Statistics and Its Interface

Volume 10 (2017)

Number 1

Lipschitz optimization methods for fitting a sum of damped sinusoids to a series of observations

Pages: 59 – 70



J. W. Gillard (School of Mathematics, Cardiff University, Cardiff, Wales, United Kingdom)

D. E. Kvasov (DIMES, University of Calabria, Rende, Cosenza, Italy; and Lobachevsky State University of Nizhni Novgorod, Russia)


A general nonlinear regression model is considered in the form of fitting a sum of damped sinusoids to a series of non-uniform observations. The problem of parameter estimation in this model is important in many applications like signal processing. The corresponding continuous optimization problem is typically difficult due to the high multiextremal character of the objective function. It is shown how Lipschitz-based deterministic methods can be well-suited for studying these challenging global optimization problems, when a limited computational budget is given and some guarantee of the found solution is required.

2010 Mathematics Subject Classification

Primary 90C26, 93B30. Secondary 90C56.

Published 27 September 2016