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A class of smoothing SAA methods for a stochastic linear complementarity problem

A class of smoothing SAA methods for a stochastic linear complementarity problem
A class of smoothing SAA methods for a stochastic linear complementarity problem
A class of smoothing sample average approximation (SAA) methods is proposed for solving a stochastic linear complementarity problem, where the underlying function is the expected value of stochastic function. Existence and convergence results to the proposed methods are provided and some numerical results are reported to show the efficiency of the methods proposed.
smoothing saa method, stochastic linear complementarity problem
2155-3289
145-156
Zhang, Jie
21de2303-4727-4097-9b0f-ae43d95d052a
Wu, Yue
e279101b-b392-45c4-b894-187e2ded6a5c
Zhang, Liwei
10fce21c-16d9-4096-b07a-cf2cab1591c0
Zhang, Jie
21de2303-4727-4097-9b0f-ae43d95d052a
Wu, Yue
e279101b-b392-45c4-b894-187e2ded6a5c
Zhang, Liwei
10fce21c-16d9-4096-b07a-cf2cab1591c0

Zhang, Jie, Wu, Yue and Zhang, Liwei (2012) A class of smoothing SAA methods for a stochastic linear complementarity problem Numerical Algebra, Control and Optimization, 2, (1), pp. 145-156. (doi:10.3934/naco.2012.2.145).

Record type: Article

Abstract

A class of smoothing sample average approximation (SAA) methods is proposed for solving a stochastic linear complementarity problem, where the underlying function is the expected value of stochastic function. Existence and convergence results to the proposed methods are provided and some numerical results are reported to show the efficiency of the methods proposed.

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Published date: March 2012
Keywords: smoothing saa method, stochastic linear complementarity problem
Organisations: Centre of Excellence for International Banking, Finance & Accounting

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Local EPrints ID: 336444
URI: http://eprints.soton.ac.uk/id/eprint/336444
ISSN: 2155-3289
PURE UUID: b55ab8f8-86ce-460e-a52b-c4a6bffbbe99

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Date deposited: 27 Mar 2012 09:10
Last modified: 18 Jul 2017 06:07

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Contributors

Author: Jie Zhang
Author: Yue Wu
Author: Liwei Zhang

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