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An Asymmetric Proximal Decomposition Method for Convex Programming with Linearly Coupling Constraints
- Source :
- Advances in Operations Research, Vol 2012 (2012)
- Publication Year :
- 2012
- Publisher :
- Hindawi Publishing Corporation, 2012.
-
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.
- Subjects :
- Mathematical optimization
Article Subject
Augmented Lagrangian method
Computation
Management Science and Operations Research
Separable space
Proximal point
Quadratic equation
Convex optimization
Variational inequality
Decomposition method (constraint satisfaction)
lcsh:Production management. Operations management
lcsh:TS155-194
Mathematics
Subjects
Details
- Language :
- English
- ISSN :
- 16879147
- Database :
- OpenAIRE
- Journal :
- Advances in Operations Research
- Accession number :
- edsair.doi.dedup.....eb55308fd71e95a31d3ac3cc605d439c
- Full Text :
- https://doi.org/10.1155/2012/281396