Annals of Biomedical Engineering

, Volume 21, Issue 6, pp 621–631

SENSOP: A derivative-free solver for nonlinear least squares with sensitivity scaling

  • I. S. Chan
  • A. A. Goldstein
  • J. B. Bassingthwaighte


Nonlinear least squares optimization is used most often in fitting a complex model to a set of data. An ordinary nonlinear least squares optimizer assumes a constant variance for all the data points. This paper presents SENSOP, a weighted nonlinear least squares optimizer, which is designed for fitting a model to a set of data where the variance may or may not be constant. It uses a variant of the Levenberg-Marquardt method to calculate the direction and the length of the step change in the parameter vector. The method for estimating appropriate weighting functions applies generally to 1-dimensional signals and can be used for higher dimensional signals. Sets of multiple tracer outflow dilution curves present special problems because the data encompass three to four orders of magnitude; a fractional power function provides appropriate weighting giving success in parameter estimation despite the wide range.


Optimization Curve fitting Indicator dilution Probability density function Sensitivity function Model identifiability Weighting function 


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Copyright information

© Pergamon Press Ltd. 1993

Authors and Affiliations

  • I. S. Chan
    • 1
  • A. A. Goldstein
    • 1
  • J. B. Bassingthwaighte
    • 1
  1. 1.Center for BioengineeringUniversity of WashingtonSeattle

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