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Second-order neural nets for constrained optimization

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3 Author(s)
Zhang, S. ; Exper Vision Inc., San Jose, CA, USA ; Zhu, X. ; Zou, L.-H.

Analog neural nets for constrained optimization are proposed as an analogue of Newton's algorithm in numerical analysis. The neural model is globally stable and can converge to the constrained stationary points. Nonlinear neurons are introduced into the net, making it possible to solve optimization problems where the variables take discrete values, i.e., combinatorial optimization

Published in:

Neural Networks, IEEE Transactions on  (Volume:3 ,  Issue: 6 )

Date of Publication:

Nov 1992

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