Coping with anomalies in parallel branch-and-bound algorithms
IEEE Transactions on Computers - The MIT Press scientific computation series
Learning Dominance Relations in Combined Search Problems
IEEE Transactions on Software Engineering
Efficient Branch-and-Bound Algorithms on a Two-Level Memory System
IEEE Transactions on Software Engineering
Computational Efficiency of Parallel Combinatorial OR-Tree Searches
IEEE Transactions on Software Engineering
Search Heuristics for Box Decomposition Methods
Journal of Global Optimization
Evaluation of a Parallel Branch-and-Bound Algorithm on a Class of Multiprocessors
IEEE Transactions on Parallel and Distributed Systems
Efficient parallel branch-and-bound algorithm for constructing minimum ultrametric trees
Journal of Parallel and Distributed Computing
An average-case analysis of branch-and-bound with applications: summary of results
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
Depth-first vs. best-first search: new results
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
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Branch-and-bound algorithms are organized and intelligently structured searches of solutions in a combinatorially large problem space. In this paper, we propose an approximate stochastic model of branch-and-bound algorithms with a best-first search. We have estimated the average memory space required and have predicted the average number of subproblems expanded before the process terminates. Both measures are exponentials of sublinear exponent. In addition, we have also compared the number of subproblems expanded in a best-first search to that expanded in a depth-first search. Depth-first search has been found to have computational complexity comparable to best-first search when the lower-bound function is very accurate or very inaccurate; otherwise, best-fit search is usually better. The results obtained are useful in studying the efficient evaluation of branch-and-bound algorithms in a virtual memory environment. They also confirm that approximations are very effective in reducing the total number of iterations.