On a Frank-Wolfe Approach for Abs-smooth Optimization ongoing
Motivated by nonsmooth problems in machine learning, we solve the problem of minimizing an abs-smooth function subject to closed convex constraints. New theory and algorithms are developed using linear minimization oracles to enforce constraints and abs-linearization methods to handle nonsmoothness.
🧑🎓 Project Members
🪙 Funding
This project is being funded by the Berlin Mathematics Research Center MATH+ (project ID EF1-23), itself funded by the German Research Foundation (DFG) under Germany's Excellence Strategy (EXC-2046/1, project ID 390685689) from April 2023 to March 2026.
📝 Publications and preprints
- Kreimeier, T., Pokutta, S., Walther, A., and Woodstock, Z. (2023). On a Frank-Wolfe Approach for Abs-smooth Functions. Optimization Methods and Software.
[arXiv]
[BibTeX]
@article{abssmooth-fw_2023, year = {2023}, journal = {Optimization Methods and Software}, archiveprefix = {arXiv}, eprint = {2303.09881}, primaryclass = {math.OC}, author = {Kreimeier, Timo and Pokutta, Sebastian and Walther, Andrea and Woodstock, Zev}, title = {On a Frank-Wolfe Approach for Abs-smooth Functions} }
- Woodstock, Z., and Pokutta, S. (2023). Splitting the Conditional Gradient Algorithm.
[arXiv]
[BibTeX]
@misc{splitcg2023, archiveprefix = {arXiv}, eprint = {2311.05381}, primaryclass = {math.OC}, year = {2023}, author = {Woodstock, Zev and Pokutta, Sebastian}, title = {Splitting the Conditional Gradient Algorithm} }