Tree decomposition with function filtering

Abstract

Besides search, complete inference methods can also be used to solve soft constraint; problems. Their main drawback is the high spatial complexity. To improve its practical usage, we present an approach to decrease memory consumtion in tree decomposition methods, a class of complete inference algorithms. This approach, called function filtering, allows to detect and remove some tuples that appear to be consistent (with a cost below the upper bound) but that will become inconsistent (with a cost exceeding the upper bound) when extended to other variables. Using this idea, we have developed new algorithms CTEf, MCTEf and IMCTEf, standing for cluster, mini-cluster and iterative mini-cluster tree elimination with function filtering. We demonstrate empirically the benefits of our approach. © Springer-Verlag Berlin Heidelberg 2005.