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Applied Optimization with MATLAB Programming Hardcover

ISBN-13: 978-0470084885 ISBN-10: 047008488X Edition: 2nd

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Applied Optimization with MATLAB Programming + Engineering Optimization: Theory and Practice + A First Course in Optimization Theory
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Product Details

  • Hardcover: 544 pages
  • Publisher: Wiley; 2 edition (March 23, 2009)
  • Language: English
  • ISBN-10: 047008488X
  • ISBN-13: 978-0470084885
  • Product Dimensions: 9.2 x 6.1 x 1.2 inches
  • Shipping Weight: 1.9 pounds (View shipping rates and policies)
  • Average Customer Review: 3.4 out of 5 stars  See all reviews (7 customer reviews)
  • Amazon Best Sellers Rank: #140,133 in Books (See Top 100 in Books)

Editorial Reviews

From the Back Cover

A new approach to learning classical optimization methods–numerical techniques modeled and illustrated via MATLAB

This unique and timely volume combines a formal presentation of classical methods of design optimization with detailed instruction in the application of these methods using MATLAB. It introduces readers to the symbolic, numerical, and graphic features of MATLAB and integrates this powerful combination in the translation of many algorithms into applied optimization techniques with animation.

Applied Optimization with MATLAB® Programming develops all necessary mathematical concepts, illustrates abstract mathematical ideas of optimization using MATLAB’s rich graphics features, and introduces new programming skills incrementally as optimization concepts are presented. This valuable learning tool:

  • Focuses on real-world optimization techniques
  • Covers all areas of optimization, including linear, nonlinear, discrete, and global
  • Includes creative examples from many disciplines
  • Presents a number of practical, open-ended design problems
  • Features an accompanying Web site with MATLAB code for all the numerical techniques and examples in the book

This one-of-a-kind resource enables senior-undergraduate and graduate students in engineering and other design disciplines to develop practical programming skills as they master the concepts of optimization. It is also an excellent self-teaching guide for design engineers in all fields of endeavor. --This text refers to an alternate Hardcover edition.

About the Author

P. Venkataraman, PhD, is an associate professor in the Mechanical Engineering Department, Rochester Institute of Technology, Rochester, New York.


More About the Author

P. Venkataraman graduated from the Indian Institute of Technology at Kanpur in India in 1974. He worked for several years at the Hindustan Aeronautics Limited in the Helicopter Division. He continued graduate studies at Rice University and obtained his PhD in 1984.

Since then he has been at the Rochester Institute of Technology in the department of mechanical engineering, insisting his students use MATLAB, whatever the course maybe.

His current interests include optimization, Bezier curves, Carnatic music, and mathematics inspired digital art. You can learn more by visiting his site at:

people.rit.edu/pnveme

Customer Reviews

3.4 out of 5 stars
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Most Helpful Customer Reviews

2 of 2 people found the following review helpful By ShadabK on March 4, 2013
Format: Hardcover Verified Purchase
I used this book for a course and the breadth of coverage of Applied Optimization topics is pretty good. The book talks about majority of algorithms and even provides MATLAB code.

Please keep in mind that this is an 'Applied' Optimization text, so if you're expecting rigorous bound proofs, look elsewhere, for example Linear Programming by Bertsekas or Nonlinear Programming by Bertsimas.
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2 of 2 people found the following review helpful By A. N. Bajwa on January 18, 2012
Format: Hardcover Verified Purchase
I have used this book for my graduate course on NLP. One thing should be clear that this book is not an extensive text on optimization but it covers a lot of aspects of optimization. The main contribution of this book is that it teaches you how to use MATLAB for optimization, and it does an excellent job. For those of you who need to develop codes for solving NLP, it will make your life a lot easier. One of the review gives example of a 'bad code'. It is true and I would agree to this, however the codes are working well and are explained very well in the text. I have picked up quite a few of these and have fine tuned them for my applications. the modular structure of the codes is helpful and you can pick sub routines and use them in your coding. While using these codes I learned a lot on using matlab for optimization. simply put its worth it !
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14 of 20 people found the following review helpful By A Customer on January 3, 2002
Format: Hardcover
In this text the author chooses MATLAB as the tool in running computer-based optimization problems. This approach clearly covers all levels of optimization, and the book further supports this coverage through many helpful examples that balance theory with the application. The open-ended problems that are provided are a helpful mechanism for reinforcing the lessons of the text. The website that is a companion to the book, helped me access the reference links to the MATLAB software and the author's own personal site. This web site is a true lifeline to the book.
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5 of 7 people found the following review helpful By Eric on December 23, 2010
Format: Hardcover
This book skips on some key details. For example, the linesearch methods for unconstrained minimization problems is left to a couple poorly-documented Matlab functions. Some discussion of linesearch methods should be considered in the text.

Secondly, the Matlab code is terrible. Consider lines 48-52 of UpperBound_nVar.m:

48: for i = 1:ntrials;
49: j = 0; dela = j*das; a00 = a0 + dela;
50: dx0 = a00*s; x0 = x + dx0; f0 = feval(functname,x0);
51: j = j+1; dela = j*das; a01 = a0 + dela;
52: dx1 = a01*s; x1 = x + dx1; f1 = feval(functname,x1);

Each iteration through the loop, j is initialized to 0. So dela is 0 and a00 = a0. These variables are not used elsewhere. So lines 49-52 could be simply replaced by

x0 = x + a0*s;
x1 = x + (a0 + das)*s
f0 = feval(functname, x + x0);
f1 = feval(functname, x + x1);

This is considerably easier to read than the author's code (4 assignment operations compared to 12). The variables j, dela, a00, dx0, a01, and dx1 never need to be created. Creating these variables makes the algorithm, which is not documented in the text, nearly intractable to read without converting to decent code. My guess is the author translated some ancient Fortran code into Matlab without taking the time to clean it up.

So to summarize:
1. This book skimps on details pertaining to the algorithms it has code for.
2. The code is hard to follow because of the author's programming style.

This book might be useful if you're looking for canned algorithms, but I would first check the Matlab File Exchange. This book is not useful for learning the details of how to implement these algorithms.
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