Leveraging "partial" smoothness for faster convergence in nonsmooth optimization

Wednesday, November 9, 2022 - 3:00pm

Event Calendar Category

Other LIDS Events

Speaker Name

Damek Davis

Affiliation

Cornell University

Join Zoom meeting

https://mit.zoom.us/j/93673116988

Abstract

First-order methods in nonsmooth optimization are often described as "slow." I will present two (locally) accelerated first-order methods that violate this perception: a superlinearly convergent method for solving nonsmooth equations, and a linearly convergent method for solving "generic" nonsmooth optimization problems. The key insight in both cases is that nonsmooth functions are often "partially" smooth in useful ways.

Biography

Damek Davis is an Associate Professor of Operations Research at Cornell University. His research focuses on the interplay of optimization, signal processing, statistics, and machine learning. He has received several awards for his work, including a Sloan Research Fellowship in Mathematics (2020), the INFORMS Optimization Society Young Researchers Prize (2019), and an NSF CAREER Award (2021).