacniJednotka
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| is http://linked.open...avai/riv/vysledek
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