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Data fitting with signomial programming compatible difference of convex functions
Name
11081_2022_Article_9717.pdf
Size
1.8 MB
Format
Adobe PDF
Checksum (MD5)
b6ff54fabfdc2825974bf0339569407d
Author(s)
Karcher, Cody J.
Date Issued
April 7, 2022
Publisher
Springer US
Citation
Karcher, Cody J. 2022. "Data fitting with signomial programming compatible difference of convex functions."
Version
Final published version
Abstract
Abstract
Signomial Programming (SP) has proven to be a powerful tool for engineering design optimization, striking a balance between the computational efficiency of Geometric Programming (GP) and the extensibility of more general methods for optimization. While techniques exist for fitting GP compatible models to data, no models have been proposed that take advantage of the increased modeling flexibility available in SP. Here, a new Difference of Softmax Affine function is constructed by utilizing existing methods of GP compatible fitting in Difference of Convex (DC) functions. This new function class is fit to data in log–log space and becomes either a signomial or a set of signomials upon inverse transformation. Examples presented here include simple test cases in 1D and 2D, and a fit to the performance data of the NACA 24xx family of airfoils. In each case, RMS error is driven to less than 1%.
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Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1007/s11081-022-09717-4
