Report (Published version) - 
Free access
Highlights
More informations
Report
unige:5864 
 |
Confidence Level Solutions for Stochastic Programming |
|
| Authors | ||
| Year | 2000 | |
| Collection |
Cahiers de recherche; 2000.05 |
|
| Abstract | We propose an alternative apporach to stochastic programming based on Monte-Carlo sampling and stochastic gradient optimization. The procedure is by essence probabilistic and the computed solution is a random variable. The associated objectiev value is doubly random, since it depends two outcomes: the event in the stochastic program and the randomized algorithm. We propose a solution concept in which the propability that the randomized algorithm produces a solution with an expected objective value departing from the optimal one by more than is small enough. We derive complexity bounds for this process. We show that by repeating the basic process on independent sample, one can significantly sharpen the complexity bounds. | |
| Full text | ||
| Structures | ||
| Citation (ISO format) | NESTOROV, Yurii, VIAL, Jean-Philippe. Confidence Level Solutions for Stochastic Programming. 2000 https://archive-ouverte.unige.ch/unige:5864 |
210 hits 
517 downloads 
Contact an author
Update