title: Second-order neural nets for constrained optimization.
abstract: 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.
authors.affiliation: Exper Vision Inc., San Jose, CA.
authors.forename: S
authors.initials: S
authors.lastname: Zhang
authors.forename: X
authors.initials: X
authors.lastname: Zhu
authors.forename: L H
authors.initials: LH
authors.lastname: Zou