Algorithms for the solution of stochastic dynamic minimax problems

In this paper, we present algorithms for the solution of the dynamic minimax problem in stochastic programs. This dynamic minimax approach is suggested for the analysis of multi-stage stochastic decision problems when there is only partial knowledge on the joint probability distribution of the random data. The algorithms proposed in this paper are based on projected sub-gradient and bundle methods.RésuméDans cet article, nous proposons des algorithmes pour la solution du problème du minimax dynamique stochastique. Ce problème se présente par exemple lorsque, dans un problème de décision dynamique stochastique, l'information disponible au sujet des distributions de probabilité des paramètres est incomplète. Les algorithmes proposés sont fondés sur la méthode de sous-gradient projeté et la méthode des faisceaux.

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