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CCCP is Frank-Wolfe in Disguise
Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.ORCID iD: 0000-0001-7320-1506
Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.
2022 (English)In: Advances in Neural Information Processing Systems 35 (NeurIPS 2022) / [ed] S. Koyejo; S. Mohamed; A. Agarwal; D. Belgrave; K. Cho; A. Oh, 2022Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
2022.
Series
Advances in neural information processing systems, ISSN 1049-5258
National Category
Computational Mathematics
Identifiers
URN: urn:nbn:se:umu:diva-200701Scopus ID: 2-s2.0-85162842285ISBN: 9781713871088 (print)OAI: oai:DiVA.org:umu-200701DiVA, id: diva2:1707419
Conference
NeurIPS 2022, Thirty-sixth Conference on Neural Information Processing Systems, Hybrid via New Orleans, USA, November 28 - December 9, 2022
Funder
Wallenberg AI, Autonomous Systems and Software Program (WASP)Available from: 2022-10-31 Created: 2022-10-31 Last updated: 2024-07-02Bibliographically approved

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Yurtsever, Alp

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