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Journal Articles Computational Optimization and Applications Year : 2017

Descent algorithm for nonsmooth stochastic multiobjective optimization

Fabrice Poirion
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Quentin Mercier
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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 multi-objective optimization using the properties of a common descent vector defined 10 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.
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Dates and versions

hal-01660788 , version 1 (11-12-2017)

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Fabrice Poirion, Quentin Mercier, Jean-Antoine Desideri. Descent algorithm for nonsmooth stochastic multiobjective optimization. Computational Optimization and Applications, 2017, 68 (2), pp.317-331. ⟨10.1007/s10589-017-9921-x⟩. ⟨hal-01660788⟩
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