2018-06-29

Title: Network Models for Multi-Objective Discrete Optimization

Speaker: Carlos Cardonha

Abstract: This work provides a novel framework for solving multi-objective discrete optimization problems with an arbitrary number of objectives. Our framework formulates these problems as network models, in that enumerating the Pareto frontier amounts to solving a multi-criteria shortest path problem in an auxiliary network. We design tools and techniques for exploiting the network model in order to accelerate the identification of the Pareto frontier, most notably a number of operations to simplify the network by removing nodes and arcs while preserving the set of nondominated solutions. We show that the proposed framework yields orders-of magnitude performance improvements over existing state-of-the-art algorithms on four problem classes containing both linear and nonlinear objective functions.
This is a joint work with David Bergman, Merve Bodur, and André Ciré.

Mini-bio: Carlos Cardonha is a Research Staff Member of the Optimization under Uncertainty Group at IBM Research Brazil, with a Ph.D. in Mathematics (T.U. Berlin) and with a Bachelor’s and a Master’s degree in Computer Science (Universidade de São Paulo). His primary research interests are mathematical programming and theoretical computer science, with focus on the application of techniques in mixed integer linear programming, combinatorial optimization, and algorithms design and analysis to real-world and/or operations research problems.