Many important, combinatorial optimization problems can be expressed as constraint, satisfaction problems with soft constraints. When problems are too difficult to be solved exactly, approximation methods become the best; option. Mini-bucket elimination (MBE) is a well known approximation method for combinatorial optimization problems. It has a control parameter z that allow us to trade time and space for accuracy. In practice it; is the space and not the time that limits the execution with high values of z. In this paper we introduce a set of improvements on the way MBE handles memory. The resulting algorithm dfMBE may be orders of magnitude more efficient. As a consequence, higher values of z can be used which, in turn, yields significantly better bounds. We demonstrate our approach in scheduling, probabilistic reasoning and resource allocation problems. © Springer-Verlag Berlin Heidelberg 2005.
CITATION STYLE
Rollon, E., & Larrosa, J. (2005). Depth-first mini-bucket elimination. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3709 LNCS, pp. 563–577). https://doi.org/10.1007/11564751_42
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