Work

STLS: Cycle-Cutset-Driven Local Search For MPE

Year: 2021

Type: conference-paper

Abstract: In this paper we present Stochastic Tree-based Local Search or STLS, a local search algorithm combining the notion of cycle-cutsets with the well-known Belief Propagation to approximatethe optimum of sums of unary and binary potentials. This is done by the previously unexplored concept oftraversal from one cutset to another and updating the induced forest, thus creating a local search algorithm, whose updatephase spans over all the forest variables. We study empirically two pure variants of STLS against the state-of-the art GLS+ scheme and against a hybrid.

Source: Proceedings of the International Symposium on Combinatorial Search

DOI: 10.1609/socs.v5i1.18336

Authors: Alon Milchgrub, Rina Dechter

Institutions: Hebrew University of Jerusalem, 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, G4713059963, RI: Medium: Approximation Algorithms for Probabilistic Graphical Models with Constraints, G6724302884