An efficient simplified neural network for solving linear and quadratic programming problems Hasan Ghasabi-Oskoei a , Nezam Mahdavi-Amiri b, * a Mathematics and Informatics Research Group, Academic Center for Education Culture and Research, Tarbiat Modarres University, P.O. Box 14115-343, Tehran, Iran b Department of Mathematical Sciences, Sharif University of Technology, P.O. Box 11365-9415, Tehran, Iran Abstract We present a high-performance and efficiently simplified new neural network which improves the existing neural networks for solving general linear and quadratic program- ming problems. The network, having no need for parameter setting, results in a simple hardware requiring no analog multipliers, is shown to be stable and converges globally to the exact solution. Moreover, using this network we can solve both linear and quadratic programming problems and their duals simultaneously. High accuracy of the obtained solutions and low cost of implementation are among the features of this network. We prove the global convergence of the network analytically and verify the results numerically. Ó 2005 Elsevier Inc. All rights reserved. Keywords: Neural network; Quadratic programming; Linear programming; Global convergence 0096-3003/$ - see front matter Ó 2005 Elsevier Inc. All rights reserved. doi:10.1016/j.amc.2005.07.025 * Corresponding author. E-mail addresses: hgoskoei@modares.ac.ir (H. Ghasabi-Oskoei), nezamm@sina.sharif.edu (N. Mahdavi-Amiri). Applied Mathematics and Computation 175 (2006) 452–464 www.elsevier.com/locate/amc