Name
Title | ||
|---|---|---|
A completely positive representation of 0-1 linear programs with joint probabilistic constraints |
Abstract | ||
|---|---|---|
In this paper, we study 0-1 linear programs with joint probabilistic constraints. The constraint matrix vector rows are assumed to be independent, and the coefficients to be normally distributed. Our main results show that this non-convex problem can be approximated by a convex completely positive problem. Moreover, we show that the optimal values of the latter converge to the optimal values of the original problem. Examples randomly generated highlight the efficiency of our approach. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1016/j.orl.2013.08.008 | Operations Research Letters |
Keywords | Field | DocType |
semidefinite programming,stochastic programming | Row,Combinatorics,Mathematical optimization,Regular polygon,Probabilistic logic,Stochastic programming,Mathematics,Constraint matrix,Semidefinite programming | Journal |
Volume | Issue | ISSN |
41 | 6 | 0167-6377 |
Citations | PageRank | References |
2 | 0.45 | 10 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jianqiang Cheng | 1 | 72 | 9.66 |
| Abdel Lisser | 2 | 168 | 29.93 |