Stochastic Methods Based on -Decomposition Methods for Stochastic Convex Minimax Problems

Yuan Lu, Wei Wang, Shuang Chen and Ming Huang

Mathematical Problems in Engineering, 2014, vol. 2014, 1-5

Abstract:

This paper applies sample average approximation (SAA) method based on -space decomposition theory to solve stochastic convex minimax problems. Under some moderate conditions, the SAA solution converges to its true counterpart with probability approaching one and convergence is exponentially fast with the increase of sample size. Based on the -theory, a superlinear convergent -algorithm frame is designed to solve the SAA problem.

Date: 2014
References: Add references at CitEc
Citations:

Downloads: (external link)
http://downloads.hindawi.com/journals/MPE/2014/894248.pdf (application/pdf)
http://downloads.hindawi.com/journals/MPE/2014/894248.xml (text/xml)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:894248

DOI: 10.1155/2014/894248

Access Statistics for this article

More articles in Mathematical Problems in Engineering from Hindawi
Bibliographic data for series maintained by Mohamed Abdelhakeem ().