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Biomedical subjects

A A Goldstein

Publications and source records attributed to A A Goldstein.

7 recordsLinked to original sources
PubMed1988

An efficient method for smoothing indicator-dilution and other unimodal curves.

A mathematical function has been developed for approximating unimodal functions, particularly those which are non-Gaussian, skewed, and incomplete. It is useful as an alternative to cubic splines in smoothing noisy experimental data. Particular applications are to indicator-dilution curves and probability density functions of varied form. In its Fortran implementation, SMOEX, it is computationally inexpensive compared to standard cubic spline smoothing routines and requires less storage to preserve the smoothed function for retrieval or interpolation.

Animals↗
PubMedDate not supplied

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

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.

Capillaries↗
PubMedDate not supplied

GGOPT: an unconstrained non-linear optimizer.

GGOPT is a derivative-free non-linear optimizer for smooth functions with added noise. If the function values arise from observations or from extensive computations, these errors can be considerable. GGOPT uses an adjustable mesh together with linear least squares to find smoothed values of the function, gradient and Hessian at the center of the mesh. These values drive a descent method that estimates optimal parameters. The smoothed values usually result in increased accuracy.

Algorithms↗