NeurIPS 2022accept15 citations
CCCP is Frank-Wolfe in disguise
Abstract
This paper uncovers a simple but rather surprising connection: it shows that the well-known convex-concave procedure (CCCP) and its generalization to constrained problems are both special cases of the Frank-Wolfe (FW) method. This connection not only provides insight of deep (in our opinion) pedagogical value, but also transfers the recently discovered convergence theory of nonconvex Frank-Wolfe methods immediately to CCCP, closing a long-standing gap in its non-asymptotic convergence theory. We hope the viewpoint uncovered by this paper spurs the transfer of other advances made for FW to both CCCP and its generalizations.
convex-concave procedurecccpfrank-wolfeconditional gradient methoddifference of convex programmingexpectation maximizationsinkhorn
BibTeX
@inproceedings{
yurtsever2022cccp,
title={{CCCP} is Frank-Wolfe in disguise},
author={Alp Yurtsever and Suvrit Sra},
booktitle={Advances in Neural Information Processing Systems},
editor={Alice H. Oh and Alekh Agarwal and Danielle Belgrave and Kyunghyun Cho},
year={2022},
url={https://openreview.net/forum?id=OGGQs4xFHrr}
}