Optimization Under First Order Stochastic Dominance Constraints
We consider stochastic optimization problems involving stochastic dominance constraints of first order, also called stochastic ordering constraints. They are equivalent to a continuum of probabilistic constraints or chance constraints. We develop first order necessary and sufficient conditions of optimality for these models. We show that the Lagrange multipliers corresponding to stochastic dominance constraints are piecewise constant nondecreasing utility functions. These results extend our theory of stochastic dominance-constrained optimization to the first order case, in which the main challenge is the potential non- convexity of the problem. We also show that the convexification of stochastic ordering relation is equivalent to second order stochastic dominance under rather weak assumptions. This paper appeared as "Semi-Infinite Probabilistic Optimization: First Order Stochastic Dominance Constraints" in "Optimization" 53(2004) 583-- 601.
|Date of creation:||05 Mar 2004|
|Date of revision:||07 Aug 2005|
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- Ronald E. Gangnon & William N. King, 2002. "Minimum distance estimation of the distribution functions of stochastically ordered random variables," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 51(4), pages 485-492.
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