Abstract
Partial separability and partitioned quasi-Newton updating have been recently introduced and experimented with success in large scale nonlinear optimization, large nonlinear least squares calculations and in large systems of nonlinear equations. It is the purpose of this paper to apply this idea to large dimensional nonlinear network optimization problems. The method proposed thus uses these techniques for handling the cost function, while more classical tools as variable partitioning and specialized data structures are used in handling the network constraints. The performance of a code implementing this method, as well as more classical techniques, is analyzed on several numerical examples.
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