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Generalized Programming by Linear Approximation of the Dual Gradient: Convex Programming Case


  • Michael H. Wagner

    (ICF, Inc., Washington, D.C. 20036)

  • J. Franklin Sharp

    (AT&T Analytic Support Center, New York)


A modified version of Generalized Programming is presented for solving convex programming problems. The procedure uses convenient linear approximations of the gradient of the dual in order to approximate the Kuhn-Tucker conditions for the dual. Solution points of these approximate Kuhn-Tucker conditions are then used for column generation. Computational results are reported.

Suggested Citation

  • Michael H. Wagner & J. Franklin Sharp, 1977. "Generalized Programming by Linear Approximation of the Dual Gradient: Convex Programming Case," Management Science, INFORMS, vol. 23(12), pages 1307-1313, August.
  • Handle: RePEc:inm:ormnsc:v:23:y:1977:i:12:p:1307-1313

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