Overview
- Features innovative perspectives on the simplex algorithm and Krushal-Kuhn-Tucker-John conditions
- Serves as a resource for readers to use the tools of optimization without needing to acquire expertise in the theory
- Features plentiful resources that focus on rigorous definitions, proofs, and case studies
Part of the book series: Synthesis Lectures on Mathematics & Statistics (SLMS)
Access this book
Tax calculation will be finalised at checkout
Other ways to access
About this book
This book presents a short introduction to the main tools of optimization methodology including linear programming, steepest descent, conjugate gradients, and the Karush-Kuhn-Tucker-John conditions. Each topic is developed in terms of a specific physical model, so that the strategy behind every step is motivated by a logical, concrete, easily visualized objective. A quick perusal of the Fibonacci search algorithm provides a simple and tantalizing first encounter with optimization theory, and a review of the max-min exposition of one-dimensional calculus prepares readers for the more sophisticated topics found later in the book. Notable features are the innovative perspectives on the simplex algorithm and Karush-Kuhn-Tucker-John conditions as well as a wealth of helpful diagrams. The author provides pointers to references for readers who would like to learn more about rigorous definitions, proofs, elegant reformulations and extensions, and case studies. However, the book is sufficiently self-contained to serve as a reliable resource for readers who wish to exploit commercially available optimization software without investing the time to develop expertise in its aspects.
This book also:
This book also:
- Features innovative perspectives on the simplex algorithm and Krushal-Kuhn-Tucker-John conditions
- Serves as a resource for readers to use the tools of optimization without needing to acquire expertise in the theory
- Features plentiful resources that focus on rigorous definitions, proofs, and case studies
Similar content being viewed by others
Table of contents (6 chapters)
Authors and Affiliations
About the author
Arthur David Snider, PhD., is a Professor Emeritus in the Departments of Mathematics, Physics, and Electrical Engineering at the University of South Florida. He has 50+ years of experience in modeling physical systems in the areas of heat transfer, electromagnetics, microwave circuits, and orbital mechanics, as well as the mathematical areas of numerical analysis, signal processing, differential equations, and optimization.
Accessibility Information
Accessibility information for this book is coming soon. We're working to make it available as quickly as possible. Thank you for your patience.
Bibliographic Information
Book Title: Basics of Optimization Theory
Authors: Arthur David Snider
Series Title: Synthesis Lectures on Mathematics & Statistics
DOI: https://doi.org/10.1007/978-3-031-29219-4
Publisher: Springer Cham
eBook Packages: Synthesis Collection of Technology (R0), eBColl Synthesis Collection 12
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023
Hardcover ISBN: 978-3-031-29218-7Published: 11 August 2023
Softcover ISBN: 978-3-031-29221-7Published: 12 August 2024
eBook ISBN: 978-3-031-29219-4Published: 10 August 2023
Series ISSN: 1938-1743
Series E-ISSN: 1938-1751
Edition Number: 1
Number of Pages: VIII, 143
Number of Illustrations: 121 b/w illustrations, 59 illustrations in colour
Topics: Optimization, Mathematics, general, Applications of Mathematics, Computational Mathematics and Numerical Analysis, Mathematics of Computing, Algorithms