Estimating the Exponents of Kurdyka-Łojasiewicz (KL) Inequality and Error Bounds for Optimization Models
Guoyin Li (University of New South Wales)
Abstract: The Kurdyka-Łojasiewicz (KL) inequality and error bounds are two fundamental tools for establishing convergence of many numerical methods. In particular, the exponents of the KL inequality and error bounds play an important role in estimating the convergence rate of many contemporary first-order methods. Nevertheless, these exponents are extremely hard to estimate in general, particularly in the case where the associated mappings are not polyhedral. In this talk, we will outline some strategies in estimating or identifying these exponents by exploiting the so-called inf-projection operation and specific structure such as polynomial structure, semi-definite cone program representability and $C^2$-cone reducible structures.
optimization and control
Audience: advanced learners
Comments: The address and password of the zoom room of the seminar are sent by e-mail on the mailinglist of the seminar one day before each talk
One World Optimization seminar
Series comments: Description: Online seminar on optimization and related areas
The address and password of the zoom room of the seminar are sent by e-mail on the mailinglist of the seminar one day before each talk
| Organizers: | Sorin-Mihai Grad*, Radu Ioan Boț, Shoham Sabach, Mathias Staudigl |
| *contact for this listing |