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In this talk we present two exact approaches that exploit in very different ways the separability of mixed integer nonlinear programming (MINLP) problems. On one side, a global optimization method for box-constrained polynomial optimization is proposed. The main idea is to identify separable under-estimators to obtain quickly good quality lower bounds employed in a branch-and-bound scheme. On the other side, a global optimization method for MINLP problems with separable non-convexities is proposed. Convex (and linear) lower bounds are proposed and employed in an iterative method that is proven to converge to (epsilon) optimality. Computational results are provided so as to show the practical efficiency of the two algorithms with respect to the classic resolution methods.

Also part of the Optimization Seminar Series