Other Nonlinear Regression Methods for Algebraic Models

Other Nonlinear Regression Methods for Algebraic Models 

There is a variety of general purpose unconstrained optimization methods that can be used to estimate unknown parameters. These methods are broadly classified into two categories direct search methods and gradient methods (Edgar and Himmelblau, 1988 Gill et al. 1981 Kowalik and Osborne, 1968 Sargent, 1980 Reklaitis, 1983 Scales, 1985). 

A brief overview of this relatively vast subject is presented and several of these methods are briefly discussed in the following sections. Over the years many comparisons of the performance of many methods have been carried out and reported in the literature. For example, Box (1966) evaluated eight unconstrained optimization methods using a set of problems with up to twenty variables. 

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