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 in | Engineering Optimization p. 1 |
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Main Author | |
Format | Book Chapter |
Language | English |
Published |
United States
John Wiley & Sons
2020
John Wiley & Sons, Incorporated John Wiley & Sons, Inc |
Edition | 5th Edition |
Subjects | |
Online Access | Get full text |
ISBN | 1119454719 9781119454717 |
DOI | 10.1002/9781119454816.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. |
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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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Copyright | 2020 2020 John Wiley & Sons, Inc. |
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Edition | 5th Edition |
Editor | Rao, Singiresu S |
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PublicationDate | 2020 2019 2019-10-22 |
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PublicationSubtitle | Theory and Practice |
PublicationTitle | Engineering Optimization |
PublicationYear | 2020 2019 |
Publisher | John Wiley & Sons John Wiley & Sons, Incorporated 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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