Title:

Convexification and optimization of problems involving the Euclidean norm.

Abstract:

The field of mixed-integer nonlinear optimization has advanced significantly over the past three decades. However, even small instances of many nonconvex optimization problems involving the Euclidean norm are beyond the capabilities of existing algorithms. These problems stem from applications such as molecular energy minimization, object packing, and facility location, and often share specific features that make them challenging. In this presentation, we identify these features, introduce new algorithms to address them, and present numerical results that demonstrate the impact of our techniques. Furthermore, we identify several related open problems and opportunities for analytical and computational advances. This is joint work with Anatoliy Kuznetsov.

Bio:

Nick Sahinidis is Butler Family Chair and Professor of Industrial & Systems Engineering and Chemical & Biomolecular Engineering at the Georgia Institute of Technology. Dr. Sahinidis previously taught at the University of Illinois at Urbana-Champaign (1991-2007) and Carnegie Mellon University (2007-2020). He has pioneered algorithms and developed widely used software for optimization and machine learning. He received the INFORMS Computing Society Prize in 2004, the Beale-Orchard-Hays Prize from the Mathematical Programming Society in 2006, the Computing in Chemical Engineering Award in 2010, the Constantin Carathéodory Prize in 2015, and the National Award and Gold Medal from the Hellenic Operational Research Society in 2016. He is a member of the US National Academy of Engineering, a fellow of INFORMS, a fellow of AIChE, a fellow of the Asia-Pacific Artificial Intelligence Association, and past Editor-in-Chief of Optimization and Engineering.