Approximately shortest paths in large scale networks: Algorithms and probabilistic analysis
A class of algorithms, Hierarchical Aggregation Algorithms (HAA), for approximately solving shortest paths problems in very large scale networks are proposed which aim at reducing the computational effort. Networks are first aggregated into a set of subnetworks. Higher level imbedded macronetworks are then defined. The shortest paths are approximated by combining exact shortest paths in subnetworks and in higher level networks. We discuss a probabilistic error analysis and the simulation results in Manhattan-type networks. The algorithm is furthermore implemented on a real-world network, southeastern Michigan network. The numerical results from variations of the algorithm will be compared.
- OSTI ID:
- 35905
- Report Number(s):
- CONF-9408161--
- Country of Publication:
- United States
- Language:
- English
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