2007
NeurIPS
NeurIPS 2007
CPR for CSPs: A Probabilistic Relaxation of Constraint Propagation
Abstract
This paper proposes constraint propagation relaxation (CPR), a probabilistic approach to classical constraint propagation that provides another view on the whole parametric family of survey propagation algorithms SP(ρ), ranging from belief propagation (ρ = 0) to (pure) survey propagation(ρ = 1). More importantly, the approach elucidates the implicit, but fundamental assumptions underlying SP(ρ), thus shedding some light on its effectiveness and leading to applications beyond k-SAT.
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- Automated Reasoning
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Trend Setter
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Keyword Pioneer
- constraint satisfaction problem
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Hot Topic Early Bird
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Authors
Topics
Artificial Intelligence > Core AI > Causal Inference
Artificial Intelligence > Bayesian & Probabilistic > Probabilistic Modeling
Machine Learning > Optimization & Theory > Optimization
Machine Learning > Bayesian & Probabilistic > Probabilistic Modeling
Machine Learning > Core Methods > Structured Prediction
Artificial Intelligence > Core AI > Automated Reasoning