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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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Authors and Affiliations

  1. National University of Singapore, Singapore, Singapore

    William B. Haskell

  2. Purdue University, West Lafayette, IN, USA

    J. George Shanthikumar

  3. University of California Berkeley, Berkeley, CA, USA

    Z. Max Shen

Authors
  1. William B. Haskell
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  2. J. George Shanthikumar
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  3. Z. Max Shen
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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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