Projection free methods on product domains

Immanuel Bomze (), Francesco Rinaldi () and Damiano Zeffiro ()
Additional contact information
Immanuel Bomze: Universität Wien
Francesco Rinaldi: University of Padua
Damiano Zeffiro: University of Padua

Computational Optimization and Applications, 2025, vol. 91, issue 2, No 7, 540 pages

Abstract: Abstract Projection-free block-coordinate methods avoid high computational cost per iteration, and at the same time exploit the particular problem structure of product domains. Frank–Wolfe-like approaches rank among the most popular ones of this type. However, as observed in the literature, there was a gap between the classical Frank–Wolfe theory and the block-coordinate case, with no guarantees of linear convergence rates even for strongly convex objectives in the latter. Moreover, most of previous research concentrated on convex objectives. This study now deals also with the non-convex case and reduces above-mentioned theory gap, in combining a new, fully developed convergence theory with novel active set identification results which ensure that inherent sparsity of solutions can be exploited in an efficient way. Preliminary numerical experiments seem to justify our approach and also show promising results for obtaining global solutions in the non-convex case.

Keywords: Projection free optimization; First order optimization; Block coordinate descent; 90C06; 90C26; 90C30 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10589-024-00585-5 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:coopap:v:91:y:2025:i:2:d:10.1007_s10589-024-00585-5

Ordering information: This journal article can be ordered from
http://www.springer.com/math/journal/10589

DOI: 10.1007/s10589-024-00585-5

Access Statistics for this article

Computational Optimization and Applications is currently edited by William W. Hager

More articles in Computational Optimization and Applications from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().