cid 2eb00dcec286423963d67848fe3794f633a13692de7b4b19aaeb4ed452fd52bc · original · via openalex
Title: Improving DPOP with function filtering Authors: Ismel Brito; Pedro Meseguer Venue: Year: 2010 Canonical: doi:10.5555/1838206.1838226 URL: https://doi.org/10.5555/1838206.1838226 Collector: openalex OA: false ABSTRACT DPOP is an algorithm for distributed constraint optimization which has, as main drawback, the exponential size of some of its messages. Recently, some algorithms for distributed cluster tree elimination have been proposed. They also suffer from exponential size messages. However, using the strategy of cost function filtering, in practice these algorithms obtain important reductions in maximum message size and total communication cost. In this paper, we explain the relation between DPOP and these algorithms, and show how cost function filtering can be combined with DPOP. We present experimental evidence of the benefits of this new approach.