|
|
 |
|
Mercredi 3 Octobre
| Heure: |
14:00 - 15:00 |
| Lieu: |
Salle B107, bâtiment B, Université de Villetaneuse |
| Résumé: |
First Order Algorithms for Constrained Optimization Problems in Machine Learning |
| Description: |
Francesco Rinaldi Thanks to the advent of the "Big Data era", simple iterative first-order optimization approaches for constrained convex optimization have re-gained popularity in the last few years. In the talk, we first review a few classic methods (i.e., conditional and projected gradient method) in the context of Big Data applications. Then, we discuss both theoretical and computational aspects of some new active-set variants for those classic methods. Finally, we examine current challenges and future research perspectives.
DISCLAIMER: This aimes to be a wide audience talk (for any LIPN member, Ph. D. students included) and you are not assume to know what is a "first-order optimization approach", a "conditional or projected gradient method" or an "active-set variant". |
|
|
