Abstract
Nonlinear optimization is one of the crucial topics in the numerical treatment of chemical engineering problems. Numerical optimization deals with the problems of solving systems of nonlinear equations or minimizing nonlinear functionals (with respect to side conditions). In this article we present a new method for unconstrained minimization which is suitable as well in large scale as in bad conditioned problems. The method is based on a true multi-dimensional modeling of the objective function in each iteration step. The scheme allows the incorporation of more given or known information into the search than in common line search methods.
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© 1996 Springer-Verlag Berlin Heidelberg
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Rascher-Friesenhausen, R. (1996). Subspace Search Methods for Large Scale Nonlinear Optimization. In: Keil, F., Mackens, W., Voß, H., Werther, J. (eds) Scientific Computing in Chemical Engineering. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-80149-5_21
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DOI: https://doi.org/10.1007/978-3-642-80149-5_21
Publisher Name: Springer, Berlin, Heidelberg
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