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An Optimization Primer: On Models, Algorithms, and Duality

Autor Lawrence Nazareth
en Limba Engleză Paperback – 18 mai 2004
Optimization is the task of finding the best member of a finite or infinite set of possible choices, based on some objective measure of the merit of each choice in the set. The three key facets of the subject are the art of constructing optimization models, the science of discovering and implementing efficient algorithms for solving optimization models, and the mathematics of optimization models and algorithms. This book provides a very gentle introduction to modeling, algorithms and duality and should appeal to several audiences at once: students (as a supplement to a regular textbook), general readers such as people in business (as an introduction to how optimization affects their everyday lives), and instructors (as a source of ideas for how to teach optimization differently).
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Specificații

ISBN-13: 9780387211558
ISBN-10: 0387211551
Pagini: 108
Ilustrații: XII, 108 p. 1 illus.
Dimensiuni: 155 x 235 x 8 mm
Greutate: 0.24 kg
Ediția:2004
Editura: Springer
Colecția Springer
Locul publicării:New York, NY, United States

Public țintă

Lower undergraduate

Cuprins

1. Simple Motivating Examples.- 1.1 Shopping for Food.- 1.2 Watering the Garden.- 1.3 Chopping Wood.- 1.4 Going Fishing.- 1.5 Summary.- 2. A Quintessential Optimization Problem.- 2.1 Models.- 2.2 Algorithms.- 2.3 Duality.- 2.4 Notes.- 3. Duality on Bipartite Networks.- 3.1 Matching.- 3.2 Covering.- 3.3 König-Egerváry Duality.- 3.4 Notes.- 4. A Network Flow Overview.- 4.1 Problem Reformulations.- 4.2 A Network Flow Tree.- 4.3 Summary: Combinatorial vis-à-vis Continuous.- 4.4 Notes.- 5. Duality in Linear Programming.- 5.1 In the Marketplace.- 5.2 Farkas Duality and LP Optimality.- 5.3 Notes.- 6. The Golden Age of Optimization.- 6.1 Dantzig’s Simplex Algorithm.- 6.2 Linear Programming in Practice.- 6.3 Network Simplex Algorithm.- 6.4 Notes.- 7. An Algorithmic Revolution.- 7.1 Affine Scaling.- 7.2 Central Path.- 7.3 Interior-Point Algorithms.- 7.4 Notes.- 8. Nonlinear Programming.- 8.1 Geometric Perspective.- 8.2 Algebraic Perspective.- 8.3 Information Costs.- 8.4 Dimensions.- 8.5 Constraints.- 8.6 Differentiable Programming.- 8.7 Notes.- 9. DLP and Extensions.- 9.1 A Timber-Harvesting Problem.- 9.2 A Rangeland Improvement Problem.- 9.3 Resource-Decision Software.- 9.4 Notes.- 10. Optimization: The Big Picture.- 10.1 A “Cubist” Portrait.- 10.2 Notes.- References.- About the Author.

Recenzii

From the reviews:
Your latest, An Optimization Primer, is a little masterpiece.  Congratulations!
- George B. Dantzig, Stanford University
Your book looks very good, a particularly nice way to introduce people to the topic.
- James Renegar, Cornell University
"This book provides a very gentle introduction to three key aspects of the optimization field: models, algorithms and duality. … The book will be of interest to college students taking an introductory course in optimization, high school students beginning their studies in Mathematics and Science, and specialists in optimization interested in developing new ways of teaching the subject to their students." (I. M. Stancu-Minasian, Zentralblatt MATH, Vol. 1103 (5), 2007)

 

Textul de pe ultima copertă

Optimization is the art, science and mathematics of finding the "best" member of a finite or infinite set of possible choices, based on some objective measure of the merit of each choice in the set. Three key facets of the subject are:
- the construction of optimization models that capture the range of available choices within a feasible set and the measure-of-merit of any particular choice in a feasible set relative to its competitors;
- the invention and implementation of efficient algorithms for solving optimization models;
- a mathematical principle of duality that relates optimization models to one another in a fundamental way. Duality cuts across the entire field of optimization and is useful, in particular, for identifying optimality conditions, i.e., criteria that a given member of a feasible set must satisfy in order to be an optimal solution.
This book provides a gentle introduction to the above topics and will be of interest to college students taking an introductory course in optimization, high school students beginning their studies in mathematics and science, the general reader looking for an overall sense of the field of optimization, and specialists in optimization interested in developing new ways of teaching the subject to their students.
John Lawrence Nazareth is Professor Emeritus in the Department of Mathematics at Washington State University and Affiliate Professor in the Department of Applied Mathematics at the University of Washington. He is the author of two recent books also published by Springer-Verlag which explore the above topics in more depth, Differentiable Optimization and Equation Solving (2003) and DLP and Extensions: An Optimization Model and Decision Support System (2001).

Caracteristici

Introduces a general audience to the main facets of optimization