On convergence of an augmented Lagrangian decomposition method for sparse convex-optimization

On convergence of an augmented Lagrangian decomposition method for sparse convex-optimization

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Article ID: iaor20041784
Country: United States
Volume: 20
Issue: 3
Start Page Number: 634
End Page Number: 656
Publication Date: Aug 1995
Journal: Mathematics of Operations Research
Authors:
Keywords: programming: linear, programming: probabilistic
Abstract:

A decomposition method for large-scale convex optimization problems with block-angular structure and many linking constraints is analysed. The method is based on separable approximation of the augemented 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.

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