A NOVEL RECURRENT NEURAL NETWORK FOR SOLVING MLCPs AND ITS APPLICATION TO LINEAR AND QUADRATIC PROGRAMMING PROBLEMS

DOI10.1142/S0217595911003223zbMath1233.90261OpenAlexW2000460928MaRDI QIDQ3174017

Masumeh Abbasi, Sohrab Effati, A. Ghomashi

Publication date: 11 October 2011

Published in: Asia-Pacific Journal of Operational Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1142/s0217595911003223

zbMATH Keywords

stabilityglobal convergencelinear programmingquadratic programmingdynamical systemmixed linear complementarity problemNCP functions

Mathematics Subject Classification ID

Semidefinite programming (90C22) Approximation methods and heuristics in mathematical programming (90C59) Complementarity and equilibrium problems and variational inequalities (finite dimensions) (aspects of mathematical programming) (90C33)

Related Items (2)

An inverse-free dynamical system for solving the absolute value equations ⋮ An efficient neural network for solving convex optimization problems with a nonlinear complementarity problem function

Cites Work

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• A deterministic annealing neural network for convex programming
• On the stability of globally projected dynamical systems
• Nonlinear complementarity as unconstrained optimization
• Application of projection neural network in solving convex programming problems
• Lagrange programming neural networks
• Mathematical Programming in Neural Networks
• A special newton-type optimization method
• On convergence rate of projection neural networks
• Mathematical control theory: an introduction

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