Corpus ID: 119664780

# Restarting Frank-Wolfe

@inproceedings{Kerdreux2018RestartingF,
title={Restarting Frank-Wolfe},
author={T. Kerdreux and A. d'Aspremont and Sebastian Pokutta},
booktitle={AISTATS},
year={2018}
}
• Published in AISTATS 2018
• Computer Science, Mathematics, Philosophy
• Conditional Gradients (aka Frank-Wolfe algorithms) form a classical set of methods for constrained smooth convex minimization due to their simplicity, the absence of projection step, and competitive numerical performance. While the vanilla Frank-Wolfe algorithm only ensures a worst-case rate of $O(1/\epsilon)$, various recent results have shown that for strongly convex functions, the method can be slightly modified to achieve linear convergence. However, this still leaves a huge gap between… CONTINUE READING

#### Explore Further: Topics Discussed in This Paper

Locally Accelerated Conditional Gradients
• Mathematics, Computer Science
• 2020
3
Primal-Dual Block Frank-Wolfe
• Computer Science, Mathematics
• 2019
Sharpness, Restart and Acceleration
• Computer Science, Mathematics
• 2017
32
Second-order Conditional Gradients
• Mathematics, Computer Science
• 2020
1
Blended Matching Pursuit
• Computer Science, Mathematics
• 2019