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Aspects of optimization with stochastic dominance

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Abstract

We consider stochastic optimization problems with integral stochastic order constraints. This problem class is characterized by an infinite number of constraints indexed by a function space of increasing concave utility functions. We are interested in effective numerical methods and a Lagrangian duality theory. First, we show how sample average approximation and linear programming can be combined to provide a computational scheme for this problem class. Then, we compute the Lagrangian dual problem to gain more insight into this problem class.

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Correspondence to William B. Haskell.

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Haskell, W.B., Shanthikumar, J.G. & Shen, Z.M. Aspects of optimization with stochastic dominance. Ann Oper Res 253, 247–273 (2017). https://doi.org/10.1007/s10479-016-2299-9

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  • DOI: https://doi.org/10.1007/s10479-016-2299-9

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