Reviews (5)

garbage
i don't understand why everyone is giving this book such great reviews.as a linear programming student, i find this book extremely difficult to understand, very poorly orgranized, extremely lacking in practical examples to demonstrate the concepts that the author is attempting to describe, and basically a piece of garbage.it was obviously written by a mathematician, not someone capable of teaching.i would not recommend it to anyone.

It's a Keeper
I cut my teeth on this text in George Nemhauser's class. The book is clear and concise and does an excellent job explaining this topic to beginners. I've not come across a better introductory text yet. I still have this book in my reference library.

If you want an introduction to LP, this is the text for you.

Reviews (5)

One of the best...
I first used this book as a text for a graduate course in Integer Programming. At first it seemed to be a very poorly organized book, but as I read it and grew familiar with the subject, I realized that this is a hybrid textbook-handbook. Though the title says "Integer and Combinatorial", the authors go a step ahead and present topics in advanced linear programming, computational complexity, polyhedral theory in a fashion appropriate to the learning of this subject. The exercises are challenging and it has a very good list of references (only up to 1988). Our professor had to supplement the text with recent papers to cover the latest advances. My only complaint is that model formulation could have been dealt with in more detail. You might want to use HP Williams' "Model Building in Mathematical Programming" to look up good Math Prog models.
Though this is an excellent book in all respects, I would recommend Papadimitriou's older book on combinatorial optimization for a good discussion of P, NP problems and decision / optimization problems.

great book
Nemhauser is a great professor and this book is text book for IP. But this book requires that you are familiar with Linear programming. So read this book only after you had LP. Bertsimas book is really good for Lp. Or even Bazaara's book is a goo start.

This book starts with the mathematical basics behind linear programming and develops on these introducing new techniques like Bender's decomposition, various cuts, etc. The way the mathematics is dealt is flawless but I thought the methods required more examples for better understanding. But ofcourse the book had to be concise....

I have no opinion on the combinatorial optimisation part.

Reviews (10)

the best network flow book for computer scientists
I've been using this book as the primary text for my class in
"Network Flow Programming" (senior & graduate level) at the
University of Tennessee for about 10 years.Prior to that time
I had used Jensen & Barnes' Network Flow Programming (now long out
of print).The code in Jensen & Barnes is in FORTRAN (not so fun
or useful for CS majors) and the intended audience seemed to be OR.
Ahuja's code is pascal pseudo-code for the most part, which usually
translates easily into the C language that most of our students
use.

For CS students, there is excellent use of algorithm analysis
(big-O) throughout the book, and there are long discussions
about different approaches and algorithms and the complexity of
each.There is a lot of mathematical notation, but my students
have never had to worry about PDEs and the like here.Any good
advanced CS student (graduate or undergraduate) will find the
book very worthwhile.In my course the students must implement
min-cost spanning trees, shortest paths, critical path/PERT
networks (not in Ahuja), max flow, and min-cost flow.I would
also recommend (for CS majors) Tarjan's excellent (and
succinct) Data Structures and Network Algorithms.

Good Introductory Book
This is a good introductory book. I particularly liked the applications of the problems, introduced in the book. The main negative point that I could mention is, the redundant explanations and discussions you might see in different chapters. I would say the volume of the book could have been reduced by some 15%-20%, if the authors had chosen to be more concise.
The book, Combinatorial Optimization by William J. Cook, et al, is an alternative. It covers a whole lot of topics and is just too succinct, a little more elaboration would have been appreciated !

Reviews (2)

Another classic book
I read Introduction to Algorithms (1990, MIT) and this book is recommended for "further studies" on networks flow and Matroids. However, can anyone tell me is Eugene S Lawler the same person as Eugene L Lawler (a UCB professor)? Or are they two persons?

A good overview of combinatorial optimisation
Combinatorial Optimisation : Networks and Matroids by Eugene Lawler examines shortest paths, network flows, bipartite matching, non bipartite matching. More importantly there is an excellent introduction to matroid theory including matroids and the greedy algorithm, matroid intersections and matroid parity problems, some of these Lawler's own results.

