Aspects of optimization with stochastic dominance

William B. Haskell (), J. George Shanthikumar () and Z. Max Shen ()
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William B. Haskell: National University of Singapore
J. George Shanthikumar: Purdue University
Z. Max Shen: University of California Berkeley

Annals of Operations Research, 2017, vol. 253, issue 1, No 12, 247-273

Abstract: 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.

Keywords: Stochastic dominance; Convex optimization; Sample average approximation; Duality (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10479-016-2299-9

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