Version of DAOOPT with implementations of dynamic heuristics and best-first search.
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ARP
daoopt
mex
minisat
zchaff
CMakeLists.txt
GPL.TXT
LICENSE.TXT
README.md
daoopt_solver.cpp

README.md

DAOOPT: Distributed AND/OR Optimization

An implementation of sequential and distributed AND/OR Branch and Bound for combinatorial optimization problems expressed as MPE (max-product) queries over graphical models like Bayes and Markov networks.

Also implements the following:

  • full context-based caching.
  • mini-buckets for heuristic generation.
  • minfill algorithm to find variable orderings.
  • limited discrepancy search to quickly find initial solution.
  • stochastic local search to quickly find initial solution (via GLS+ code by Frank Hutter).

By Lars Otten, University of California, Irvine. Main AOBB source code under GPL, included libraries vary -- see LICENSE.txt for details

Compilation

A recent set of Boost library headers is required to compile the solver, either in the system-wide include path or copied/symlinked into ./lib/boost locally (confirmed to work is version 1.47.0).

CMake

The easiest and most universal way of compilation is provided through the included CMake files. Create a build subfolder and from within it run cmake ... Afterwards make all starts compilation, while make edit_cache allows to choose between release and debug compiler flags, toggle static linking, and select one of the solver variants (see references below); the default choice is the release-optimized, dynamically linked, sequential solver.

Usage

To see the list of command line parameters, run the solver with the -help argument. Problem input should be in UAI format, a simple text-based format to specify graphical model problems. Gzipped input is supported.

Background / References

AND/OR Branch and Bound

AND/OR Branch and Bound (AOBB) is a search framework developed in Rina Dechter's research group at UC Irvine. Relevant publications:

  • Rina Dechter and Robert Mateescu. "AND/OR Search Spaces for Graphical Models". In Artificial Intelligence, Volume 171 (2-3), pages 73-106, 2006.
  • Radu Marinescu and Rina Dechter. "AND/OR Branch-and-Bound Search for Combinatorial Optimization in Graphical Models." In Artificial Intelligence, Volume 173 (16-17), pages 1457-1491, 2009.
  • Radu Marinescu and Rina Dechter. "Memory Intensive AND/OR Search for Combinatorial Optimization in Graphical Models." In Artificial Intelligence, Volume 173 (16-17), pages 1492-1524, 2009.

Distributed AND/OR Branch and Bound

A recent expansion of the AOBB framework to allow using the parallel resources of a computational grid; it is still the focus of ongoing research. Some relevant publications:

  • Lars Otten and Rina Dechter. "Towards Parallel Search for Optimization in Graphical Models". In Proceedings of ISAIM'10, Fort Lauderdale, FL, USA, January 2010.
  • Lars Otten and Rina Dechter. "Load Balancing for Parallel Branch and Bound". In SofT'10 Workshop and CRAGS'10 Workshop, at CP'10, St. Andrews, Scotland, September 2010.
  • Lars Otten and Rina Dechter. "Learning Subproblem Complexities in Distributed Branch and Bound". In DISCML'11 Workshop, at NIPS'11, Granada, Spain, December 2011.

Acknowledgments

This work has been partially supported by NIH grant 5R01HG004175-03 and NSF grants IIS-0713118 and IIS-1065618.

Disclaimer

This code was written for research purposes and does therefore not strictly adhere to established coding practices and guidelines. View and use at your own risk! ;-)