We propose and analyze a distributed asynchronous subgradient method for minimizing a convex function that consists of the sum of a large number of component functions. This type of minimization arises in a dual context from Lagrangian relaxation of the coupling constraints of large scale separable problems. The idea is to distribute the computation of the component subgradients among a set of processors, which communicate only with a coordinator. The coordinator performs the subgradient iteration incrementally and asynchronously, by taking steps along the subgradients of the component funtions that are available at the update time. The incremental approach has performed very well in centralized computation, and the parallel implementation should improve its performance substantially, particularly for typical problems where computation of the component subgradients is relatively costly.
ASJC Scopus subject areas
- Computational Mathematics