A non-penalty recurrent neural network for solving a class of constrained optimization problems

DOI10.1016/j.neunet.2015.09.013zbMath1405.90127OpenAlexW1829299842WikidataQ50551939 ScholiaQ50551939MaRDI QIDQ1669292

Alireza Hosseini

Publication date: 30 August 2018

Published in: Neural Networks (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.neunet.2015.09.013

zbMATH Keywords

nonsmooth optimizationdifferential inclusionrecurrent neural networksevolution timesolution trajectory

Mathematics Subject Classification ID

Nonlinear programming (90C30) Neural networks for/in biological studies, artificial life and related topics (92B20) Ordinary differential inclusions (34A60)

Related Items (6)

A nonpenalty neurodynamic model for complex-variable optimization ⋮ Nonlinear recurrent neural networks for finite-time solution of general time-varying linear matrix equations ⋮ A neurodynamic approach to nonsmooth constrained pseudoconvex optimization problem ⋮ A one-layer recurrent neural network for nonsmooth pseudoconvex optimization with quasiconvex inequality and affine equality constraints ⋮ A neurodynamic approach to convex optimization problems with general constraint ⋮ A neurodynamic optimization approach for complex-variables programming problem

Cites Work

• A one-layer recurrent neural network for constrained pseudoconvex optimization and its application for dynamic portfolio optimization
• A recurrent neural network for solving a class of generalized convex optimization problems
• A one-layer recurrent neural network for constrained nonsmooth invex optimization
• A hybrid steepest descent method for constrained convex optimization
• Two \(k\)-winners-take-all networks with discontinuous activation functions
• A collective neurodynamic optimization approach to bound-constrained nonconvex optimization
• A new steepest descent differential inclusion-based method for solving general nonsmooth convex optimization problems
• Generalized Lyapunov approach for convergence of neural networks with discontinuous or non-Lipschitz activations
• A differential inclusion-based approach for solving nonsmooth convex optimization problems
• Optimization and nonsmooth analysis
• Generalized Neural Network for Nonsmooth Nonlinear Programming Problems
• Sparse Representations and Compressive Sensing for Imaging and Vision
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