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The Stochastic Descent Algorithm for a Kind ofSemidefinite Variational Inequality Problem(PDF)

《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

Issue:
2017年01期
Page:
6-
Research Field:
·数学与计算机科学·
Publishing date:

Info

Title:
The Stochastic Descent Algorithm for a Kind ofSemidefinite Variational Inequality Problem
Author(s):
Xu Haiwen1; Sun Liming2
(1.College of Computer Science and Technology,Civil Aviation Flight University of China,Guanghan 618307,China)(2.College of Science,Nanjing Audit University,Nanjing 211815,China)
Keywords:
semidefinite variational inequality problem; correction projection and contraction algorithm; stochastic descent algorithm; probability convergence
PACS:
O221
DOI:
10.3969/j.issn.1001-4616.2017.01.002
Abstract:
The amplification inequality is used for many times in the proof of drop function of correction projection and contraction algorithm,so we propose the stochastic descent algorithm for a class of semidifinite variational inequality problem through the random steplength extension with the random number series satisfying the Gaussian distribution or Uniform distribution and these random number series have a fixed mean. Subsequently,the probability convergence of stochastic descent algorithm is provided by the properties of Markov’s inequality and probability convergence under some suitable conditions. Finally,some numerical experiments show the effectiveness and efficiency of the stochastic descent algorithm,and reasonable selecting mean and variance of random number can improve the efficiency of the algorithm.

References:

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