Presents recent developments of key topics in nonlinear programming using a logical and self-contained format. Divided into three sections that deal with convex analysis, optimality conditions and duality, computational techniques. Precise statements of algorithms are given along with convergence analysis. Each chapter contains detailed numerical examples, graphical illustrations and numerous exercises to aid readers in understanding the concepts and methods discussed.
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Editorial Reviews
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"The promotional message on the back cover proclaims 'this book is a solid reference for professionals and a useful text for students…"; and I fully agree." (Technometrics, February 2007)
"Noted and recommended for its logical format and sharp editing that never wavers in its focus." (Electric Review, September/October 2006)
"…highly recommended for a course in the theory of nonlinear programming…" (MAA Reviews, July 17, 2006)
‘… ‘the Bazaraa’ is a must if you are interested in optimization…’ (Journal of the Operational Research Society, 2007)
From the Publisher
Presents recent developments of key topics in nonlinear programming using a logical and self-contained format. Divided into three sections that deal with convex analysis, optimality conditions and duality, computational techniques. Precise statements of algorithms are given along with convergence analysis. Each chapter contains detailed numerical examples, graphical illustrations and numerous exercises to aid readers in understanding the concepts and methods discussed.
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23 of 25 people found the following review helpful:
5.0 out of 5 stars
Great Book for NLP (for the mathematically inclined only!),
By
This review is from: Nonlinear Programming: Theory and Algorithms, 2nd Edition (Hardcover)
I am referring to the Bazaraa, Sherali and Shetty book "Nonlinear Programming, Theory and Applications", second edition (it seems that Amazon missed the third author).This is a great book for anyone who is interested in nonlinear optimization. The book presents the topic in a clear and concise manner, provides learning aides in form of examples and generally has a very well structured layout. I have other books on NLP, but I consider this the best one (Luenberger is great, too - but very condensed). The book consists of three parts: the first part presents convex analysis, the second part looks at optimality conditions and the third part presents algorithms. If you went through some OR textbooks and felt that they didn't give you enough on NLP, this is the place to get your fix! This book for NLPs together with Dantzig's work on LPs and you have the basic toolset for static optimization.
3 of 3 people found the following review helpful:
5.0 out of 5 stars
One of the best books on NLP at this level,
By
This review is from: Nonlinear Programming: Theory and Algorithms, 2nd Edition (Hardcover)
I am also referring to the 2nd Edition of the book.
I largely agree with review by Marc Sachon except the part about Dantzig's book: if you are new to LP/NLP, or Mathematical Programming in general, stay away from Dantzig's book. Its writing style is entirely outdated and will put you to sleep in no time. Reading from Dantzig to learn about LP is like reading Newton's originals to learn physics/calculus. If you're new to LP / NLP I *strongly* recommend Vanderbei, and THEN this book. This book covers enough ground for fast paced novices and beyond novices. It's mathematical but not rigorous in the strict mathematician's way - for that kind of exposure look elsewhere. It covers a breadth of subjects/issues related to LP / NLP not often found in other books at its level, so in a way it is like a small compendium. It's more up-to-date than say, R. Fletcher's "Practical Methods of Optimization", or Gill, Murray & Wright's "Practical Optimization" both good MSc level books but somewhat dated now and perhaps a bit tedious sometimes. However, if you're a novice, I advise you to look at them also, if you have access to them, as they might serve your specific needs/ reading style better/equally well. You should also look at Luenberger's "Linear and Nonlinear Programming" which is also quite old but has a classic writing style and is holding up rather well. If you want the fine nitty-gritty details and the breadth of coverage though, Bazaraa has more. Luenberger's more solid and rigorous. Haven't had the chance to look at the more recent "Nonlinear Optimization" by Andrzej Ruszczynski but it might be as good/better as he's also an expert in the field - so keep that in mind. "Convex Optimization" by Stephen Boyd is more advanced (not too advanced though, depending on your maths ability) and moving in a slightly different field/teritory.
5.0 out of 5 stars
A very good book,
By
This review is from: Nonlinear Programming: Theory and Algorithms (Hardcover)
This book presents the theory and algorithms of nonlinear programming.
