Efficient Parameters Estimation Method for the Separable Nonlinear Least Squares Problem

Ke Wang, Guolin Liu, Qiuxiang Tao and Min Zhai

Complexity, 2020, vol. 2020, 1-16

Abstract:

In this work, we combine the special structure of the separable nonlinear least squares problem with a variable projection algorithm based on singular value decomposition to separate linear and nonlinear parameters. Then, we propose finding the nonlinear parameters using the Levenberg–Marquart (LM) algorithm and either solve the linear parameters using the least squares method directly or by using an iteration method that corrects the characteristic values based on the L-curve, according to whether or not the nonlinear function coefficient matrix is ill posed. To prove the feasibility of the proposed method, we compared its performance on three examples with that of the LM method without parameter separation. The results show that (1) the parameter separation method reduces the number of iterations and improves computational efficiency by reducing the parameter dimensions and (2) when the coefficient matrix of the linear parameters is well-posed, using the least squares method to solve the fitting problem provides the highest fitting accuracy. When the coefficient matrix is ill posed, the method of correcting characteristic values based on the L-curve provides the most accurate solution to the fitting problem.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:hin:complx:9619427

DOI: 10.1155/2020/9619427

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