  An approximate formulation of a robust geometric program (RGP) as a convex
program is proposed. Interest in using geometric programs (GPs) to model
complex engineering systems has been growing, and this has motivated explicitly
modeling the uncertainties fundamental to engineering design optimization. RGPs
provide a framework for modeling and solving GPs while representing their
uncertainties as belonging to an uncertainty set. The RGP methodologies
presented here are based on reformulating the GP and then robustifying it with
methods from robust linear programming. These new methodologies, along with
previous ones from the literature, are used to robustify two aircraft design
problems, and the results of these different methodologies are compared and
discussed.
