Distributed iterative aggregation algorithms for box-constrained minimization problems and optimal routing in data networks

A gradient-projection algorithm using iterative aggregation and disaggregation is proposed and analyzed for box-constrained minimization problems. In a distributed computation model, the algorithm is shown to converge. The algorithm is applied to optimal routing in a large interconnected data communication network, resulting in a multilevel hierarchical clustering that naturally fits the hierarchical topological structure of such networks. An implementation of the algorithm for a 52-node network shows that the serial version of the algorithm has a saving of 35% of the computational time as compared to a path-formulated gradient-projection code that is among the fastest existing programs for path-formulated optimal routing