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Signomial Programming (SP) has proven to be a powerful tool for engineeringdesign optimization, striking a balance between the computational efficiency ofGeometric Programming (GP) and the extensibility of more general optimizationmethods like Sequential Quadratic Programming (SQP). But when an existingengineering analysis tool is incompatible with the mathematics of the SPformulation, options are limited. Previous literature has suggested schemes forfitting GP compatible models to pre-computed data, but no methods have yet beenproposed that take advantage of the increased modeling flexibility available inSP. This paper describes a new Soft Difference of Max Affine (SDMA) functionclass that is constructed from existing methods of GP compatible fitting andthe theory of Difference of Convex (DC) functions. When a SDMA function is fitto data in log-log transformed space, it becomes either a signomial or a set ofsignomials upon inverse transformation. Three examples of fitting are presentedhere, including simple test cases in 2D and 3D, and a fit to the performancedata of the NACA 24xx family of airfoils. In each case, RMS error is driven toless than 1%.

Data Fitting with Signomial Programming Compatible Difference of Convex Functions