Two-phase sample average approximation for video distribution strategy of edge computing in heterogeneous network

2021 ◽  
Author(s):  
Jingfu Lu ◽  
Jiuling Li
Keyword(s):  
Heterogeneous Network ◽  
Sample Average Approximation ◽  
Edge Computing ◽  
Two Phase ◽  
Distribution Strategy ◽  
Video Distribution ◽  
Sample Average ◽  
Phase Sample ◽  
Average Approximation

Sample Average Approximation

2017 ◽  
pp. 75-84
Author(s):  
Tingsong Wang ◽  
Shuaian Wang ◽  
Qiang Meng
Keyword(s):  
Sample Average Approximation ◽  
Sample Average ◽  
Average Approximation

SAMPLE AVERAGE APPROXIMATION METHOD FOR SOLVING A DETERMINISTIC FORMULATION FOR BOX CONSTRAINED STOCHASTIC VARIATIONAL INEQUALITY PROBLEMS

2012 ◽  
Vol 29 (02) ◽  
pp. 1250014
Author(s):  
MEI-JU LUO ◽  
GUI-HUA LIN
Keyword(s):  
Variational Inequality ◽  
Approximation Method ◽  
Deterministic Model ◽  
Random Parameter ◽  
Sample Average Approximation ◽  
Variational Inequality Problems ◽  
Stochastic Variational Inequality ◽  
Sample Average ◽  
Sample Average Approximation Method ◽  
Average Approximation

In this paper, we discuss the Expected Residual Minimization (ERM) method, which is to minimize the expected residue of some merit function for box constrained stochastic variational inequality problems (BSVIPs). This method provides a deterministic model, which formulates BSVIPs as an optimization problem. We first study the conditions under which the level sets of the ERM problem are bounded. Then, we show that solutions of the ERM formulation are robust in the sense that they may have a minimum sensitivity with respect to random parameter variations in BSVIPs. Since the integrality involved in the ERM problem is difficult to compute generally, we then employ sample average approximation method to solve it. Finally, we show that the global optimal solutions and generalized KKT points of the approximate problems converge to their counterparts of the ERM problem. On the other hand, as an application, we consider the model of European natural gas market under price uncertainty. Preliminary numerical experiments indicate that the proposed approach is applicable.

scholarly journals Sample average approximation of stochastic dominance constrained programs

2010 ◽  
Vol 133 (1-2) ◽  
pp. 171-201 ◽  
Author(s):  
Jian Hu ◽  
Tito Homem-de-Mello ◽  
Sanjay Mehrotra
Keyword(s):  
Stochastic Dominance ◽  
Sample Average Approximation ◽  
Sample Average ◽  
Average Approximation
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