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

By Michael H. Wagner and J. Franklin Sharp

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.

DOI identifier: 10.1287/mnsc.23.12.1307
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