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Geometric programming

A geometric program (GP) is an optimization problem of the form

Convex form
Geometric programs are not in general convex optimization problems, but they can be transformed to convex problems by a change of variables and a transformation of the objective and constraint functions. In particular, after performing the change of variables y_i = \log(x_i) and taking the log of the objective and constraint functions, the functions f_i, i.e., the posynomials, are transformed into log-sum-exp functions, which are convex, and the functions g_i, i.e., the monomials, become affine. Hence, this transformation transforms every GP into an equivalent convex program. ==Software==
Software
Several software packages exist to assist with formulating and solving geometric programs. • MOSEK is a commercial solver capable of solving geometric programs as well as other non-linear optimization problems. • CVXOPT is an open-source solver for convex optimization problems. • GPkit is a Python package for cleanly defining and manipulating geometric programming models. There are a number of example GP models written with this package here. • GGPLAB is a MATLAB toolbox for specifying and solving geometric programs (GPs) and generalized geometric programs (GGPs). • CVXPY is a Python-embedded modeling language for specifying and solving convex optimization problems, including GPs, GGPs, and LLCPs. ==See also==
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