A Numerical Investigation of Several One-Dimensional Search Procedures in Nonlinear Regression Problems

Publisher Website
Google Scholar
Abstract
The solution of nonlinear regression problems often requires the use of iterative algorithms in order to obtain the best possible set of parameters. It has become apparent that the inclusion of a one-dimensional search procedure between iterations is generally beneficial for improving both convergence and rates of convergence. There are a number of different search procedures currently in use and this report investigates five of them by using the modified Gauss-Newton iterative algorithm with and without second partial derivatives. Four different measures of effectiveness, related to the accuracy and efficiency of the total algorithm, are used to rate the search procedures. Trials on two nonlinear regression problems of three and six parameters show that although a certain degree of accuracy in the search procedure is necessary, an increase in accuracy does not necessarily improve the performance of the total algorithm.

This publication has 0 references indexed in Scilit: