Stochastic optimization problems with nonlinear dependence on a probability measure via the Wasserstein metric

Vlasta Kaňková ()
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Vlasta Kaňková: Institute of Information Theory and Automation

Journal of Global Optimization, 2024, vol. 90, issue 3, No 2, 593-617

Abstract: Abstract Nonlinear dependence on a probability measure has recently been encountered with increasing intensity in stochastic optimization. This type of dependence corresponds to many situations in applications; it can appear in problems static (one-stage), dynamic with finite (multi-stage) or infinite horizon, and single- and multi-objective ones. Moreover, the nonlinear dependence can appear not only in the objective functions but also in the constraint sets. In this paper, we will consider static one-objective problems in which the nonlinear dependence appears in the objective function and may also appear in the constraint sets. In detail, we consider “deterministic” constraint sets, whose dependence on the probability measure is nonlinear, constraint sets determined by second-order stochastic dominance, and sets given by mean-risk problems. The last mentioned instance means that the constraint set corresponds to solutions which guarantee acceptable values of both criteria. To obtain relevant assertions, we employ the stability results given by the Wasserstein metric, based on the $$ {{\mathcal {L}}}_{1} $$ L 1 norm. We mainly focus on the case in which a solution has to be obtained on the basis of the data and of investigating a relationship between the original problem and its empirical version.

Keywords: Stochastic optimization problem; Nonlinear dependence; Static problems; Second-order stochastic dominance; Mean-risk model; Wasserstein metric; Stability; Empirical estimates; 90C15; 90C15 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10898-024-01380-6

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