Original title: Empirical Estimates via Stability in Stochastic Programming
Translated title: Empirické odhady a stabilita ve stochastickém programování
Authors: Kaňková, Vlasta
Document type: Research reports
Year: 2007
Language: eng
Series: Research Report, volume: 2192
Abstract: It is known that optimization problems depending on a probability measure correspond to many applications. It is also known that these problems belong mostly to a class of nonlinear optimization problems and, moreover, that very often an ``underlying" probability measure is not completely known. The aim of the research report is to deal with the case when an empirical measure substitutes the theoretical one. In particular, the aim is to generalize reults dealing with convergence rate in the case of empirical esrimates. The introduced results are based on the stability results corresponding to the Wasserstein metric. A relationship berween tails of one-dimensional marginal distribution functions and exponentional rate of convergence are introduced. The corresponding results are focus mainly on ``classical" type of problems corresponding to the cases with penalty and recourse. However, an integer simple recourse case and some special risk funkcionals are discussed also.
Keywords: convergence rate; empirical estimates; integer simple recourse case; problems with penalty and recourse; resk funkcionals; stability; Stochastic programming; Wasserstein metric
Project no.: CEZ:AV0Z10750506 (CEP), GA402/06/1417 (CEP), GA402/05/0115 (CEP), GA402/07/1113 (CEP)
Funding provider: GA ČR, GA ČR, GA ČR

Institution: Institute of Information Theory and Automation AS ČR (web)
Document availability information: Fulltext is available at the institute of the Academy of Sciences.
Original record: http://hdl.handle.net/11104/0157128

Permalink: http://www.nusl.cz/ntk/nusl-38385


The record appears in these collections:
Research > Institutes ASCR > Institute of Information Theory and Automation
Reports > Research reports