ORIE Colloquium: Gonzalo Muñoz (Universidad de Chile)

Separating hyperplanes for non-convex quadratically-constrained optimization

In 1971, Balas introduced the intersection cut framework as a method for generating separating hyperplanes (or “cuts”) in integer optimization. These cuts are derived from convex S-free sets, and inclusion-wise, maximal S-free sets yield the strongest intersection cuts. When S is a lattice, maximal S-free sets are well-studied from theoretical and computational standpoints. In this talk, we focus on the case when a non-convex quadratic inequality defines S and show how to construct basic maximal quadratic-free sets. Additionally, we explore how to generalize the basic procedure to construct a plethora of new maximal quadratic-free sets for homogeneous quadratics.

This is joint work with Antonia Chmiela, Joseph Paat, and Felipe Serrano.

Bio:
Gonzalo Muñoz is an assistant professor at the Industrial Engineering Department of Universidad de Chile and a researcher at the Institute of Complex Systems in Engineering, Chile. He obtained his Ph.D. from the Department of Industrial Engineering and Operations Research at Columbia University. His main interests include theoretical and computational developments in mixed integer linear and non-linear optimization. His work has received recognition from the optimization community, such as the 2023 INFORMS Optimization Society Young Researcher Prize.

Become a Fellow

Join the Cornell Institute for Digital Agriculture and become a participating member in advancing research, thought, policy and practice to advance the field of digital agriculture and help build stronger, more resilient agri-food systems.

Stay up to Date

Receive our newsletter for announcements of events, opportunities, digital ag news, Cornell news, and more.

CIDA - Cornell Institute for Digital Agriculture

If you have a disability and are having trouble accessing information on this website or need materials in an alternate format, contact [email protected] for assistance.

FOLLOW US


CIDA Copyright 2023 | CIDA is an equal opportunity employer | Terms of Use | Privacy Policy