Publication: An AD-Enabled Optimization ToolBox in LabVIEW™
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An AD-Enabled Optimization ToolBox in LabVIEW™

- incollection -
 

Author(s)
Abhishek Kr. Gupta , Shaun A. Forth

Published in
Recent Advances in Algorithmic Differentiation

Editor(s)
Shaun Forth, Paul Hovland, Eric Phipps, Jean Utke, Andrea Walther

Year
2012

Publisher
Springer

Abstract
LabVIEWTM is a visual programming environment for data acquisition, instrument control and industrial automation. This article presents LVAD, a graphically programmed implementation of forward mode Automatic Differentiation for LabVIEW. Our results show that the overhead of using overloaded ad in LabVIEW is sufficiently low as to warrant further investigation and that, within the graphical programming environment, ad may be made reasonably user friendly. We further introduce a prototype LabVIEW Optimization Toolbox which utilizes LVAD’s derivative information. Our toolbox presently contains two main LabVIEW procedures fzero and fmin for calculating roots and minima respectively of an objective function in a single variable. Two algorithms, Newton and Secant, have been implemented in each case. Our optimization package may be applied to graphically coded objective functions, not the simple string definition of functions used by many of the optimizers of LabVIEW’s own optimization package.

Cross-References
Forth2012RAi

BibTeX
@INCOLLECTION{
         Gupta2012AAE,
       title = "An {AD}-Enabled Optimization ToolBox in {LabVIEW}\texttrademark",
       doi = "10.1007/978-3-642-30023-3_26",
       author = "Abhishek Kr. Gupta and Shaun A. Forth",
       abstract = "LabVIEWTM is a visual programming environment for data acquisition, instrument
         control and industrial automation. This article presents LVAD, a graphically programmed
         implementation of forward mode Automatic Differentiation for LabVIEW. Our results show that the
         overhead of using overloaded AD in LabVIEW is sufficiently low as to warrant further investigation
         and that, within the graphical programming environment, AD may be made reasonably user friendly. We
         further introduce a prototype LabVIEW Optimization Toolbox which utilizes LVAD’s
         derivative information. Our toolbox presently contains two main LabVIEW procedures fzero and fmin
         for calculating roots and minima respectively of an objective function in a single variable. Two
         algorithms, Newton and Secant, have been implemented in each case. Our optimization package may be
         applied to graphically coded objective functions, not the simple string definition of functions used
         by many of the optimizers of LabVIEW’s own optimization package.",
       pages = "285--295",
       crossref = "Forth2012RAi",
       booktitle = "Recent Advances in Algorithmic Differentiation",
       series = "Lecture Notes in Computational Science and Engineering",
       publisher = "Springer",
       address = "Berlin",
       volume = "87",
       editor = "Shaun Forth and Paul Hovland and Eric Phipps and Jean Utke and Andrea Walther",
       isbn = "978-3-540-68935-5",
       issn = "1439-7358",
       year = "2012"
}


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