2017/06/07 by Jesús Cerquides, Cerquides, Jesús, Rémi Emonet +5
Computer Science · Decision Sciences · #Advanced Vision and Imaging #Artificial Intelligence (cs.AI) #Constraint Satisfaction and Optimization #FOS: Computer and information sciences #Multiagent Systems (cs.MA) #Scheduling and Timetabling Solutions
paper · doi:10.48550/arxiv.1706.02209
openalex publication_date 2017/06/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In the context of solving large distributed constraint optimization problems (DCOP), belief-propagation and approximate inference algorithms are candidates of choice. However, in general, when the factor graph is very loopy (i.e. cyclic), these solution methods suffer from bad performance, due to non-convergence and many exchanged messages. As to improve performances of the Max-Sum inference algorithm when solving loopy constraint optimization problems, we propose here to take inspiration from the belief-propagation-guided dec-imation used to solve sparse random graphs (k-satisfiability). We propose the novel DeciMaxSum method, which is parameterized in terms of policies to decide when to trigger decimation, which variables to decimate, and which values to assign to decimated variables. Based on an empirical evaluation on a classical BP benchmark (the Ising model), some of these combinations of policies exhibit better performance than state-of-the-art competitors.