CMX Lunch Seminar

Leveraging "partial" smoothness for faster convergence in nonsmooth optimization

Damek Davis, Associate Professor, Operations Research and Information Engineering, Cornell University,

Speaker's Bio:
Damek Davis received his Ph.D. in mathematics from the University of California, Los Angeles in 2015. In July 2016 he joined Cornell University's School of Operations Research and Information Engineering as an Assistant Professor. Damek is broadly interested in the mathematics of data science, particularly the interplay of optimization, signal processing, statistics, and machine learning. He serves as an associated editor for Mathematical Programming. He is the recipient of several awards, including the INFORMS Optimization Society Young Researchers Prize in (2019), a Sloan Research Fellowship in Mathematics (2020), and an NSF CAREER Award (2021).

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.

Event Sponsors:

Computing and Mathematical Sciences (CMS) - More events from this Sponsor

For more information, please contact Jolene Brink by phone at (626)395-2813 or by email at [email protected] or visit CMX Website.