Augmented Lagrangian Decomposition for Sparse Convex Optimization

Ruszczynski, A. (1992). Augmented Lagrangian Decomposition for Sparse Convex Optimization. IIASA Working Paper. IIASA, Laxenburg, Austria: WP-92-075

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A decomposition method for large-scale convex optimization problems with block-angular structure and many linking constraints is analyzed. The method is based on a separable approximation of the augmented Lagrangian function. Weak global convergence of the method is proved and speed of convergence analysed. It is shown that convergence properties of the method are heavily dependent on sparsity of the linking constraints. Application to large scale linear programming and stochastic programming is discussed.

Item Type: Monograph (IIASA Working Paper)
Research Programs: Adaption and Optimization (ADO)
Depositing User: IIASA Import
Date Deposited: 15 Jan 2016 02:01
Last Modified: 27 Aug 2021 17:14

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