Name
Abstract | ||
|---|---|---|
Stochastic dominance constraints allow a decision maker to manage risk in an optimization setting by requiring his or her 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. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1137/070707956 | SIAM Journal on Optimization |
Keywords | Field | DocType |
new integer,new formulations,stochastic dominance constraints,linear programming formulation,stochastic programming,probabilistic constraints,random outcome,reference random outcome,second-order stochastic dominance constraint,first-order formulation yield,new first-order formulation,decision maker,risk,new formulation,integer programming,second-order formulation,second order,first order,stochastic dominance,linear program | Integer,Stochastic optimization,Mathematical optimization,Stochastic dominance,Heuristics,Integer programming,Linear programming,Stochastic programming,Mathematics,Branching (version control) | Journal |
Volume | Issue | ISSN |
19 | 3 | 1052-6234 |
Citations | PageRank | References |
26 | 1.20 | 15 |
Authors | ||
1 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| James Luedtke | 1 | 439 | 25.95 |