Asymptotically Optimal Allocation of Simulation Experiments in Discrete Stochastic Optimization
Approximate solutions for discrete stochastic optimization problems are often obtained via simulation. It is reasonable to complement these solutions by confidence regions for the argmin-set. We address the question, how a certain total number of random draws should be distributed among the set of alternatives. We propose a one-step allocation rule which turns out to be asymptotically optimal in the case of normal errors for two goals: To minimize the costs caused by using only an approximate solution and to minimize the expected size of the confidence sets.
|Date of creation:||Mar 1996|
|Date of revision:|
|Contact details of provider:|| Postal: A-2361 Laxenburg|
Web page: http://www.iiasa.ac.at/Publications/Catalog/PUB_ONLINE.html
More information through EDIRC
When requesting a correction, please mention this item's handle: RePEc:wop:iasawp:wp96023. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Thomas Krichel)
If references are entirely missing, you can add them using this form.