El Moudden, M. and El Ghali, A. (2018) A new reduced gradient method for solving linearly constrained multiobjective optimization problems. Computational Optimization and Applications, 71 (3). pp. 719-741.

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Abstract

In this paper, we consider the linearly constrained multiobjective minimization, and we propose a new reduced gradient method for solving this problem. Our approach solves iteratively a convex quadratic optimization subproblem to calculate a suitable descent direction for all the objective functions, and then use a bisection algorithm to find an optimal stepsize along this direction. We prove, under natural assumptions, that the proposed algorithm is well-defined and converges globally to Pareto critical points of the problem. Finally, this algorithm is implemented in the MATLAB environment and comparative results of numerical experiments are reported. © 2018, Springer Science+Business Media, LLC, part of Springer Nature.

Item Type:	Article
Uncontrolled Keywords:	Constrained optimization; Gradient methods; Multiobjective optimization; Quadratic programming, Bisection algorithms; Constrained multiobjective optimization; Convex quadratic optimization; Linear constraints; Numerical experiments; Objective functions; Pareto critical point; Reduced gradient method, Problem solving
Subjects:	Mathematics
Divisions:	SCIENTIFIC PRODUCTION > Mathematics
Depositing User:	Administrateur Eprints Administrateur Eprints
Last Modified:	31 Jan 2020 15:48
URI:	http://eprints.umi.ac.ma/id/eprint/3886

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