Abstract
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.
An extented version of this paper is available at: http://www.i3s.unice.fr/%7Emh/RR/2008/RR-08.11-A.GOLDSZTEJN.pdf
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Goldsztejn, A., Lebbah, Y., Michel, C., Rueher, M. (2008). Revisiting the Upper Bounding Process in a Safe Branch and Bound Algorithm. In: Stuckey, P.J. (eds) Principles and Practice of Constraint Programming. CP 2008. Lecture Notes in Computer Science, vol 5202. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85958-1_49
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DOI: https://doi.org/10.1007/978-3-540-85958-1_49
Publisher Name: Springer, Berlin, Heidelberg
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