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Descent algorithm for nonsmooth stochastic multiobjective optimization

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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 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.

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Authors and Affiliations

  1. ONERA The French Aerospace Lab, Palaiseau, France

    Fabrice Poirion & Quentin Mercier

  2. INRIA, Nice, France

    Jean-Antoine Désidéri

Authors
  1. Fabrice Poirion
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  2. Quentin Mercier
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  3. Jean-Antoine Désidéri
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Correspondence to Fabrice Poirion.

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Poirion, F., Mercier, Q. & Désidéri, JA. Descent algorithm for nonsmooth stochastic multiobjective optimization. Comput Optim Appl 68, 317–331 (2017). https://doi.org/10.1007/s10589-017-9921-x

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  • DOI: https://doi.org/10.1007/s10589-017-9921-x

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