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: | Ruszczynski Andrzej |
Keywords: | programming: linear, programming: probabilistic |
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.