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Continuous Nonlinear Optimization for Engineering Applications in GAMS Technology

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概要 This book presents the theoretical details and computational performances of algorithms used for solving continuous nonlinear optimization applications imbedded in GAMS. Aimed toward scientists and gr...aduate students who utilize optimization methods to model and solve problems in mathematical programming, operations research, business, engineering, and industry, this book enables readers with a background in nonlinear optimization and linear algebra to use GAMS technology to understand and utilize its important capabilities to optimize algorithms for modeling and solving complex, large-scale, continuous nonlinear optimization problems or applications. Beginning with an overview of constrained nonlinear optimization methods, this book moves on to illustrate key aspects of mathematical modeling through modeling technologies based on algebraically oriented modeling languages. Next, the main feature of GAMS, an algebraically oriented language that allows for high-level algebraic representation of mathematical optimization models, is introduced to model and solve continuous nonlinear optimization applications. More than 15 real nonlinear optimization applications in algebraic and GAMS representation are presented which are used to illustrate the performances of the algorithms described in this book. Theoretical and computational results, methods, and techniques effective for solving nonlinear optimization problems, are detailed through the algorithms MINOS, KNITRO, CONOPT, SNOPT and IPOPT which work in GAMS technology.続きを見る
目次 1. Introduction
2. Mathematical modeling using algebraically oriented languages for nonlinear optimization
3. Introduction to GAMS technology
4. Applications of continuous nonlinear optimization
5. Optimality conditions for continuous nonlinear optimization
6. Simple bound constraint optimization
7. Penalty and augmented Langrangian methods
8. Penalty-Barrier Algorithm
9. Linearly Constrained Augmented Lagrangian
10. Quadratic programming
11. Sequential quadratic programming
12. A SQP Method using only Equalit Constrained Sub-problem
12. A Sequential Quadratic Programming Algorithm with Successive Error Restoration
14. Active-set Sequential Linear-Quadratic Programming
15. A SQP algorithm for Large-Scale Constrained Optimization
16. Generalized Reduced Gradient with sequential linearization
17. Interior point methods
18. Filter methods
19. Interior Point Sequential Linear-Quadratic Programming
20. Interior Point Filer Line-Search IPOPT
21. Numerical studies.
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本文を見る Full text available from Springer Mathematics and Statistics eBooks 2017 English/International
Full text available from SpringerLink ebooks - Mathematics and Statistics without Lecture Notes (2017)

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登録日 2020.06.27
更新日 2020.06.28