However there is not much on NP completeness, since this book was published in 1976. For a more to date version of events in combinatorial optimisation one might want to look at Papadimitriou and Steglitz's book on combinatorial optimisation (quite old too, considering this was published in 1982), Ahuja, Magnanti and Orlin's book on Network algorithms, Hochbaum's book on approximation algorithms and Cook, Cunnigham,Pulleyblank and Schrijver's book on combinatorial optimisation (listed in the order they were published).

Reviews (2)

A nice book on machine scheduling
Pinedo's book is an excellent reference for almost all problems in machine scheduling.The author is very economic with words and yet the explanations are incredibly clear.I use it for a graduate course in Scheduling that I teach. I often find myself referring to it whenever I need to review some particular class of scheduling problems.There is a chapter on metaheuristics that explains how these methods work - this is a bonus given that the book concentrates on theoretically rigorous methods.What I really like is the fact that step-by-step descriptions of many algorithms are provided, which can be used directly to write computer programs.The interested reader will find results related to structures of many scheduling problems.Overall this is a great book for teaching and research reference purposes.I have read two other texts on scheduling and I must say that this is by far the best.

Isbn: 0471045551
Sales Rank: 716326
Subjects:  1. Business / Economics / Finance    2. Management    3. Production & quality control management   

Reviews (1)

Great Book for NLP (for the mathematically inclined only!)
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 isinterested in nonlinear optimization. The book presents the topic in aclear and concise manner, provides learning aides in form of examples andgenerally has a very well structured layout. I have other books on NLP, butI consider this the best one (Luenberger is great, too - but verycondensed). The book consists of three parts: the first part presentsconvex analysis, the second part looks at optimality conditions and thethird part presents algorithms. If you went through some OR textbooks andfelt that they didn't give you enough on NLP, this is the place to get yourfix!

Reviews (1)

Reviews (2)

Formalism doesn't equal good introduction.
Given that there are not many books in the area of stochastic programmingBirge et al have written a book that will be a necessary reference for thetime being. The first third of the book does provide a good introduction tothe basics of SP but after that a level of formalism dominates that makesone wonder if she is reading from an arcane optimization journal. The latertwo thirds of the book is really nothing more than an amalgam of resultspulled from the literature (journals). As such, little motivation isprovided for the major results that are for the most part just juxtaposedon after another. One wonders why such a journalistic style would be usedfor an introductory text. After all the subject should not be presented asa springer-verlag MATH text in a field like algebraic topology where atheorem-proof format is legimate.Thus, until a better introductory textcomes along that blends more of the practical engineering aspects with thetheory we must be content with the current state of the art.

Reviews (4)

Excellent
If there is anything that I would hold against my favorite Operations Research books - it would be the lack of emphasis on model and structure. Williams' book fills in that gap and is an essential companion to every Math Prog book. It is not a cookbook where one can look up a particular problem and the possible ways to model it. Instead, it takes a systematic and very sensible approach to modeling.
The three chapters on Integer Programming Models are amazingly easy to understand and were a real help during a graduate course in the subject. The huge number of practical examples in Parts 2, 3 and 4 of the book is the real value of the book. I would be hard-pressed for space to describe the range of problems that are modeled in Part 2... Part 3 covers a good deal of discussion on these formulations and Part 4 follows it up with solutions. Though solutions are not discussed in detail, they are a great help for someone who has worked hard through the problems and needs a verification of the solutions.
Another useful section in the book is a chapter on the interpretation of Linear Programming solutions. For a person without a Math Prog background (say, a manager), this kind of material is very useful. In fact, it once served as a good refresher for me in a hurry... and an excellent one at that.
The only sore point is a very limited discussion on nonlinear models.

