Computational Optimization and Applications
Probabilistic subproblem selection in branch-and-bound algorithms
Journal of Computational and Applied Mathematics
Multi-voltage floorplan design with optimal voltage assignment
Proceedings of the 2009 international symposium on Physical design
Probabilistic subproblem selection in branch-and-bound algorithms
Journal of Computational and Applied Mathematics
An optimal algorithm for layer assignment of bus escape routing on PCBs
Proceedings of the 48th Design Automation Conference
A simple and faster branch-and-bound algorithm for finding a maximum clique
WALCOM'10 Proceedings of the 4th international conference on Algorithms and Computation
All roads lead to Rome---New search methods for the optimal triangulation problem
International Journal of Approximate Reasoning
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We introduce a very simple but efficient idea for branch and bound ({\cal B}& {\cal B}) algorithms in global optimization (GO). As input for our generic algorithm, we need an upper bound algorithm for the GO maximization problem and a branching rule. The latter reduces the problem into several smaller subproblems of the same type. The new {{\cal B}& {\cal B} approach delivers one global optimizer or, if stopped before finished, improved upper and lower bounds for the problem. Its main difference to commonly used {{\cal B}& {\cal B} techniques is its ability to approximate the problem from above and from below while traversing the problem tree. It needs no supplementary information about the system optimized and does not consume more time than classical {{\cal B}& {\cal B} techniques. Experimental results with the maximum clique problem illustrate the benefit of this new method.