Revisiting the Upper Bounding Process in a Safe Branch and Bound Algorithm

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.

• Type: Conference
• Year: 2008
• Where: CP
• Authors: Alexandre Goldsztejn, Yahia Lebbah, Claude Michel, Michel Rueher
• Keywords: Artificial Intelligence, CP 2008, Feasible Points, Promising Upper Bound, Strategy Takes Advantage