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An Introduction to Optimization
An up-to-date, accessible introduction to an increasingly important field This timely and authoritative book fills a growing need for an introductory text to optimization methods and theory at the senior undergraduate and beginning graduate levels. With consistently accessible and elementary treatment of all topics, An Introduction to Optimization 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-out examples to illustrate both theory and algorithms, this book also This book 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.
- GenresMathematics
409 pages, Hardcover
First published November 17, 1995
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Displaying 1 - 2 of 2 reviews
April 19, 2021
solid
A solid introduction. Good coverage of unconstrained optimization (linear, nonlinear), and constrained optimization (linear and nonlinear) - both over 'continuous' spaces. A solid focus on the centrality of Linear Programming (linear constrained optimization) and the still amazing Simplex Algorithm and the remarkable fact that though it is technically exponential in time in practice it seems to work out ok (polynomial time) in most cases. There is a good introduction to subsequent interior point algorithms for LP - Ellipsoidal and Karmarker. And a description of how these are 'almost' polynomial time. But they require floating point calculations of high precision - hundreds of digits for even small problems. Its kind of fascinating that you seem to be able to start moving from exponential time to polynomial time by moving from integers to reals. And of course reals can be only approximated - though sufficiently well for exact solutions to the original problem
My only criticism is the authors try a bit too hard. The notation is overly elaborate and more detailed than it needs to be. This is the symbolic correlate of 'too much jargon' in word space. Part One seems to be largely a waste of time. This is all covered much better elsewhere. Why repeat the obvious in a rushed way of no use at all to anyone who doesn't already know it.
A solid introduction. Good coverage of unconstrained optimization (linear, nonlinear), and constrained optimization (linear and nonlinear) - both over 'continuous' spaces. A solid focus on the centrality of Linear Programming (linear constrained optimization) and the still amazing Simplex Algorithm and the remarkable fact that though it is technically exponential in time in practice it seems to work out ok (polynomial time) in most cases. There is a good introduction to subsequent interior point algorithms for LP - Ellipsoidal and Karmarker. And a description of how these are 'almost' polynomial time. But they require floating point calculations of high precision - hundreds of digits for even small problems. Its kind of fascinating that you seem to be able to start moving from exponential time to polynomial time by moving from integers to reals. And of course reals can be only approximated - though sufficiently well for exact solutions to the original problem
My only criticism is the authors try a bit too hard. The notation is overly elaborate and more detailed than it needs to be. This is the symbolic correlate of 'too much jargon' in word space. Part One seems to be largely a waste of time. This is all covered much better elsewhere. Why repeat the obvious in a rushed way of no use at all to anyone who doesn't already know it.
January 2, 2023
I think the book needs more examples and some parts are very rushed, but the other parts are great. The exercises are pretty good though.
Displaying 1 - 2 of 2 reviews