Statistics and Data Science Seminar Series: Leveraging "partial" smoothness for faster convergence in nonsmooth optimization

Name: Statistics and Data Science Seminar Series: Leveraging "partial" smoothness for faster convergence in nonsmooth optimization
Start: 2022-11-11T11:00:00-06:00
End: 2022-11-11T12:00:00-06:00
Location: Chambers Hall, Ruan Conference Room – lower level

Friday, November 11, 2022 | 11:00 AM - 12:00 PM CT

Chambers Hall, Ruan Conference Room – lower level , 600 Foster St, Evanston, IL 60208 map it

Leveraging "partial" smoothness for faster convergence in nonsmooth optimization

Damek Davis, Associate Professor of Operations Research, Cornell University

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.

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Kisa Kowal (847) 491-3974

k-kowal@northwestern.edu

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Department of Statistics and Data Science

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Academic (general)

Statistics and Data Science Seminar Series: Leveraging "partial" smoothness for faster convergence in nonsmooth optimization

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