Saurabh Paul, Christos Boutsidis, et al.
JMLR
The paper presents and evaluates the power of limited memory best-first search over AND/OR spaces for optimization tasks in graphical models. We propose Recursive Best-First AND/OR Search with Overestimation (RBFAOO), a new algorithm that explores the search space in a best-first manner while operating with restricted memory. We enhance RBFAOO with a simple overestimation technique aimed at minimizing the overhead associated with re-expanding internal nodes and prove correctness and completeness of RBFAOO. Our experiments show that RBFAOO is often superior to the current stateof- the-art approaches based on AND/OR search, especially on very hard problem instances.
Saurabh Paul, Christos Boutsidis, et al.
JMLR
C.A. Micchelli, W.L. Miranker
Journal of the ACM
Joxan Jaffar
Journal of the ACM
Kenneth L. Clarkson, Elad Hazan, et al.
Journal of the ACM