Optimization Online


New Formulations for Optimization Under Stochastic Dominance Constraints

James Luedtke (jluedtk***at***us.ibm.com)

Abstract: Stochastic dominance constraints allow a decision-maker to manage risk in an optimization setting by requiring their decision to yield a random outcome which stochastically dominates a reference random outcome. We present new integer and linear programming formulations for optimization under first and second-order stochastic dominance constraints, respectively. These formulations are more compact than existing formulations, and relaxing integrality in the first-order formulation yields a second-order formulation, demonstrating the tightness of this formulation. We also present a specialized branching strategy and heuristics which can be used with the new first-order formulation. Computational tests illustrate the potential benefits of the new formulations.

Keywords: Stochastic programming, stochastic dominance constraints, risk, probabilistic constraints, integer programming

Category 1: Stochastic Programming


Download: [PDF]

Entry Submitted: 11/12/2007
Entry Accepted: 11/12/2007
Entry Last Modified: 05/02/2008

Modify/Update this entry

  Visitors Authors More about us Links
  Subscribe, Unsubscribe
Digest Archive
Search, Browse the Repository


Coordinator's Board
Classification Scheme
Give us feedback
Optimization Journals, Sites, Societies
Mathematical Programming Society