Revisiting the upper bounding process in a safe Branch and Bound   algorithm
  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. 
Alexandre Goldsztejn (I3S), Yahia Lebbah (I3S), Claude Michel (I3S),   Michel Rueher (I3S)