Summaring the contents: Ch1. Introduction Part1 Convex Analysis Ch2. Convex Sets Ch3. Convex, PseudoConvex and QuasiConvex Functions Part2 Optimality Conditions and Duality Ch.4 The Fritz John and Karush-Kuhn-Tucker Optimality Ch. 5 Constraint Qualifications Ch. 6 Lagrangian Duality and Saddle Point Optimality Conditions Part3 Algorithms and Their Convergence Ch. 7 The Concept of an Algorithm (based on Zangwill work) Ch. 8 Unconstrained Optimization (includes something about Trust Region) Ch. 9 Penalty and Barrier Functions Ch. 10 Methods of Feasible Directions Ch. 11 Linear Complementary Problem, Quadratic, Separable, Fractional, and Geometric Programming App. A Mathematical Review App. B Summary of Convexity, Optimality Conditions, and Duality ----------------------------- - Bu this edition as the second edition has several typos. - a very good reference (up to date) for optimization courses.
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Inside This Book
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First Sentence:
The concept of convexity is of great importance in the study of optimization problem. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
improving feasible direction, following line search problem, cyclic coordinate method, dichotomous search method, complementary pivoting algorithm, saddle point optimality conditions, strictly quasiconvex function, line search map, algorithmic map, convergence rate behavior, restricted basis entry rule, termination scalar, strong quasiconvexity, successive linear programming approach, compact polyhedral set, initial extreme point, nonlinear complementary problem, convex simplex method, attainable directions, suitable convexity assumptions, using discrete steps, various constraint qualifications, binding inequality constraints, nonempty open convex set, unbounded optimal solution Key Phrases - Capitalized Phrases (CAPs): (learn more)
Fritz John, Theorem Let, Proof Let, Definition Let, Initialization Step Choose, Lemma Let, Proof Suppose, Initialization Step Let, Example Minimize, Proof By Theorem, Hessian of the Lagrangian, Proof First, Armijo's Rule, Lemma Consider, Proof Note, Initialization Step Find, Lemma Suppose, Line Search Starting, Proof Consider, Proof Assume, Theorem Suppose, Corollary Let, Examples Example New!
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Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
The concept of convexity is of great importance in the study of optimization problem. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
improving feasible direction, following line search problem, cyclic coordinate method, dichotomous search method, complementary pivoting algorithm, saddle point optimality conditions, strictly quasiconvex function, line search map, algorithmic map, convergence rate behavior, restricted basis entry rule, termination scalar, strong quasiconvexity, successive linear programming approach, compact polyhedral set, initial extreme point, nonlinear complementary problem, convex simplex method, attainable directions, suitable convexity assumptions, using discrete steps, various constraint qualifications, binding inequality constraints, nonempty open convex set, unbounded optimal solution Key Phrases - Capitalized Phrases (CAPs): (learn more)
Fritz John, Theorem Let, Proof Let, Definition Let, Initialization Step Choose, Lemma Let, Proof Suppose, Initialization Step Let, Example Minimize, Proof By Theorem, Hessian of the Lagrangian, Proof First, Armijo's Rule, Lemma Consider, Proof Note, Initialization Step Find, Lemma Suppose, Line Search Starting, Proof Consider, Proof Assume, Theorem Suppose, Corollary Let, Examples Example New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Citations (learn more)
This book cites 20
books:
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85
books
cite this book:
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- Mathematics of the Decision Sciences: Part 2 (Lectures in Applied Mathematics) (Pt. 2) by George Bernard Dantzig on 4 pages
- Numerical Methods for Constrained Optimization by P. Gill in Back Matter (1), and Back Matter (2)
- Optimization in Action by L.C.W. Dixon in Back Matter (1), and Back Matter (2)
- Computational Methods in Optimization: A Unified Approach (Mathematics in Science and Engineering Ser. : Vol 77) by Author Unknown in Back Matter
- Performance Evaluation of Numerical Software: I.F.I.P.Conference Proceedings, 1978 by Lloyd D. Fosdick in Back Matter
See all 20 books this book cites
- Engineering Optimization: methods and applications by A. Ravindran on page 254, page 373, and page 442
- Engineering Optimization: Methods and Applications by G. V. Reklaitis on page 211, page 343, and page 431
- Statistical Quality Control by M. Jeya Chandra on page 48, and page 79
- Aspects of Semidefinite Programming: Interior Point Algorithms and Selected Applications (Applied Optimization) by E. de Klerk in Front Matter, and Back Matter
- Multiple Criteria Decision Making: Proceedings of the Twelfth International Conference Hagen (Germany) (Lecture Notes in Economics and Mathematical Systems) by Günter Fandel on page 358
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