An Introduction to Optimization, 2nd Edition 2nd Edition by Edwin K. P. Chong (PDF)

Description

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.

User’s Reviews

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. About the Author EDWIN K. P. CHONG, PhD, is Professor of Electrical and Computer Engineering at Colorado State University, Fort Collins, Colorado. He was an Associate Editor for the IEEE Transactions on Automatic Control and received the 1998 ASEE Frederick Emmons Terman Award. STANISLAW H. ZAK, PhD, is Professor in the School of Electrical and Computer Engineering at Purdue University, West Lafayette, Indiana. He was an Associate Editor of Dynamics and Control and the IEEE Transactions on Neural Networks. Read more

Reviews from Amazon users which were colected at the time this book was published on the website:

⭐Very hard to understand, huge lack of problem examples, heck even the text of this book is all mushed together on the pages making it hard on your eyes.

⭐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. Tahahttp://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.

⭐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.

⭐I can only speak on the linear programming section in this book. This is an awful text for undergraduates. This is a math text written by engineers who have a huge case of mathematical rigor-envy. They sacrifice all context, specificity, and practicality in lieu of a ridiculus level of mathematical generality. I am experienced in upper division proofing. I found myself reading and understanding every line of the proofs( of which there are many!) and still having no idea what had just been demonstrated. If you already have a PhD in pure mathematics, then this might be the book for you. If you are an undergraduate, stay away! If you need this book for a linear programming course, do youself a favor and also buy Linear Programming be Vasek Chvatal. The Chvatal text is the premier text on LP. It’s only disadvantage is that it does not cover interior point methods, but this material can be easily supplemented from other sources. If yor are a prof. and are considering using this book for a undergraduate course, don’t. Do your students some good and use a better text.

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