A neurodynamic optimization approach to distributed nonconvex optimization based on an HP augmented Lagrangian
Huimin Guan1, Yang Liu2, Kit Ian Kou3
1School of Mathematical Sciences, Zhejiang Normal University, Jinhua 321004, China.
Abstract:
This paper develops a neurodynamic model for distributed nonconvex-constrained optimization. In the distributed constrained optimization model, the objective function and inequality constraints do not need to be convex, and equality constraints do not need to be affine. A Hestenes-Powell augmented Lagrangian function for handling the nonconvexity is established, and a neurodynamic system is developed based on this. It is proved that it is stable at a local optimal solution of the optimization model. Two illustrative examples are provided to evaluate the enhanced stability and optimality of the developed neurodynamic systems.
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