Monte Carlo Optimization and Path Dependent Nonstationary Laws of Large Numbers
New types of laws of large numbers are derived by using connections between estimation and stochastic optimization problems. They enable one to "track" time-and-path dependent functionals by using, in general, nonlinear estimators. Proofs are based on the new stochastic version of the Lyapunov's method. Applications to Monte Carlo optimization, stochastic branch and bounds method and minimization of risk functions are discussed.
|Date of creation:||Mar 1998|
|Contact details of provider:|| Postal: A-2361 Laxenburg|
Web page: http://www.iiasa.ac.at/Publications/Catalog/PUB_ONLINE.html
More information through EDIRC
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- T.Y. Ermolieva & Y.M. Ermoliev & V.I. Norkin, 1997. "Spatial Stochastic Model for Optimization Capacity of Insurance Networks Under Dependent Catastrophic Risks: Numerical Experiments," Working Papers ir97028, International Institute for Applied Systems Analysis.
- T.Y. Ermolieva, 1997. "The Design of Optimal Insurance Decisions in the Presence of Catastrophic Risks," Working Papers ir97068, International Institute for Applied Systems Analysis.
- V.I. Norkin & G.C. Pflug & A. Ruszczynski, 1996. "A Branch and Bound Method for Stochastic Global Optimization," Working Papers wp96065, International Institute for Applied Systems Analysis.
When requesting a correction, please mention this item's handle: RePEc:wop:iasawp:ir98009. 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.