Predicting the size of depth-first branch and bound search trees

Authors:
Levi H. S. Lelis;Lars Otten;Rina Dechter
Affiliations:
Computing Science Dept., University of Alberta, Edmonton, Canada;Dept. of Computer Science, University of California, Irvine, Irvine;Dept. of Computer Science, University of California, Irvine, Irvine
Venue:
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Year:
2013

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Abstract

This paper provides algorithms for predicting the size of the Expanded Search Tree (EST) of Depth-first Branch and Bound algorithms (DFBnB) for optimization tasks. The prediction algorithm is implemented and evaluated in the context of solving combinatorial optimization problems over graphical models such as Bayesian and Markov networks. Our methods extend to DFBnB the approaches provided by Knuth-Chen schemes that were designed and applied for predicting the EST size of backtracking search algorithms. Our empirical results demonstrate good predictions which are superior to competing schemes.