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A Case Study of Computational Differentiation Applied to Neutron Scattering

  • Christian H. Bischof
  • H. Martin Bücker
  • Dieter an Mey
Chapter
  • 318 Downloads

Abstract

In a neutron scattering application, an unconstrained nonlinear minimization problem is used for the fitting of model parameters to experimental data. Automatic differentiation enables, in a completely mechanical fashion, algorithmic changes by switching from a quasi-Newton method, where first order derivatives are approximated by finite differences, to a modified Gauss-Newton method using exact first order derivatives. Compared to the original code, the code generated by this black box approach produces reliable results rather than results of dubious quality. This approach also is faster in terms of execution time. Its performance is improved further by replacing the most time-consuming subroutine involved in the derivative evaluation by a surprisingly simple, hand-coded implementation of the corresponding analytic expression.

Keywords

Execution Time Order Derivative Neutron Scattering Total Execution Time Original Code 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 2002

Authors and Affiliations

  • Christian H. Bischof
  • H. Martin Bücker
  • Dieter an Mey

There are no affiliations available

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