Partial optimality of dual decomposition for map inference in pairwise MrFs

Alexander Bauer, Shinichi Nakajima, Nico Görnitz, Klaus Robert Müller

Research output: Contribution to conferencePaperpeer-review

1 Citation (Scopus)

Abstract

Markov random fields (MRFs) are a powerful tool for modelling statistical dependencies for a set of random variables using a graphical representation. An important computational problem related to MRFs, called maximum a posteriori (MAP) inference, is finding a joint variable assignment with the maximal probability. It is well known that the two popular optimisation techniques for this task, linear programming (LP) relaxation and dual decomposition (DD), have a strong connection both providing an optimal solution to the MAP problem when a corresponding LP relaxation is tight. However, less is known about their relationship in the opposite and more realistic case. In this paper, we explain how the fully integral assignments obtained via DD partially agree with the optimal fractional assignments via LP relaxation when the latter is not tight. In particular, for binary pairwise MRFs the corresponding result suggests that both methods share the partial optimality property of their solutions.

Original languageEnglish
Publication statusPublished - 2020
Event22nd International Conference on Artificial Intelligence and Statistics, AISTATS 2019 - Naha, Japan
Duration: 2019 Apr 162019 Apr 18

Conference

Conference22nd International Conference on Artificial Intelligence and Statistics, AISTATS 2019
Country/TerritoryJapan
CityNaha
Period19/4/1619/4/18

Bibliographical note

Funding Information:
This research was supported by the Federal Ministry of Education and Research under the Berlin Big Data Center 2 project (FKz 01IS18025A), and by the World Class University Program through the National Research Foundation of Korea funded by the Ministry of Education, Science, and Technology, under Grant R31-10008.

Publisher Copyright:
© 2019 by the author(s).

ASJC Scopus subject areas

  • Artificial Intelligence
  • Statistics and Probability

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