Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Bucket elimination: a unifying framework for reasoning
Artificial Intelligence
Towards a universal test suite for combinatorial auction algorithms
Proceedings of the 2nd ACM conference on Electronic commerce
A general scheme for automatic generation of search heuristics from specification dependencies
Artificial Intelligence - Special issue on heuristic search in artificial intelligence
sub-SAT: a formulation for relaxed boolean satisfiability with applications in routing
Proceedings of the 2002 international symposium on Physical design
Computational Optimization and Applications
Cliques, Coloring, and Satisfiability: Second DIMACS Implementation Challenge, Workshop, October 11-13, 1993
Nonserial Dynamic Programming
Earth Observation Satellite Management
Constraints
Mini-buckets: A general scheme for bounded inference
Journal of the ACM (JACM)
Using weighted MAX-SAT engines to solve MPE
Eighteenth national conference on Artificial intelligence
Arc consistency for soft constraints
Artificial Intelligence
Solving weighted CSP by maintaining arc consistency
Artificial Intelligence
Optimal Protein Structure Alignment Using Maximum Cliques
Operations Research
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Many important combinatorial optimization problems can be expressed as constraint satisfaction problems with soft constraints. When problems are too difficult to be solved exactly, approximation methods become the best option. Mini-bucket Elimination (MBE) is a well known approximation method for combinatorial optimization problems. It has a control parameter z that allow us to trade time and space for accuracy. In practice, it is the space and not the time that limits the execution with high values of z. In this paper we introduce a new propagation phase that MBE should execute at each bucket. The purpose of this propagation is to jointly process as much information as possible. As a consequence, the undesirable lose of accuracy caused by MBE when splitting functions into different mini-buckets is minimized. We demonstrate our approach in scheduling, combinatorial auction and max-clique problems, where the resulting algorithm MBEp gives important percentage increments of the lower bound (typically 50% and up to 1566%) with only doubling the cpu time.