Convexification for Mixed Integer Nonlinear Problems
TITLE: Convexification for Mixed Integer Nonlinear Problems
SPEAKER: Dr. Francois Margot (CMU and Axioma,Inc.)
Many industrial problems can be naturally formulated using Mixed Integer Nonlinear Programming (MINLP). Motivated by the demand for Open-Source solvers for real-world MINLP problems, we have developed a spatial Branch-and-Bound software package named couenne (Convex Over- and Under-ENvelopes for Nonlinear Estimation). In this talk, we present the structure of couenne and discuss in detail our work on two of its components: bounds tightening and branching strategies. We then present experimental results on a set of MINLP instances including some industrial applications. We also compare the performance of couenne with a state-of-the-art solver for nonconvex MINLPs.
Joint work with P. Belotti (Lehigh); L. Biegler, G. Cornuejols, I. Grossman (CMU); P. Bonami (LIF, Marseille); J. Lee, A. Waechter (IBM); L. Liberti (LIX, Paris).