General branch and bound, and its relation to A* and AO*
Artificial Intelligence
Stochastic Modeling of Branch-and-Bound Algorithms with Best-First Search
IEEE Transactions on Software Engineering - Special issue on COMPSAC 1982 and 1983
Depth-first iterative-deepening: an optimal admissible tree search
Artificial Intelligence
Coping with anomalies in parallel branch-and-bound algorithms
IEEE Transactions on Computers - The MIT Press scientific computation series
Efficient Branch-and-Bound Algorithms on a Two-Level Memory System
IEEE Transactions on Software Engineering
Parallel depth first search. Part II. analysis
International Journal of Parallel Programming
Characterization and Theoretical Comparison of Branch-and-Bound Algorithms for Permutation Problems
Journal of the ACM (JACM)
Algorithms for Scheduling Independent Tasks
Journal of the ACM (JACM)
The Power of Dominance Relations in Branch-and-Bound Algorithms
Journal of the ACM (JACM)
Random Trees and the Analysis of Branch and Bound Procedures
Journal of the ACM (JACM)
Anomalies in parallel branch-and-bound algorithms
Communications of the ACM
The solution for the branching factor of the alpha-beta pruning algorithm and its optimality
Communications of the ACM
Logic for Problem Solving
Superlinear Speedup in Parallel State-Space Search
Superlinear Speedup in Parallel State-Space Search
State of the Art in Parallel Search Techniques for Discrete Optimization Problems
IEEE Transactions on Knowledge and Data Engineering
A near-optimal multicast scheme for mobile ad hoc networks using a hybrid genetic algorithm
Expert Systems with Applications: An International Journal
Partitioning Search Spaces of a Randomized Search
Fundamenta Informaticae - RCRA 2009 Experimental Evaluation of Algorithms for Solving Problems with Combinatorial Explosion
Design of a high speed logic engine for distributed decision support systems
Intelligent Decision Technologies
| Hi-index | 0.01 |
The performance of parallel combinatorial OR-tree searches is analytically evaluated. This performance depends on the complexity of the problem to be solved, the error allowance function, the dominance relation, and the search strategies. The exact performance may be difficult to predict due to the nondeterminism and anomalies of parallelism. The authors derive the performance bounds of parallel OR-tree searches with respect to the best-first, depth-first, and breadth-first strategies, and verify these bounds by simulation. They show that a near-linear speedup can be achieved with respect to a large number of processors for parallel OR-tree searches. Using the bounds developed, the authors derive sufficient conditions for assuring that parallelism will not degrade performance and necessary conditions for allowing parallelism to have a speedup greater than the ratio of the numbers of processors. These bounds and conditions provide the theoretical foundation for determining the number of processors required to assure a near-linear speedup.