The Best Book of Its Kind
This is one of the only books I have ever encountered that focuses on the practical aspects of model formulation.This is a frequently overlooked aspect of optimization, but models that are well formulated will often result in superior performance.It is particularly strong in the formulation of mixed-integer problems, with a variety of tips for linearizing variable products and for incorporation of logical constructs.It also shows how to model SOS1 and SOS2 variable types.One other area that I found to be particularly useful was a section covering convexity analysis.This was the only book that I've read that did a good job of explaining the concepts and ramifications of problem convexity.Finally, the examples in the book cover a wide range of practical problems.Most are fairly simple, but do a good job of illustrating important techniques.

I highly recommend this book for linear and mixed-integer modelers.However, if you don't use these types of solvers in your work, the book is less likely to be valuable.

Reviews (1)

Isbn: 0471904139
Sales Rank: 987831
Subjects:  1. Combinatorial optimization    2. Combinatorics    3. Computer Books: General    4. Computers    5. Data Processing - General    6. General    7. Mathematics    8. Optimization (Mathematical Theory)    9. Traveling-salesman problem    10. Computers / Data Processing    11. Mathematical foundations   

Reviews (2)

Excellent presentation of optimization algorithmics
The first chapters deal with economics, really elementary material.However, it is a worthwhile discussion of plant sizing and costing.I liked the later chapters on optimization: HENS, MINLP, scheduling, flexibility in processes, etc.Traditional books on optimization will not help you here because they will not show you how to set up your optimization problem. Systematic Methods does this!The book also contains some discussion of liquid-liquid separations.

As far as content is concerned, the book deserves a 5, but there are problems which reflect its first edition status.The index needs reworking (where is the entry for Fenske's equation?), and there are numerous typographic errors which mar the appearance of the presentation.

The book is not example intensive.The reader is expected to think, and to follow the given example requires paper and pencil, not just casual glances.What should be appreciated is that this book is written by the leaders of the field, and the material is absent in other texts.Much of the text reflects research conducted by the authors.

Reviews (1)

Reviews (1)

Isbn: 0444194517
Sales Rank: 646295
Subjects:  1. Graph theory   

Isbn: 0195042786
Sales Rank: 1426496
Subjects:  1. Computer Books: Operating Systems    2. Data processing    3. Linear Programming    4. Mathematics    5. Numerical Control    6. Applied mathematics   

Reviews (2)

Very good introduction
This book is one of the few devoted to constraint programming, and does a good job of introducing the field to those interested. Optimization problems are finding use of constraint programming and there are a few commercial packages available that implement constraint programming technique in optimization. The book can be used as a textbook of an actual course, since there are many exercises included in it. The authors encourage the reader to use the CLP(R) package, which is freely available, to solve some of the practical exercises.

After a brief introduction to constraint programming, the authors introduce three types of constraints that exist in constraint programming, namely arithmetic, tree, and finite domain. They also introduce three operations involving constraints: satisfiability, simplification, and optimization. The authors spend most of the chapter on the question of satisfiability. Constraints are defined from the standpoint of mathematical logic, along with what it means for them to be satisfiable, and a discussion on modeling with arithmetic constraints and constraint satisfaction is given with an example from electric circuits. Tree constraints are then discussed with an example of a C-language binary tree used to motivate the discussion. Boolean constraints are then discussed, along with sequence constraints, which are shown to have an interesting application to DNA mapping and decoding. An application to artificial intelligence is given, and this one involves constraints that are not taken from mathematics. The authors

finish the chapter with a discussion of constraint solving using local propagation, a technique used in graph theory.

The authors discuss the simplification and optimization of constraints in the next chapter. They show when constraints are redundant, give rules for deciding when one constraint is equivalent to another, and show how using projection can allow the simplifying of a constraint with respect to the variables of interest. When projection cannot be done, they then show how to add variables to a constraint in order to achieve simplification. The (polynomial-time) Dantzig simplex algorithm is discussed for problems with linear real arithmetic constraints. Algorithms are discussed for deciding when two constraints are equivalent or when one implies the other.

In chapter 3, the authors discuss constraint problems for the case where the constraint domain is a finite set. The arc and node consistency, bounds propagation, and integer programming techniques, familiar from AI and operations research, are discussed in detail. The famous N-queens problem is introduced as motivation for the constraint satisfaction problem. The free-ware Prolog package ECLiPSe is introduced in the practical exercises. The authors give references to an interesting application of constraint satisfaction problems to planning gene-splicing experiments (the MOLGEN system).

