Geometric programming: Difference between revisions
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== Introduction == | == 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 == | == Theory/Methodology == | ||
Revision as of 12:03, 20 November 2021
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
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
Subject to: , = 1,...,m,
, = 1,...,p,
, = 1,...,q,
where are posynomial functions, are monomials, and are the optimization variables.
Numerical Examples
Solve a Geometric Programming problem in standard form
Minimize
Subject to: ,
,
,
,
For the problem above, this is a geometric optimization problem in standard form.
The solution is: ...
Transform Nonconvex Optimization Problems to Convex Optimization problem
Reformulate the following non-convex MINLP to a convex one
Minimize
Subject to: ,
,
,
,