the variable selection can have a dramatic impact on search performance when solving optimization tasks, we introduce in this paper a new dynamic AND/OR Branchand-Bound algorithmic framework which accommodates variable ordering heuristics. The efficiency of the dynamic AND/OR approach is demonstrated

We introduce a new generation of depth-first Branch-and-Bound algorithms that explore the AND/OR search tree using static and dynamic variable orderings for solving general constraint optimization problems. The virtue of the AND/OR representation of the search space is that its size may be far

. In this paper we extend the recently introduced AND/OR Branch-and-Bound algorithm (AOBB) [1] for solving pure 0/1 Integer Linear Programs [2]. Since the variable selection can have a dramatic impact on search performance, we introduce a new dynamic AND/OR Branch-and-Bound algorithm able to accommodate variable

years depth-first AND/OR Branch-and-Bound algorithms were shown to be very effective when exploring such search spaces, especially when using caching. Since best-first strategies are known to be superior to depth-first when memory is utilized, exploring the best-first control strategy is called for

effective support for non-trivial cost models and for physical properties such as sort order. At the same time, it is much more efficient as it combines dynamic programming, which until now had been used only for relational select-project-join optimization, with goal-directed search and branch-and-bound

-first branch-and-bound search through the space of possible children per-mutations, avoiding using a cascade of classifiers or limiting the list of possi-ble ordering outcomes. We report exper-iments in translating English to Japanese and Korean, demonstrating superior per-formance as (a) the number

. In this paper we extend the recently introduced AND/OR Branch-and-Bound algorithm [1] for solving 0/1 Mixed Integer Linear Programming problems. We propose a static version based on pseudo-trees, as well as a dynamic one based on hypergraph separators. Preliminary evaluation on problem instances from MIPLIB2003

This paper describes our entry for the MAP/MPE track of the PASCAL 2011 Probabilistic Inference Challenge, which placed first in all three time limit categories, 20 seconds, 20 minutes, and 1 hour. Our baseline is a branch-and-bound algorithm that explores the context-minimal AND/OR search graph

. In this paper we extend the recently introduced AND/OR Branch-and-Bound algorithm [1] for solving 0/1 Mixed Integer Linear Programming problems. We propose a static version based on pseudo-trees, as well as a dynamic one based on hypergraph separators. Preliminary evaluation on problem instances from MIPLIB2003

that are generated by a single master process. Subproblem generation is itself embedded into an AND/OR Branch and Bound algorithm and dynamically takes previous subproblem solutions into account. Drawing upon the underlying graph structure, we provide some theoretical analysis of the parallelization parameters. A