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Bundle-Based Decomposition For Large-Scale Convex Optimization: Error Estimate And Application To Block-Angular Linear Programs (1994)
| Content Provider | CiteSeerX |
|---|---|
| Author | Medhi, Deepankar |
| Abstract | Robinson has proposed the bundle-based decomposition algorithm to solve a class of structured large-scale convex optimization problems. In this method, the original problem is transformed (by dualization) to an unconstrained nonsmooth concave optimization problem which is in turn solved by using a modified bundle method. In this paper, we give a posteriori error estimates on the approximate primal optimal solution and on the duality gap. We describe implementation and present computational experience with a special case of this class of problems, namely, block-angular linear programming problems. We observe that the method is efficient in obtaining approximate optimal solution and compares favorably with MINOS and an advanced implementation of the Dantzig-Wolfe decomposition method. |
| File Format | |
| Volume Number | 22 |
| Journal | ANNALS OF OPERATIONS RESEARCH |
| Language | English |
| Publisher Date | 1994-01-01 |
| Access Restriction | Open |
| Subject Keyword | Large-scale Convex Optimization Bundle-based Decomposition Error Estimate Block-angular Linear Program Bundle-based Decomposition Algorithm Present Computational Experience Advanced Implementation Posteriori Error Estimate Dantzig-wolfe Decomposition Method Approximate Optimal Solution Block-angular Linear Programming Problem Bundle Method Approximate Primal Optimal Solution Optimization Problem Structured Large-scale Convex Optimization Problem Special Case Original Problem Duality Gap |
| Content Type | Text |
| Resource Type | Article |
