Geometric Programming

Geometric programming is a relatively new method of solving a class of nonlinear programming problems compared to general NLP. If the natural formulation of the optimization problem does not lead to posynomial functions, geometric programming techniques can still be applied to solve the problem by r...

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Published inEngineering Optimization p. 1
Main Author Rao, Singiresu S
Format Book Chapter
LanguageEnglish
Published United States John Wiley & Sons 2020
John Wiley & Sons, Incorporated
John Wiley & Sons, Inc
Edition5th Edition
Subjects
Online AccessGet full text
ISBN1119454719
9781119454717
DOI10.1002/9781119454816.ch8

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Abstract Geometric programming is a relatively new method of solving a class of nonlinear programming problems compared to general NLP. If the natural formulation of the optimization problem does not lead to posynomial functions, geometric programming techniques can still be applied to solve the problem by replacing the actual functions by a set of empirically fitted posynomials over a wide range of the parameters. The solution of the unconstrained minimization problem can be obtained by various procedures. The chapter presents two approaches – one based on the differential calculus and the other based on the concept of geometric inequality – for the solution of the problem. Most engineering optimization problems are subject to constraints. If the objective function and all the constraints are expressible in the form of posynomials, geometric programming can be used most conveniently to solve the optimization problem. The chapter considers the solution of the constrained minimization problem.
AbstractList Geometric programming is a relatively new method of solving a class of nonlinear programming problems compared to general NLP. If the natural formulation of the optimization problem does not lead to posynomial functions, geometric programming techniques can still be applied to solve the problem by replacing the actual functions by a set of empirically fitted posynomials over a wide range of the parameters. The solution of the unconstrained minimization problem can be obtained by various procedures. The chapter presents two approaches – one based on the differential calculus and the other based on the concept of geometric inequality – for the solution of the problem. Most engineering optimization problems are subject to constraints. If the objective function and all the constraints are expressible in the form of posynomials, geometric programming can be used most conveniently to solve the optimization problem. The chapter considers the solution of the constrained minimization problem.
Author Rao Singiresu S
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2020 John Wiley & Sons, Inc.
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Snippet Geometric programming is a relatively new method of solving a class of nonlinear programming problems compared to general NLP. If the natural formulation of...
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StartPage 1
SubjectTerms arithmetic‐geometric inequality
complementary geometric programming
constrained geometric programming problem
differential calculus
General Engineering & Project Administration
General References
mixed inequality constraints
posynomial
unconstrained geometric programming program
unconstrained minimization problem
TableOfContents 8.1 Introduction 8.2 Posynomial 8.3 Unconstrained Minimization Problem 8.4 Solution of an Unconstrained Geometric Programming Program Using Differential Calculus 8.5 Solution of an Unconstrained Geometric Programming Problem Using Arithmetic-Geometric Inequality 8.6 Primal-Dual Relationship and Sufficiency Conditions in the Unconstrained Case 8.7 Constrained Minimization 8.8 Solution of a Constrained Geometric Programming Problem 8.9 Primal and Dual Programs in the Case of Less-Than Inequalities 8.10 Geometric Programming with Mixed Inequality Constraints 8.11 Complementary Geometric Programming 8.12 Applications of Geometric Programming References and Bibliography Review Questions Problems
Title Geometric Programming
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