Finding feasible points for which the proof succeeds is a critical issue in safe Branch and Bound algorithms which handle continuous problems. In this paper, we introduce a new strategy to compute very accurate approximations of feasible points. This strategy takes advantage of the Newton method for under-constrained systems of equations and inequalities. More precisely, it exploits the optimal solution of a linear relaxation of the problem to compute efficiently a promising upper bound. First experiments on the Coconuts benchmarks demonstrate that this approach is very effective. © 2008 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Goldsztejn, A., Lebbah, Y., Michel, C., & Rueher, M. (2008). Revisiting the upper bounding process in a safe branch and bound algorithm. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5202 LNCS, pp. 598–602). https://doi.org/10.1007/978-3-540-85958-1_49
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