# Continuous Nonlinear Optimization for Engineering Applications in GAMS Technology

Part of the Springer Optimization and Its Applications book series (SOIA, volume 121)

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Part of the Springer Optimization and Its Applications book series (SOIA, volume 121)

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 graduate 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.

Lagrangian methods GAMS technology continuous nonlinear optimization nonlinear optimization modeling computational sciences alkylation process Mathematical modeling Penalty-Barrier Algorithm SPENBAR MINOS Linearly Constrained Augmented Lagrangian Quadratic programming Sequential quadratic programming SQP Sequential Linear Quadratic Programming Large-Scale Constrained Optimization Filter methods Interior Point Filter Line Search

- DOI https://doi.org/10.1007/978-3-319-58356-3
- Copyright Information Springer International Publishing AG 2017
- Publisher Name Springer, Cham
- eBook Packages Mathematics and Statistics
- Print ISBN 978-3-319-58355-6
- Online ISBN 978-3-319-58356-3
- Series Print ISSN 1931-6828
- Series Online ISSN 1931-6836
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