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

Michael H. Wagner and J. Franklin Sharp
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Michael H. Wagner: ICF, Inc., Washington, D.C. 20036
J. Franklin Sharp: AT&T Analytic Support Center, New York

Management Science, 1977, vol. 23, issue 12, 1307-1313

Abstract: 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.

Date: 1977
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