| Paper ID | SPTM-21.1 |
| Paper Title |
ACCELERATING FRANK-WOLFE WITH WEIGHTED AVERAGE GRADIENTS |
| Authors |
Yilang Zhang, Bingcong Li, Georgios B. Giannakis, University of Minnesota, United States |
| Session | SPTM-21: Optimization Methods for Signal Processing |
| Location | Gather.Town |
| Session Time: | Friday, 11 June, 13:00 - 13:45 |
| Presentation Time: | Friday, 11 June, 13:00 - 13:45 |
| Presentation |
Poster
|
| Topic |
Signal Processing Theory and Methods: [OPT] Optimization Methods for Signal Processing |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
Relying on a conditional gradient based iteration, the Frank-Wolfe (FW) algorithm has been a popular solver of constrained convex optimization problems in signal processing and machine learning, thanks to its low complexity. The present contribution broadens its scope by replacing the gradient per FW subproblem with a weighted average of gradients. This generalization speeds up the convergence of FW by alleviating its zigzag behavior. A geometric interpretation for the averaged gradients is provided, and convergence guarantees are established for three different weight combinations. Numerical comparison shows the effectiveness of the proposed methods. |
