El Ghali, A. and El Moudden, M. (2016) An implementation of a reduced subgradient method via Luenberger-Mokhtar variant. Optimization, 65 (7). pp. 1497-1518.

Full text not available from this repository.
Official URL: https://www.scopus.com/inward/record.uri?eid=2-s2....

Abstract

We present an implementable algorithm for minimizing a convex function which is not necessarily differentiable subject to linear equality constraints and to nonnegativity bounds on the variables. The algorithm is based on extending the variant proposed by Luenberger to the nondifferentiable case and using the bundle techniques introduced by Lemaréchal to approximate the subdifferential of the objective function. In particular, at each iteration, we compute a search direction by solving a quadratic subproblem, and an inexact line search along this direction yields a decrease in the objective value. Under some assumptions, the convergence of the proposed algorithm is analysed. Finally, some numerical results are presented, which show that the algorithm performs efficiently. © 2016 Informa UK Limited, trading as Taylor & Francis Group.

Item Type: Article
Subjects: Decision Sciences
Divisions: SCIENTIFIC PRODUCTION > Decision Sciences
Depositing User: Administrateur Eprints Administrateur Eprints
Last Modified: 31 Jan 2020 15:46
URI: http://eprints.umi.ac.ma/id/eprint/2820

Actions (login required)

View Item View Item