622. Constrained Optimization via Frank-Wolfe Algorithms
Invited abstract in session MA-1: Plenary 1, stream Plenaries.
Monday, 8:50-10:00Room: B100/1001
Authors (first author is the speaker)
| 1. | Sebastian Pokutta
|
| Zuse Institute Berlin |
Abstract
This talk focuses on constrained optimization problems that can be efficiently solved using first-order methods, particularly Frank-Wolfe methods (also known as Conditional Gradients). These algorithms have emerged as a crucial class of methods for minimizing smooth convex functions over polytopes, and their applicability extends beyond this domain. Recently, they have garnered significant attention due to their ability to facilitate structured optimization, a key aspect in some machine learning applications. I will provide a broad overview of these methods, highlighting their applications and presenting recent advances in both traditional optimization and machine learning. Time permitting, I will also discuss an extension of these methods to the mixed-integer convex optimization setting.
Keywords
- Computational mathematical optimization
Status: accepted
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