Deriving solution value bounds from the ADMM
| Autor: | Jonathan Eckstein |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Mathematical optimization
021103 operations research Control and Optimization Computer science 0211 other engineering and technologies Computational intelligence 010103 numerical & computational mathematics 02 engineering and technology 01 natural sciences Set (abstract data type) Lasso (statistics) Bounding overwatch Proof of concept Simple (abstract algebra) Convex optimization 0101 mathematics Subgradient method |
| Zdroj: | Optimization Letters. 14:1289-1303 |
| ISSN: | 1862-4480 1862-4472 |
| DOI: | 10.1007/s11590-020-01584-1 |
| Popis: | This short paper describes a simple subgradient-based techniques for deriving bounds on the optimal solution value when using the ADMM to solve convex optimization problems. The technique requires a bound on the magnitude of some optimal solution vector, but is otherwise completely general. Some computational examples using LASSO problems demonstrate that the technique can produce steadily converging bounds in situations in which standard Lagrangian bounds yield little or no useful information. A second set of experiments establishes a proof of concept indicating the potential practical usefulness of the bounding technique. |
| Databáze: | OpenAIRE |
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