Conditional Gradient Method for Double-Convex Fractional Programming Matrix Problems

Abderrahman Bouhamidi (), Mohammed Bellalij, Rentsen Enkhbat (), Khalid Jbilou () and Marcos Raydan ()
Additional contact information
Abderrahman Bouhamidi: Université du Littoral Côte d’Opale
Mohammed Bellalij: Université de Valenciennes
Rentsen Enkhbat: National University of Mongolia
Khalid Jbilou: Université du Littoral Côte d’Opale
Marcos Raydan: Universidad Simón Bolívar

Journal of Optimization Theory and Applications, 2018, vol. 176, issue 1, No 9, 163-177

Abstract: Abstract We consider the problem of optimizing the ratio of two convex functions over a closed and convex set in the space of matrices. This problem appears in several applications and can be classified as a double-convex fractional programming problem. In general, the objective function is nonconvex but, nevertheless, the problem has some special features. Taking advantage of these features, a conditional gradient method is proposed and analyzed, which is suitable for matrix problems. The proposed scheme is applied to two different specific problems, including the well-known trace ratio optimization problem which arises in many engineering and data processing applications. Preliminary numerical experiments are presented to illustrate the properties of the proposed scheme.

Keywords: Fractional programming; Conditional gradient method; Trace ratio problem; 65F10; 15A18; 90C32 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s10957-017-1203-3 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:joptap:v:176:y:2018:i:1:d:10.1007_s10957-017-1203-3

Ordering information: This journal article can be ordered from
http://www.springer. ... cs/journal/10957/PS2

DOI: 10.1007/s10957-017-1203-3

Access Statistics for this article

Journal of Optimization Theory and Applications is currently edited by Franco Giannessi and David G. Hull

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