Title | ||
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Subgradient-based feedback neural networks for non-differentiable convex optimization problems |
Abstract | ||
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This paper developed the dynamic feedback neural network model to solve the convex nonlinear programming problem proposed
by Leung et al. and introduced subgradient-based dynamic feedback neural networks to solve non-differentiable convex optimization problems.
For unconstrained non-differentiable convex optimization problem, on the assumption that the objective function is convex
coercive, we proved that with arbitrarily given initial value, the trajectory of the feedback neural network constructed by
a projection subgradient converges to an asymptotically stable equilibrium point which is also an optimal solution of the
primal unconstrained problem. For constrained non-differentiable convex optimization problem, on the assumption that the objective
function is convex coercive and the constraint functions are convex also, the energy functions sequence and corresponding
dynamic feedback subneural network models based on a projection subgradient are successively constructed respectively, the
convergence theorem is then obtained and the stopping condition is given. Furthermore, the effective algorithms are designed
and some simulation experiments are illustrated. |
Year | DOI | Venue |
---|---|---|
2006 | 10.1007/s11432-006-2007-5 | Science in China Series F: Information Sciences |
Keywords | Field | DocType |
non-differentiable convex optimization,feedback neural network.,convergence,projection subgradient | Mathematical optimization,Subgradient method,Convex combination,Subderivative,Conic optimization,Proper convex function,Convex optimization,Convex analysis,Mathematics,Linear matrix inequality | Journal |
Volume | Issue | ISSN |
49 | 4 | 1862-2836 |
Citations | PageRank | References |
3 | 0.43 | 3 |
Authors | ||
3 |
Name | Order | Citations | PageRank |
---|---|---|---|
Guocheng Li | 1 | 100 | 7.13 |
Shiji Song | 2 | 1247 | 94.76 |
Cheng Wu | 3 | 1154 | 93.20 |