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Received: 2005-09-08

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Journal of Zhejiang University SCIENCE A 2007 Vol.8 No.6 P.969-977


Comparison of two approximal proximal point algorithms for monotone variational inequalities

Author(s):  TAO Min

Affiliation(s):  School of Applied Mathematics and Physics, Nanjing University of Posts and Telecommunications, Nanjing 210003, China

Corresponding email(s):   mathsgirl2002@yahoo.com.cn

Key Words:  Projection and contraction methods, Proximal point algorithm (PPA), Approximate PPA (APPA), Monotone variational inequality (MVI), Prediction and correction

TAO Min. Comparison of two approximal proximal point algorithms for monotone variational inequalities[J]. Journal of Zhejiang University Science A, 2007, 8(6): 969-977.

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author="TAO Min",
journal="Journal of Zhejiang University Science A",
publisher="Zhejiang University Press & Springer",

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%DOI 10.1631/jzus.2007.A0969

T1 - Comparison of two approximal proximal point algorithms for monotone variational inequalities
A1 - TAO Min
J0 - Journal of Zhejiang University Science A
VL - 8
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EP - 977
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Y1 - 2007
PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.2007.A0969

Proximal point algorithms (PPA) are attractive methods for solving monotone variational inequalities (MVI). Since solving the sub-problem exactly in each iteration is costly or sometimes impossible, various approximate versions of PPA (APPA) are developed for practical applications. In this paper, we compare two APPA methods, both of which can be viewed as prediction-correction methods. The only difference is that they use different search directions in the correction-step. By extending the general forward-backward splitting methods, we obtain Algorithm I; in the same way, Algorithm II is proposed by spreading the general extra-gradient methods. Our analysis explains theoretically why Algorithm II usually outperforms Algorithm I. For computation practice, we consider a class of MVI with a special structure, and choose the extending Algorithm II to implement, which is inspired by the idea of Gauss-Seidel iteration method making full use of information about the latest iteration. And in particular, self-adaptive techniques are adopted to adjust relevant parameters for faster convergence. Finally, some numerical experiments are reported on the separated MVI. Numerical results showed that the extending Algorithm II is feasible and easy to implement with relatively low computation load.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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