Generalized Programming by Linear Approximation of the Dual Gradient: Convex Programming Case
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.Similar works
This paper was published in Research Papers in Economics.
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