Work

Beyond Static Mini-Bucket: Towards Integrating with Iterative Cost-Shifting Based Dynamic Heuristics

Year: 2021

Type: conference-paper

Abstract: We explore the use of iterative cost-shifting as a dynamic heuristic generator for solving MPE in graphical models via Branch and Bound. When mini-bucket elimination is limited by its memory budget, it may not provide good heuristics. This can happen often when the graphical model has a very high induced width with large variable domain sizes. In addition, we explore a hybrid setup where both MBE and the iterative cost-shifting bound are used in a combined heuristic. We compare these approaches with the most advanced statically generated heuristics.

Source: Proceedings of the International Symposium on Combinatorial Search

DOI: 10.1609/socs.v5i1.18316

Authors: William Lam, Kalev Kask, Rina Dechter, Alexander Ihler

Institution: University of California, Irvine

Language: English

Topic: Constraint Satisfaction and Optimization

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

Funders: National Science Foundation, Defense Advanced Research Projects Agency, U.S. Air Force

Awards: G2077352727, CAREER: Estimation and Decisions in Graphical Models, G4713059963, RI: Medium: Approximation Algorithms for Probabilistic Graphical Models with Constraints, G6724302884