NDLI: Multiscale optimization in neural nets: preliminary report

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International 1989 Joint Conference on Neural Networks

Multiscale optimization in neural nets: preliminary report

Content Provider	IEEE Xplore Digital Library
Author	Mjolsness, E. Garrett, C. Miranker, W.L.
Copyright Year	1990
Abstract	A multiscale optimization method for neural networks governed by quite general objective functions is presented. At the coarse scale, there is a smaller, approximating neural net. Like the original net, it is nonlinear and has a nonquadratic objective function, so the coarse-scale net is a more accurate approximation than a quadratic objective would be. The transitions and information flow form fine to coarse scale and back do not disrupt the optimization. The problem need not involve any geometric domain; all that is required is a partition of the original fine-scale variables. Given this partition, the rest of the multiscale optimization method requires no problem-specific design effort on the part of the user, since the mapping between coarse and fine scales is determined. Thus, the method can be applied easily to many problems and networks. Positive experimental results including cost comparisons are shown
Starting Page	781
Ending Page	786
File Size	430169
Page Count	6
File Format	PDF
DOI	10.1109/IJCNN.1990.137932
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1990-06-17
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Content Type	Text
Resource Type	Article

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