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An efficient simplified neural network for solving linear and quadratic programming problems

Ghasabi Oskoei, H ; Sharif University of Technology | 2006

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  1. Type of Document: Article
  2. DOI: 10.1016/j.amc.2005.07.025
  3. Publisher: 2006
  4. Abstract:
  5. We present a high-performance and efficiently simplified new neural network which improves the existing neural networks for solving general linear and quadratic programming 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. © 2005 Elsevier Inc. All rights reserved
  6. Keywords:
  7. Convergence of numerical methods ; Linear programming ; Problem solving ; Quadratic programming ; Global convergence ; Parameter settings ; Neural networks
  8. Source: Applied Mathematics and Computation ; Volume 175, Issue 1 , 2006 , Pages 452-464 ; 00963003 (ISSN)
  9. URL: https://www.sciencedirect.com/science/article/pii/S009630030500620X