Relative Interior Rule in Block-Coordinate Descent
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F20%3A00342267" target="_blank" >RIV/68407700:21230/20:00342267 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1109/CVPR42600.2020.00758" target="_blank" >https://doi.org/10.1109/CVPR42600.2020.00758</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/CVPR42600.2020.00758" target="_blank" >10.1109/CVPR42600.2020.00758</a>
Alternative languages
Result language
angličtina
Original language name
Relative Interior Rule in Block-Coordinate Descent
Original language description
It is well-known that for general convex optimization problems, block-coordinate descent can get stuck in poor local optima. Despite that, versions of this method known as convergent message passing are very successful to approximately solve the dual LP relaxation of the MAP inference problem in graphical models. In attempt to identify the reason why these methods often achieve good local minima, we argue that if in block-coordinate descent the set of minimizers over a variable block has multiple elements, one should choose an element from the relative interior of this set. We show that this rule is not worse than any other rule for choosing block-minimizers. Based on this observation, we develop a theoretical framework for block-coordinate descent applied to general convex problems. We illustrate this theory on convergent message-passing methods.
Czech name
—
Czech description
—
Classification
Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
10101 - Pure mathematics
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition
ISBN
978-1-7281-7169-2
ISSN
1063-6919
e-ISSN
2575-7075
Number of pages
9
Pages from-to
7556-7564
Publisher name
IEEE Computer Society
Place of publication
USA
Event location
Seattle
Event date
Jun 13, 2020
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
—