WorldCat Identities

Benterki, Djamel (1963-....).

Overview
Works: 5 works in 7 publications in 2 languages and 9 library holdings
Roles: Other, Thesis advisor, Contributor
Publication Timeline
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Most widely held works by Djamel Benterki
An efficient parameterized logarithmic kernel function for linear optimization by Mousaab Bouafia( )

1 edition published in 2017 in English and held by 2 WorldCat member libraries worldwide

Étude asymptotique des méthodes de points intérieurs pour la programmation linéaire by Mousaab Bouafia( Book )

2 editions published in 2016 in French and held by 2 WorldCat member libraries worldwide

Dans cette recherche, on s'intéresse à l'étude asymptotique des méthodes de points intérieurs pour la programmation linéaire. En se basant sur les travaux de Schrijver et Padberg, nous proposons deux nouveaux pas de déplacement pour accélérer la convergence de l'algorithme de Karmarkar et réduire sa complexité algorithmique. Le premier pas est une amélioration modérée du comportement de l'algorithme, le deuxième représente le meilleur pas de déplacement fixe obtenu jusqu'à présent. Ensuite nous proposons deux approches paramétrées de la l'algorithme de trajectoire centrale basé sur les fonctions noyau. La première fonction généralise la fonction noyau proposé par Y.Q. Bai et al., la deuxième est la première fonction noyau trigonométrique qui donne la meilleure complexité algorithmique, obtenue jusqu'à présent. Ces propositions ont apporté des nouvelles contributions d'ordre algorithmique, théorique et numérique
An Efficient Primal-Dual Interior Point Method for Linear Programming Problems Based on a New Kernel Function with a Trigonometric Barrier Term by Mousaab Bouafia( )

1 edition published in 2016 in English and held by 2 WorldCat member libraries worldwide

Résolution d'un problème quadratique non convexe avec contraintes mixtes par les techniques de l'optimisation D.C. by Mira Al Kharboutly( )

2 editions published in 2018 in French and held by 2 WorldCat member libraries worldwide

Our objective in this work is to solve a binary quadratic problem under mixed constraints by the techniques of DC optimization. As DC optimization has proved its efficiency to solve large-scale problems in different domains, we decided to apply this optimization approach to solve this problem. The most important part of D.C. optimization is the choice of an adequate decomposition that facilitates determination and speeds convergence of two constructed suites where the first converges to the optimal solution of the primal problem and the second converges to the optimal solution of the dual problem. In this work, we propose two efficient decompositions and simple to manipulate. The application of the DC Algorithm (DCA) leads us to solve at each iteration a convex quadratic problem with mixed, linear and quadratic constraints. For it, we must find an efficient and fast method to solve this last problem at each iteration. To do this, we apply three different methods: the Newton method, the semidefinite programing and interior point method. We present the comparative numerical results on the same benchmarks of these three approaches to justify our choice of the fastest method to effectively solve this problem
Méthodes de points intérieurs et leurs applications sur des problèmes d'optimisation semi-définis by Amina Zerari( )

1 edition published in 2020 in French and held by 1 WorldCat member library worldwide

Interior point methods are well known as the most efficient to solve optimization problems. These methods have a polynomial convergence and good behavior. In this research, we are interested in a theoretical, numerical and an algorithmic study of interior-point methods for semidefinite programming.Indeed, we present in a first part, a primal-dual projective interior point algorithm of polynomial type with two phases, where we introduced three new effective alternatives for computing the displacement step.Then, in the second part, we are interested in a primal-dual central trajectory method via a kernel function, we proposed two new kernel functions with a logarithmic term that give the best-known complexity results
 
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Audience level: 0.94 (from 0.92 for Résolutio ... to 0.97 for An Efficie ...)

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