Abstract

This paper describes a method for optimizing large partly nonlinear systems. The method is based on the GRG-algorithm, that solves problems with nonlinear objective function and nonlinear equality constraints. The original GRG-algorithm is described and its relations with LP are stressed. Some storage problems in large problems are discussed, and a special inversion procedure for the GRG-algorithm is presented. Some special kinds of constraints, inequalities and linear constraints, are considered, and it is shown, how their special features can be utilized. Finally some computational results with the method are given.KeywordsBasic VariableInversion ProcedurePivot ElementNonbasic VariableNonlinear Objective FunctionThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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