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Springer Optimization and Its Applications

Practical Mathematical Optimization

Basic Optimization Theory and Gradient-Based Algorithms

Authors: Snyman, Jan A, Wilke, Daniel N

  • Guides readers to understand processes and strategies in real world optimization problems
  • Contains new material on gradient-based methods, algorithm implementation via Python, and basic optimization principles
  • Covers fundamental optimization concepts and definitions, search techniques for unconstrained minimization and standard methods for constrained optimization
  • Includes example problems and exercises 
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eBook 55,92 €
price for Spain (gross)
  • ISBN 978-3-319-77586-9
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover 69,67 €
price for Spain (gross)
  • ISBN 978-3-319-77585-2
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
  • The final prices may differ from the prices shown due to specifics of VAT rules
About this Textbook

This textbook presents a wide range of tools for a course in mathematical optimization for upper undergraduate and graduate students in mathematics, engineering, computer science, and other applied sciences.  Basic optimization principles are presented with emphasis on gradient-based numerical optimization strategies and algorithms for solving both smooth and noisy discontinuous optimization problems. Attention is also paid to the difficulties of expense of function evaluations and the existence of multiple minima that often unnecessarily inhibit the use of gradient-based methods. This second edition addresses further advancements of gradient-only optimization strategies to handle discontinuities in objective functions. New chapters discuss the construction of surrogate models as well as new gradient-only solution strategies and numerical optimization using Python. A special Python module is electronically available (via springerlink) that makes the new algorithms featured in the text easily accessible and directly applicable. Numerical examples and exercises are included to encourage senior- to graduate-level students to plan, execute, and reflect on numerical investigations. By gaining a deep understanding of the conceptual material presented, students, scientists, and engineers will be  able to develop systematic and scientific numerical investigative skills.

 

About the authors

Jan A. Snyman currently holds the position of emeritus professor in the Department of Mechanical and Aeronautical Engineering of the University of Pretoria, having retired as full professor in 2005. He has taught physics, mathematics and engineering mechanics to science and engineering students at undergraduate and postgraduate level, and has supervised the theses of 26 Masters and 8 PhD students. His research mainly concerns the development of gradient-based trajectory optimization algorithms for solving noisy and multi-modal problems, and their application in approximation methodologies for the optimal design of engineering systems. He has authored or co-authored 89 research articles in peer-reviewed journals as well as numerous papers in international conference proceedings.

Daniel N. Wilke is a senior lecturer in the Department of Mechanical and Aeronautical Engineering of the University of Pretoria.   He teaches computer programming, mathematical programming and computational mechanics to science and engineering students at undergraduate and postgraduate level. His current research focuses on the development of interactive design optimization technologies, and enabling statistical learning (artificial intelligence) application layers, for industrial processes and engineering design. He has co-authored over 50 peer-reviewed journal articles and full length conference papers.

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Table of contents (9 chapters)

  • INTRODUCTION

    Snyman, Jan A. (et al.)

    Pages 3-40

    Preview Buy Chapter 30,19 €
  • LINE SEARCH DESCENT METHODS FOR UNCONSTRAINED MINIMIZATION

    Snyman, Jan A. (et al.)

    Pages 41-69

    Preview Buy Chapter 30,19 €
  • STANDARD METHODS FOR CONSTRAINED OPTIMIZATION

    Snyman, Jan A. (et al.)

    Pages 71-112

    Preview Buy Chapter 30,19 €
  • BASIC EXAMPLE PROBLEMS

    Snyman, Jan A. (et al.)

    Pages 113-167

    Preview Buy Chapter 30,19 €
  • SOME BASIC OPTIMIZATION THEOREMS

    Snyman, Jan A. (et al.)

    Pages 169-193

    Preview Buy Chapter 30,19 €

Buy this book

eBook 55,92 €
price for Spain (gross)
  • ISBN 978-3-319-77586-9
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover 69,67 €
price for Spain (gross)
  • ISBN 978-3-319-77585-2
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
  • The final prices may differ from the prices shown due to specifics of VAT rules
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Bibliographic Information

Bibliographic Information
Book Title
Practical Mathematical Optimization
Book Subtitle
Basic Optimization Theory and Gradient-Based Algorithms
Authors
Series Title
Springer Optimization and Its Applications
Series Volume
133
Copyright
2018
Publisher
Springer International Publishing
Copyright Holder
Springer International Publishing AG, part of Springer Nature
eBook ISBN
978-3-319-77586-9
DOI
10.1007/978-3-319-77586-9
Hardcover ISBN
978-3-319-77585-2
Series ISSN
1931-6828
Edition Number
2
Number of Pages
XXVI, 372
Number of Illustrations and Tables
64 b/w illustrations, 17 illustrations in colour
Topics