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An Introduction to Optimization, 2nd Edition
 
 
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An Introduction to Optimization, 2nd Edition [Hardcover]

Edwin K. P. Chong (Author), Stanislaw H. Zak (Author)
2.4 out of 5 stars  See all reviews (8 customer reviews)


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Hardcover $82.33  
Hardcover, July 27, 2001 --  
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There is a newer edition of this item:
An Introduction to Optimization (Wiley Series in Discrete Mathematics and Optimization) An Introduction to Optimization (Wiley Series in Discrete Mathematics and Optimization) 2.4 out of 5 stars (8)
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Book Description

0471391263 978-0471391265 July 27, 2001 2
A modern, up-to-date introduction to optimization theory and methods
This authoritative book serves as an introductory text to optimization at the senior undergraduate and beginning graduate levels. With consistently accessible and elementary treatment of all topics, An Introduction to Optimization, Second Edition helps students build a solid working knowledge of the field, including unconstrained optimization, linear programming, and constrained optimization.
Supplemented with more than one hundred tables and illustrations, an extensive bibliography, and numerous worked examples to illustrate both theory and algorithms, this book also provides:
* A review of the required mathematical background material
* A mathematical discussion at a level accessible to MBA and business students
* A treatment of both linear and nonlinear programming
* An introduction to recent developments, including neural networks, genetic algorithms, and interior-point methods
* A chapter on the use of descent algorithms for the training of feedforward neural networks
* Exercise problems after every chapter, many new to this edition
* MATLAB(r) exercises and examples
* Accompanying Instructor's Solutions Manual available on request
An Introduction to Optimization, Second Edition helps students prepare for the advanced topics and technological developments that lie ahead. It is also a useful book for researchers and professionals in mathematics, electrical engineering, economics, statistics, and business.

An Instructor's Manual presenting detailed solutions to all the problems in the book is available from the Wiley editorial department.

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Editorial Reviews

Review

"...an excellent introduction to optimization theory..." (Journal of Mathematical Psychology, 2002)

"A textbook for a one-semester course on optimization theory and methods at the senior undergraduate or beginning graduate level." (SciTech Book News, Vol. 26, No. 2, June 2002)

From the Back Cover

A modern, up-to-date introduction to optimization theory and methods

This authoritative book serves as an introductory text to optimization at the senior undergraduate and beginning graduate levels. With consistently accessible and elementary treatment of all topics, An Introduction to Optimization, Second Edition helps students build a solid working knowledge of the field, including unconstrained optimization, linear programming, and constrained optimization.

Supplemented with more than one hundred tables and illustrations, an extensive bibliography, and numerous worked examples to illustrate both theory and algorithms, this book also provides:
* A review of the required mathematical background material
* A mathematical discussion at a level accessible to MBA and business students
* A treatment of both linear and nonlinear programming
* An introduction to recent developments, including neural networks, genetic algorithms, and interior-point methods
* A chapter on the use of descent algorithms for the training of feedforward neural networks
* Exercise problems after every chapter, many new to this edition
* MATLAB(r) exercises and examples
* Accompanying Instructor's Solutions Manual available on request

An Introduction to Optimization, Second Edition helps students prepare for the advanced topics and technological developments that lie ahead. It is also a useful book for researchers and professionals in mathematics, electrical engineering, economics, statistics, and business.

Product Details

  • Hardcover: 496 pages
  • Publisher: Wiley-Interscience; 2 edition (July 27, 2001)
  • Language: English
  • ISBN-10: 0471391263
  • ISBN-13: 978-0471391265
  • Product Dimensions: 9.3 x 6.4 x 1.2 inches
  • Shipping Weight: 1.8 pounds
  • Average Customer Review: 2.4 out of 5 stars  See all reviews (8 customer reviews)
  • Amazon Best Sellers Rank: #800,583 in Books (See Top 100 in Books)

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Customer Reviews

8 Reviews
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4 star:
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3 star:    (0)
2 star:
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1 star:
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Average Customer Review
2.4 out of 5 stars (8 customer reviews)
 
 
 
 
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Most Helpful Customer Reviews

14 of 16 people found the following review helpful:
4.0 out of 5 stars took the class, liked the book, April 29, 1999
Drs. Chong and Zak are Professors of Electrical Engineering at Purdue, and Dr. Chong was the instructor for the ECE grad level optimization class when I took it spring '97. The book alone is good, detailed and rigorous enough for a graduate course without sacrificing readability or in-chapter examples. However, without the MATLAB examples that were developed by the authors to accompany lectures and illustrate each optimization method covered, the material might be a little abstract or dry for self-teaching. An excellent introduction or reference nonetheless, those without a solid base in linear algebra should keep a reference text handy while reading.
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3 of 3 people found the following review helpful:
4.0 out of 5 stars Excellent and concise work on Engineering Linear Programming, October 26, 2010
This review is from: An Introduction to Optimization, 2nd Edition (Hardcover)
This is to all the yahoos bemoaning this work as being terrible.

