Abstract
A new algorithm is presented for finding the minimum of a nonnegative objective function subject to general nonlinear constraints. This algorithm, based of Gauss’ method for unconstrained problems, is developed as an extension to the Gauss constrained technique for linear constraints. The derivation of the algorithm, using a Lagrange multiplier approach, is based on the Kuhn-Tucker conditions so that, when the iteration process terminates, these conditions are automatically satisfied. A feasible design is maintained throughout the iteration process. The solution of preliminary examples indicate excellent results in terms of the number of objective function evaluations required by the algorithm to obtain an optimal design.
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