An Asymmetric Proximal Decomposition Method for Convex Programming with Linearly Coupling Constraints

Xiaoling Fu, Xiangfeng Wang, Haiyan Wang and Ying Zhai

Advances in Operations Research, 2012, vol. 2012, 1-20

Abstract:

The problems studied are the separable variational inequalities with linearly coupling constraints. Some existing decomposition methods are very problem specific, and the computation load is quite costly. Combining the ideas of proximal point algorithm (PPA) and augmented Lagrangian method (ALM), we propose an asymmetric proximal decomposition method (AsPDM) to solve a wide variety separable problems. By adding an auxiliary quadratic term to the general Lagrangian function, our method can take advantage of the separable feature. We also present an inexact version of AsPDM to reduce the computation load of each iteration. In the computation process, the inexact version only uses the function values. Moreover, the inexact criterion and the step size can be implemented in parallel. The convergence of the proposed method is proved, and numerical experiments are employed to show the advantage of AsPDM.

Date: 2012
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlaor:281396

DOI: 10.1155/2012/281396

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