Nonlinear Optimization in the Presence of Noise

Nonlinear Optimization in the Presence of Noise (via Zoom)

Abstract: We begin by presenting three case studies that illustrate the nature of noisy optimization problems arising in practice. They originate in atmospheric sciences, machine learning, and engineering design. We wish to understand the source of the noise (e.g. a lower fidelity model, sampling or reduced precision arithmetic), its properties, and how to estimate it. This sets the stage for the presentation of our goal of redesigning constrained and unconstrained nonlinear optimization methods to achieve noise tolerance.

Bio: Jorge Nocedal is the Walter P. Murphy Professor in the Department of Industrial Engineering and Management Sciences at Northwestern University. He studied at UNAM (Mexico) and Rice University. His research is in optimization, both deterministic and stochastic, with emphasis on very large-scale problems. He is a SIAM Fellow, was awarded the 2012 George B. Dantzig Prize and the 2017 Von Neumann Theory Prize, for contributions to theory and algorithms of nonlinear optimization. He is a member of the US National Academy of Engineering