English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Paper

The Lazy Flipper: MAP Inference in Higher-Order Graphical Models by Depth-limited Exhaustive Search

MPS-Authors
There are no MPG-Authors available
External Resource
Fulltext (public)

arXiv:1009.4102.pdf
(Preprint), 718KB

Supplementary Material (public)
There is no public supplementary material available
Citation

Andres, B., Kappes, J. H., Koethe, U., & Hamprecht, F. A. (2010). The Lazy Flipper: MAP Inference in Higher-Order Graphical Models by Depth-limited Exhaustive Search. Retrieved from http://arxiv.org/abs/1009.4102.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0017-96DD-F
Abstract
This article presents a new search algorithm for the NP-hard problem of optimizing functions of binary variables that decompose according to a graphical model. It can be applied to models of any order and structure. The main novelty is a technique to constrain the search space based on the topology of the model. When pursued to the full search depth, the algorithm is guaranteed to converge to a global optimum, passing through a series of monotonously improving local optima that are guaranteed to be optimal within a given and increasing Hamming distance. For a search depth of 1, it specializes to Iterated Conditional Modes. Between these extremes, a useful tradeoff between approximation quality and runtime is established. Experiments on models derived from both illustrative and real problems show that approximations found with limited search depth match or improve those obtained by state-of-the-art methods based on message passing and linear programming.