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Nonlinear Programming

Nonlinear Programming

Nonlinear Programming

Concepts, Algorithms, and Applications to Chemical Processes
Lorenz T. Biegler, Carnegie Mellon University, Pennsylvania
October 2010
Hardback
9780898717020
£68.00
GBP
Hardback

    This book addresses modern nonlinear programming concepts and algorithms, especially as they apply to challenging applications in chemical process engineering. It relates the material to real-world problem classes in process optimisation, thus bridging the gap between the mathematical material and the practical uses. Nonlinear Programming: Concepts, Algorithms, and Applications to Chemical Processes shows readers which methods are best suited for specific applications, how large-scale problems should be formulated and what features of these problems should be emphasised, and how existing NLP methods can be extended to exploit specific structures of large-scale optimisation models. The book serves a dual function: it will be useful to chemical engineers who wish to understand and use nonlinear programming; it will also be of interest to experts in mathematical optimisation who want to understand process engineering problems and develop better approaches to solving them.

    • Detailed treatment of state-of-art NLP algorithms
    • Comprehensive development and presentation of dynamic optimization algorithms
    • Suitable as a supplementary text for courses on nonlinear programming

    Product details

    October 2010
    Hardback
    9780898717020
    416 pages
    260 × 840 × 26 mm
    0.89kg
    This item is not supplied by Cambridge University Press in your region. Please contact Soc for Industrial & Applied Mathematics for availability.

    Table of Contents

    • Preface
    • 1. Introduction to process optimization
    • 2. Concepts of unconstrained optimization
    • 3. Newton-type methods for unconstrained optimization
    • 4. Concepts of constrained optimization
    • 5. Newton methods for equality constrained optimization
    • 6. Numerical algorithms for constrained optimization
    • 7. Steady state process optimization
    • 8. Introduction to dynamic process optimization
    • 9. Dynamic optimization methods with embedded DAE solvers
    • 10. Simultaneous methods for dynamic optimization
    • 11. Process optimization with complementarity constraints
    • Bibliography
    • Index.
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