scholarly journals Generalization of Almost Sure Convergence Properties of Pairwise NQD Random Sequences

2011 ◽  
Vol 3 (3) ◽  
Author(s):  
Ying Zhang ◽  
Yanchun Wu ◽  
Xingwang Luo
Keyword(s):  
Almost Sure Convergence ◽  
Convergence Properties ◽  
Random Sequences

scholarly journals Some Almost-Sure Convergence Properties Useful in Sequential Analysis

2005 ◽  
Vol 24 (4) ◽  
pp. 411-419 ◽  
Author(s):  
Seong-Hee Kim ◽  
Barry L. Nelson ◽  
James R. Wilson
Keyword(s):  
Sequential Analysis ◽  
Almost Sure Convergence ◽  
Convergence Properties

Almost sure convergence for non-stationary random sequences

2009 ◽  
Vol 79 (7) ◽  
pp. 857-863 ◽  
Author(s):  
Zhongquan Tan ◽  
Zuoxiang Peng
Keyword(s):  
Almost Sure Convergence ◽  
Random Sequences

scholarly journals A Regularization SAA Scheme for a Stochastic Mathematical Program with Complementarity Constraints

2014 ◽  
Vol 2014 ◽  
pp. 1-12
Author(s):  
Yu-xin Li ◽  
Jie Zhang ◽  
Zun-quan Xia
Keyword(s):  
Recent Literature ◽  
Mathematical Program ◽  
Uncertain Data ◽  
Almost Sure Convergence ◽  
Sample Average Approximation ◽  
Accumulation Point ◽  
Complementarity Constraints ◽  
Convergence Properties ◽  
Sample Average ◽  
Average Approximation

To reflect uncertain data in practical problems, stochastic versions of the mathematical program with complementarity constraints (MPCC) have drawn much attention in the recent literature. Our concern is the detailed analysis of convergence properties of a regularization sample average approximation (SAA) method for solving a stochastic mathematical program with complementarity constraints (SMPCC). The analysis of this regularization method is carried out in three steps: First, the almost sure convergence of optimal solutions of the regularized SAA problem to that of the true problem is established by the notion of epiconvergence in variational analysis. Second, under MPCC-MFCQ, which is weaker than MPCC-LICQ, we show that any accumulation point of Karash-Kuhn-Tucker points of the regularized SAA problem is almost surely a kind of stationary point of SMPCC as the sample size tends to infinity. Finally, some numerical results are reported to show the efficiency of the method proposed.

Convergence properties of hit-and-run samplers

1998 ◽  
Vol 14 (4) ◽  
pp. 767-800
Author(s):  
Claude Bélisle ◽  
Arnon Boneh ◽  
Richard J. Caron
Keyword(s):  
Convergence Properties ◽  
Hit And Run
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