The next part of the book concerns the constraint logic programming (CLP) paradigm wherein the authors define constraint logic programs and programming techniques. The reader familiar with logic programming (Prolog for example), will clearly see the influence of ideas from that area, such as rules, goals, rewriting, and derivations. An interesting and useful example of applying CLP to the modeling of options trading is given. Also, the authors show how to employ some of the more common data structures, such as lists and binary trees into CLP. In addition, they show how one can measure the efficiency of a CLP program, and how to improve it using various programming techniques to reduce the search space. The authors show how CLP can be implemented for both cases of infinite and finite domain constraints.

In the last part of the book the authors discuss other ways of viewing constraint logic programs, such as thinking it in terms of a database, called a constraint database. The discussion is very interesting, for the authors show how they are generalizations of the standard databases, and they show how the usual evaluation techniques in CLP, such as backtracking, must be generalized if one is to efficiently implement constraint databases. This "bottom-up" evaluation is compared with the "top-down"; approach usually employed. They show in great detail how constraint databases are a natural generalization of relational databases. They also show how CLP can be generalized to the case of concurrent constraint programming, where agents can execute concurrently and communicate via some global constraint in memory. In addition, they give a brief overview of how CLP can be implemented into the functional and imperative programming paradigms. They mention the use of various commercial packages for doing constraint programming, such as Mathematica, Maple, Macsyma, and ILOG SOLVER. Since the time of publication a very powerful commercial package, called ILOG OPL has appeared.

The applications of constraint programming are mushrooming, and I have found it to be a very powerful tool for example in network modeling and simulation, and in mathematical portfolio analysis, although sometimes one must be patient because of performance.The programming methodologies used are different than the usual ones, but I find them to be very effective for program transparency and economy of thought. Others have also apparently found constraint programming to be useful, for example the problem of protein structure prediction has recently made heavy use of constraint programming techniques. Other recent uses of CLP include a system for transport planning and scheduling for a large food industry, a system for a TV/radio company to plan and control the assignment of journalists and technicians to different emissions, and a system to develop work plans and schedules for train drivers and conductors, optimal planning of digital cordless communication systems, and nuclear fuel transportation and scheduling.

Editorial Review

Physics and computer science genius Stephen Wolfram, whose Mathematica computer language launched a multimillion-dollar company, now sets his sights on a more daunting goal: understanding the universe. Wolfram lets the world see his work in A New Kind of Science, a gorgeous, 1,280-page tome more than a decade in the making. With patience, insight, and self-confidence to spare, Wolfram outlines a fundamental new way of modeling complex systems.

On the frontier of complexity science since he was a boy, Wolfram is achampion of cellular automata--256 "programs" governed by simplenonmathematical rules. He points out that even the most complexequations fail to accurately model biological systems, but the simplestcellular automata can produce results straight out of nature--treebranches, stream eddies, and leopard spots, for instance. The graphicsin A New Kind of Science show striking resemblance to thepatterns we see in nature every day.

Reviews (6)

Symbolic computation
This book explains and illustrates the algorithms used by symbolic math packages such as Mathematica, Maple, CoCoA, MatLab, MuPAD,... to solve problems involving polynomials in many variables, and along theway teaches the elements of real algebraic geometry-- most mathematics texts concentrate on the complex-variable version. It is not just for undergraduates; electrical engineers, for instance, should see it.Lots of pictures!

Easiest introduction to Algebraic Geometry
This is the easiest introduction to algebraic geometry and commutative algebra, the authors had done a great job in writing a book that assume very little from the readers. To learn some algebraic geometry, you can either start with this book, or you can spend a year to read a lot of background materials in algebra and then go to a Graduate Text like Harris' book. Of course, if you want to be an expert in algebra, you eventually need a lot of background, what this book can help you is to offer you a quick start, much quicker than you would ever imagine.