Basic HTML version of Foils prepared 15 March 1996

Foil 66 NP Completeness and Neural Networks In Summary

From Physical Optimization and Physical Computation CPSP713 Case studies in Computational Science -- Spring Semester 1996. by Geoffrey C. Fox
1 Neural Networks work well for data decomposition as neural variables are natural nonredundant description
2 In "analogous" TSP and navigation problems, constraints on redundant neural variable Þ elastic net (can view as a generalized neural net) better
3 Why do all methods work so well for graph partitioning when computer scientists are taught to be terrified by such NP complete problems
in Table To:

Northeast Parallel Architectures Center, Syracuse University, npac@npac.syr.edu

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