Geometric programming

Authors: Wenjun Zhu(wz274), Sam Olsen(sgo23) (SYSEN5800, FALL 2021)

Introduction

A geometric programming (GP) is a family of non-linear optimization problems. Geometric programming optimization problems are typically not convex optimization problems. However, geometric programming optimization problems can be transformed from non-convex optimization problems to convex optimization problems given by their special properties. The convexification for geometric programming optimization problems are implemented by a mathematical transformation of the objective function and constraint functions and a change of decision variables. Geometric programming is generally used to solve and analyze various large scale applications. Typical applications include but are not limited to optimizing power control in communication systems, optimizing doping profile in semiconductor device engineering, optimizing electronic component sizing in IC design, and optimizing aircraft design in aerospace engineering.

Theory/Methodology

Monomial function

A monomial function, or a monomial, is defined as: $f(x)$ = $cx_{1}^{a}$$cx_{1}^{a}$...$cx_{1}^{a}$

The exponents $a_{i}$ of a monomial can be any real numbers, including fractional or negative, but the coefficient $c$ can only be positive.

Posynomial function

A posynomial function, or a posynomial, is defined as: $f(x)$ = $cx_{1}^{a}$

This is a function of sum of monomial functions defined in the above section.

Definition

The standard form of Geometric Programming optimization is to minimize the objective function which must be posynomial. The inequality constraints can only have the form of a posynomial less than or equal to one, and the equality constraints can only have the form of a monomial equal to one.

Standard Form

Minimize $f_{0}(x)$

Subject to: $f_{i}(x)\leqslant 1$, $i$ = 1,...,m,

$g_{i}(x)=1$, $i$ = 1,...,p,

$x_{i}>0$, $i$ = 1,...,q,

where $f_{i}(x)$ are posynomial functions, $g_{i}(x)$ are monomials, and $x_{i}$ are the optimization variables.

Numerical Examples

Solve a Geometric Programming problem in standard form

Minimize $f_{0}(x,y)=x^{2}y^{3}+3x+2xy$

Subject to: $x^{5}+2y^{6}+1\leqslant 1$,

$x+2y\leqslant 1$,

$xy=1$,

$x>0$,

$y>0$

The equivalent standard form GP is as follows:

Minimize $f_{0}(x,y)=x^{2}y^{3}+3x+2xy$

Subject to: $x^{5}+2y^{6}+1\leqslant 1$,

$x+2y\leqslant 1$,

$xy=1$,

$x>0$,

$y>0$

Transform Non-convex Optimization Problems to Convex Optimization problem

Reformulate the following non-convex MINLP to a convex one

Minimize $f_{0}(x_{1},x_{2},x_{3})=16(x_{1})^{2}(x_{2})^{2}+3(x_{2})^{3}(x_{3})^{4}$

Subject to: $x^{5}+2y^{6}+1\leqslant 1$,

$x+2y\leqslant 1$,

$xy=1$,

$x>0$,

$y>0$

Conclusion

In general, geometric programming is a simple but powerful family of non-linear optimization problems. Though geometric programming optimization problems are typically not convex optimization problems, they can be transformed to convex optimization problems by multiple convexification techniques. This makes the optimization problems more tractable. Because of this special property, geometric programming is one of the best way to solve and analyze various large scale applications.