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.
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Notes
- 1.
LabVIEWTM is a trademark of National Instruments. This publication is independent of National Instruments, which is not affiliated with the publisher or the author, and does not authorize, sponsor, endorse or otherwise approve this publication.
- 2.
See www.autodiff.org for a list of such tools.
- 3.
- 4.
A further VI (details omitted for brevity) is supplied to set these options within the cluster.
- 5.
Both block diagrams omitted for brevity.
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The authors thank National Instruments for permission to include LabVIEW screenshots.
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Gupta, A.K., Forth, S.A. (2012). An AD-Enabled Optimization ToolBox in LabVIEWTM . In: Forth, S., Hovland, P., Phipps, E., Utke, J., Walther, A. (eds) Recent Advances in Algorithmic Differentiation. Lecture Notes in Computational Science and Engineering, vol 87. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30023-3_26
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DOI: https://doi.org/10.1007/978-3-642-30023-3_26
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