Gonzalo Muñoz
Polytechnique Montreal
Local AA-5491
Gonzalo Muñoz is an Applied Mathematics major from the University of Chile. He obtained his Ph.D. degree in 2017 from the Industrial Engineering and Operations Research Department at Columbia University. He currently holds an IVADO Fellowship at Polytechnique Montréal.
Gonzalo’s research interests fit into the category of Non-Linear Mixed-Integer Optimization, including both theoretical perspectives and implementation of efficient algorithms to address this type of problems. His Ph.D. dissertation focused on efficient LP approximations to sparse polynomial problems, motivated by Power Grid operations problems. He has also worked in optimization methodologies for Open-pit Mine scheduling problems. Gonzalo was the “Best Student Poster” award in the MIP workshops of 2015 and 2017, and the 2016 “Best Student Paper” prize by the Informs Optimization Society. Most recently, he has been studying computational methods for non-convex optimization problems arising in statistics.



Journal papers

G. Muñoz,D. Espinoza,M. Goycoolea,E. Moreno,M. Queyranne,O. R. Letelier, 2017. A Study of the Bienstock–Zuckerberg Algorithm: Applications in Mining and Resource Constrained Project Scheduling. 69, 2, 501-534

Conference papers

C. Matke, D. Bienstock,G.Muñoz S. Yang, D. Kleinhans S. Sager, 2016. Robust Optimization of Power Network Operation: Storage Devices and the Role of Forecast Errors in Renewable Energies. Complex Networks 2016, 693, 809-820

D. Bienstock, G. Munoz, 2015. Approximate Method for AC Transmission Switching Based on a Simple Relaxation for AC-OPF Problems. 2015 IEEE Power & Energy Society General Meetin, 26-30 july 2015, Denver, Colorado, US. DOI: 10.1109/PESGM.2015.7286321

D. Espinoza, M. Goycoolea, E. Moreno, G. Muñoz, M. Queyranne, 2013. Open Pit Mine Scheduling under Uncertainty: a Robust Approach. APCOM 2013, 433-444

Working papers

D. Bienstock, G. Munoz, 2015. LP approximations to mixed-integer polynomial optimization problems


G. Munoz, 2017. Integer Programming Techniques for Polynomial Optimization. Ph.D. Thesis. doi.org/10.7916/D82F812G