scholarly journals Non-Monotone Space Decomposition Methods for Minimization Problems

PAMM ◽  
2002 ◽  
Vol 1 (1) ◽  
pp. 472
Author(s):  
S. Keesmann ◽  
W. Mönch
Keyword(s):  
Decomposition Methods ◽  
Minimization Problems ◽  
Space Decomposition

scholarly journals Stochastic Methods Based onVU-Decomposition Methods for Stochastic Convex Minimax Problems

2014 ◽  
Vol 2014 ◽  
pp. 1-5 ◽  
Author(s):  
Yuan Lu ◽  
Wei Wang ◽  
Shuang Chen ◽  
Ming Huang
Keyword(s):  
Sample Size ◽  
Minimax Problems ◽  
Sample Average Approximation ◽  
Decomposition Methods ◽  
Stochastic Methods ◽  
Decomposition Theory ◽  
Space Decomposition ◽  
Sample Average ◽  
Average Approximation

This paper applies sample average approximation (SAA) method based onVU-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 theVU-theory, a superlinear convergentVU-algorithm frame is designed to solve the SAA problem.

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