It's an Engineering class on Linear Programming and Optimization. It's not an Operations Research class on Optimization with Linear Programming and the Simplex Method for Business majors or other Non-Applied Sciences.

Do some research before you take a course with a textbook written and/or taught by a Professor of Electrical Engineering or other engineering discipline.

Having taken this course as an elective during my Mechanical Engineering and Computer Science bachelor degrees I watched my Calculus Professor forcibly pausing and having to stop and restate constantly the work he was trying to teach because it was a Business heavy class.

Off topic:
Washington State University really needs to split the course into two courses and let a grad student teach the basic class for Business Majors and leave the quantitative class for non-business majors who understand Vectors, Linear Algebra, Differential Equations and Multi-Variable Calculus.

Personal Experience:
I personally had a class in Tensor Calculus with a demoted Electrical Engineering Professor who had to move to the Pure and Applied Mathematics department and he could never shut up about how wronged he was but always ignored his past and lack of research that cost him his post.

He was an atrocious professor and his choice in material was garbage. When I asked him to work out his Manifold partial derivatives derivations [how he went from A to Z] he sat for five to ten minutes staring at the board while the 25 students waited. He later turned and told me, ``If you don't understand how to do the derivation then you should not be in this class.''

Did I whine about it on a board about how terrible the course was? No. I told the man I'm paying him to be the professor and prove how he arrived at that result. I then said, if you can't manage that then you are of no use to anyone in this class.

I dropped the class along with about half the other students and he hasn't taught the class since. Retaking Tensor Calculus turned out to be proof that the man was an overbearing braggart who was over his head in teaching this material. When it was taught by a competent person it was like a light bulb going on.

There are excellent works and there are non-excellent works, and then there are nightmares of professors. It's up to the student to determine if it's worth their time to suffer or to cut ties and find a different class with the right combination.

If you don't you'll regret that approach to your university days.

IF YOUR BACKGROUND IS LIGHT IN APPLIED CALCULUS, LINEAR ALGEBRA AND MORE THEN THIS BOOK IS NOT FOR YOU.

It is an excellent book for it's target audience.

Operations Research: An Introduction (8th Edition) by Hamdy A. Taha

http://www.amazon.com/Operations-Research-Introduction-Hamdy-Taha/dp/0131889230/ref=pd_sim_b_5

is an excellent work for one's non-analytically heavy professional life where business and financial analysis is the focus first.

Personally, I learn from both.
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4 of 5 people found the following review helpful:
2.0 out of 5 stars It reads like source code, April 17, 2007
This review is from: An Introduction to Optimization, 2nd Edition (Hardcover)
I'm an undergraduate math major who is using this book in a linear programming course. The general consesus in my class is that this is a very difficult book to comprehend. Everything seems like it's been abstracted to the n-th degree. Variables are frequently used without reference to definitions, which in many cases appear in earlier sections. It's a pain to try to look up something then have to hunt around for the meaning of all the components used in the definition. That's not to say this book isn't informative, it just takes a lot of work to glean useful information from it. As a student, I prefer books that are easy to reference. I simply don't have time to read the whole chapter about the simplex method when I just want to know how to compute cost coefficients.
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Inside This Book (learn more)
First Sentence:
Consider two statements, "A" and "B," which could be either or false. Read the first page
Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
step size gradient algorithm, rank one algorithm, reduced cost coefficients, revised tableau, interior feasible point, affine scaling method, weak duality lemma, strictly interior point, conjugate direction algorithm, basic feasible solution, strict local minimizer, projected gradient algorithm, optimal feasible solution, canonical tableau, ith nutrient, affine scaling algorithm, revised simplex method, corresponding dual problem, feasible solution corresponding, global minimizer, nutrient pills, leading principal minors, steepest descent algorithm, above tableau, tableau corresponding
Key Phrases - Capitalized Phrases (CAPs): (learn more)
Golden Section, Use Exercise, Proof of Condition, Line Fitting
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