261. On fundamental proof structures in first-order optimization
Invited abstract in session MD-8: Systematic and computer-aided analyses III: noisy gradient methods and fixed-point algorithms, stream Systematic and computer-aided analyses of optimization algorithms.
Monday, 16:30-18:30Room: B100/7007
Authors (first author is the speaker)
| 1. | Aymeric Dieuleveut
|
| CMAP, Ecole Polytechnique | |
| 2. | Baptiste Goujaud
|
| CMAP, Ecole Polytechnique | |
| 3. | Adrien Taylor
|
| Inria/ENS |
Abstract
First-order optimization methods have attracted a lot of attention due to their practical success in many applications, including in machine learning. Obtaining convergence guarantees and worst-case performance certificates for first-order methods have become crucial for understanding ingredients underlying efficient methods and for developing new ones. However, obtaining, verifying, and proving such guarantees is often a tedious task. Therefore, a few approaches were proposed for rendering this task more systematic, and even partially automated. In addition to helping researchers finding convergence proofs, these tools provide insights on the general structures of such proofs. We aim at presenting those structures, showing how to build convergence guarantees for first-order optimization methods.
Keywords
- Computer-aided algorithm analysis
Status: accepted
Back to the list of papers