• DocumentCode
    285640
  • Title

    A primal-dual linear programming solver with linear order complexity

  • Author

    Chiang, Hsiao-Dong ; Yuan, Jen-Lun ; Chu, Chia-Chi

  • Author_Institution
    Sch. of Electr. Eng., Cornell Univ., Ithaca, NY, USA
  • Volume
    4
  • fYear
    1992
  • fDate
    3-6 May 1992
  • Firstpage
    1697
  • Abstract
    Recurrent artificial neural network (ANN) models are presented for solving primal-dual linear programming problems. The theoretical background is introduced based on the nonlinear analysis of an ANN. A general procedure to synthesize an ANN for optimization problems is discussed. A method to reduce the circuit complexity of the proposed ANN from the order of O(mn) to O(m+n ) is developed. Simulation results are presented through an example of up to 20 variables
  • Keywords
    linear programming; recurrent neural nets; artificial neural network; circuit complexity; linear order complexity; nonlinear analysis; optimization problems; primal-dual linear programming solver; recurrent ANN; Artificial neural networks; Circuit simulation; Complexity theory; Computational modeling; Computer networks; Linear programming; Neural networks; Neurons; Output feedback; Voltage;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Circuits and Systems, 1992. ISCAS '92. Proceedings., 1992 IEEE International Symposium on
  • Conference_Location
    San Diego, CA
  • Print_ISBN
    0-7803-0593-0
  • Type

    conf

  • DOI
    10.1109/ISCAS.1992.230349
  • Filename
    230349
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=285640