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Descent algorithm for nonsmooth stochastic multiobjective optimization

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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Contributor : Jean-Antoine Désidéri Connect in order to contact the contributor
Submitted on : Monday, December 11, 2017 - 1:39:30 PM
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Fabrice Poirion, Quentin Mercier, Jean-Antoine Desideri. Descent algorithm for nonsmooth stochastic multiobjective optimization. Computational Optimization and Applications, Springer Verlag, 2017, 68 (2), pp.317-331. ⟨10.1007/s10589-017-9921-x⟩. ⟨hal-01660788⟩



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