What do you think?
Rate this book
An Introduction to Optimization
Praise for the Third Edition ". . . guides and leads the reader through the learning path . . . [e]xamples are stated very clearly and the results are presented with attention to detail." ―MAA Reviews
Fully updated to reflect new developments in the field, the Fourth Edition of Introduction to Optimization fills the need for accessible treatment of optimization theory and methods with an emphasis on engineering design. Basic definitions and notations are provided in addition to the related fundamental background for linear algebra, geometry, and calculus.
This new edition explores the essential topics of unconstrained optimization problems, linear programming problems, and nonlinear constrained optimization. The authors also present an optimization perspective on global search methods and include discussions on genetic algorithms, particle swarm optimization, and the simulated annealing algorithm. Featuring an elementary introduction to artificial neural networks, convex optimization, and multi-objective optimization, the Fourth Edition also
Introduction to Optimization, Fourth Edition is an ideal textbook for courses on optimization theory and methods. In addition, the book is a useful reference for professionals in mathematics, operations research, electrical engineering, economics, statistics, and business.
Fully updated to reflect new developments in the field, the Fourth Edition of Introduction to Optimization fills the need for accessible treatment of optimization theory and methods with an emphasis on engineering design. Basic definitions and notations are provided in addition to the related fundamental background for linear algebra, geometry, and calculus.
This new edition explores the essential topics of unconstrained optimization problems, linear programming problems, and nonlinear constrained optimization. The authors also present an optimization perspective on global search methods and include discussions on genetic algorithms, particle swarm optimization, and the simulated annealing algorithm. Featuring an elementary introduction to artificial neural networks, convex optimization, and multi-objective optimization, the Fourth Edition also
Introduction to Optimization, Fourth Edition is an ideal textbook for courses on optimization theory and methods. In addition, the book is a useful reference for professionals in mathematics, operations research, electrical engineering, economics, statistics, and business.
- GenresMathematics
622 pages, Hardcover
First published November 17, 1995
4 people are currently reading
51 people want to read
Ratings & Reviews
Friends & Following
Create a free account to discover what your friends think of this book!
Community Reviews
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