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Fast Compact Algorithms and Software for Spline Smoothing [electronic resource] / by Howard L. Weinert.

By: Contributor(s): Material type: TextTextSeries: SpringerBriefs in Computer SciencePublisher: New York, NY : Springer New York : Imprint: Springer, 2013Description: VIII, 45 p. 7 illus., 5 illus. in color. online resourceContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9781461454960
Subject(s): Additional physical formats: Printed edition:: No titleDDC classification:
  • 519.5 23
LOC classification:
  • QA276-280
Online resources:
Contents:
Introduction -- Cholesky Algorithm -- QR Algorithm -- FFT Algorithm -- Discrete Spline Smoothing.
In: Springer eBooksSummary: Fast Compact Algorithms and Software for Spline Smoothing investigates algorithmic alternatives for computing cubic smoothing splines when the amount of smoothing is determined automatically by minimizing the generalized cross-validation score. These algorithms are based on Cholesky factorization, QR factorization, or the fast Fourier transform. All algorithms are implemented in MATLAB and are compared based on speed, memory use, and accuracy. An overall best algorithm is identified, which allows very large data sets to be processed quickly on a personal computer.
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Introduction -- Cholesky Algorithm -- QR Algorithm -- FFT Algorithm -- Discrete Spline Smoothing.

Fast Compact Algorithms and Software for Spline Smoothing investigates algorithmic alternatives for computing cubic smoothing splines when the amount of smoothing is determined automatically by minimizing the generalized cross-validation score. These algorithms are based on Cholesky factorization, QR factorization, or the fast Fourier transform. All algorithms are implemented in MATLAB and are compared based on speed, memory use, and accuracy. An overall best algorithm is identified, which allows very large data sets to be processed quickly on a personal computer.

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