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