Descent algorithm for nonsmooth stochastic multiobjective optimization

Fabrice Poirion (), Quentin Mercier () and Jean-Antoine Désidéri ()
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Fabrice Poirion: ONERA The French Aerospace Lab
Quentin Mercier: ONERA The French Aerospace Lab
Jean-Antoine Désidéri: INRIA

Computational Optimization and Applications, 2017, vol. 68, issue 2, No 5, 317-331

Abstract: Abstract An algorithm for solving the expectation formulation of stochastic nonsmooth multiobjective optimization problems is proposed. The proposed method is an extension of the classical stochastic gradient algorithm to multiobjective optimization using the properties of a common descent vector defined in the deterministic context. The mean square and the almost sure convergence of the algorithm are proven. The algorithm efficiency is illustrated and assessed on an academic example.

Keywords: Multiobjective optimization; Stochastic; Nonsmooth; Almost sure convergence (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/s10589-017-9921-x

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