Accelerated Diagonal Steepest Descent Method for Unconstrained Multiobjective Optimization


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Publication Details

Output type: Journal article

UM6P affiliated Publication?: Yes

Author list: El Moudden, Mustapha; El Mouatasim, Abdelkrim

Publisher: Springer Verlag (Germany)

Publication year: 2020

Journal: Journal of Optimization Theory and Applications (0022-3239)

Volume number: 188

Issue number: 1

ISSN: 0022-3239

eISSN: 1573-2878

Languages: English (EN-GB)


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Abstract

In this paper, we propose two methods for solving unconstrained multiobjective optimization problems. First, we present a diagonal steepest descent method, in which, at each iteration, a common diagonal matrix is used to approximate the Hessian of every objective function. This method works directly with the objective functions, without using any kind of a priori chosen parameters. It is proved that accumulation points of the sequence generated by the method are Pareto-critical points under standard assumptions. Based on this approach and on the Nesterov step strategy, an improved version of the method is proposed and its convergence rate is analyzed. Finally, computational experiments are presented in order to analyze the performance of the proposed methods.


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Last updated on 2021-11-06 at 23:18