Abstract

Abstract This paper deals with the development of a completely different technique for solving the linear programming (LP) problem. The general LP problem is replaced by an LP problem having a single constraint called the surrogated linear programming (SLP) problem. The SLP problem retains the same objective function, but is constrained by a convex combination of the original constraints. A special equivalency relationship is derived between the general problem and the surrogated problem in route to the final computational algorithm. The technique is not subject to round off error propagation and has promise for savings in computation time.

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