Amazon.com
"Intended as an upper-level undergraduate or introductory graduate text in computer science theory," this book lucidly covers the key concepts and theorems of the theory of computation. The presentation is remarkably clear; for example, the "proof idea," which offers the reader an intuitive feel for how the proof was constructed, accompanies many of the theorems and a proof. Introduction to the Theory of Computation covers the usual topics for this type of text plus it features a solid section on complexity theory--including an entire chapter on space complexity. The final chapter introduces more advanced topics, such as the discussion of complexity classes associated with probabilistic algorithms.
Book Description
This highly anticipated revision builds upon the strengths of the previous edition. Sipser's candid, crystal-clear style allows students at every level to understand and enjoy this field. His innovative "proof idea" sections explain profound concepts in plain English. The new edition incorporates many improvements students and professors have suggested over the years, and offers updated, classroom-tested problem sets at the end of each chapter.
Customer Reviews:
My choice for textbook in my computation theory class.......2007-10-01
I recently encountered this book at a publisher's booth at a computer conference and read it on the ride back home. This morning I made a trip to the college bookstore and notified them that it is the textbook that I will be using in my computation theory class this spring.
The chapter titles are:
0) Introduction - this chapter contains the fundamental mathematical background of sets, functions, graphs and proofs. For most students, it could be skipped or skimmed.
1) Regular languages - this chapter is an introduction to deterministic and nondeterministic finite automata and regular expressions.
2) Context-free languages - an introduction to context-free grammars and pushdown automata.
3) The Church-Turing theses - an introduction to Turing machines and the variants, such as multiple tapes and nondeterministic Turing machines.
4) Decidability - the definition of decidability and how Turing machines and finite automata are used to prove or disprove if a language is decidable.
5) Reducibility - the definition of reducible and how Turing machines can be used to execute reductions.
6) The recursion theorem - an introduction to the recursion theorem and some applications to formal theories.
7) Time complexity - the first chapter in the coverage of algorithmic complexity, in this case execution time.
8) Space complexity - an examination of the complexity of algorithms from the perspective of the amount of memory required.
9) Intractability - an examination of the problems that can be solved in principle but not in practice.
10) Advanced topics in complexity theory - approximation algorithms, probabilistic algorithms, alternation, interactive proof systems, parallel computation and cryptography.
There is less coverage of grammars than most books, which is replaced by more in the area of algorithmic analysis. In my opinion, that is an appropriate tradeoff, the analysis of algorithms gives the students some understanding of how automata are applied in computer science.
Another excellent feature of this book is the solutions to selected exercises that appear at the end of the chapters. My estimate is that reasonably detailed solutions to approximately one-third of the problems are included. This allows the students to work extra problems by themselves, and helps the instructor if they are asked to do another example in class that they have not already worked through.
The exposition is very good; I am convinced that the students will be able to read the material on their own, which is one more reason why I adopted this book for my course.
well-organized, progressive, and understandable.......2007-01-06
As an intro to the theoretical background to computer science goes, this book is about as readable and approachable as you can get.
It gives a very thorough treatment of the whole theoretical basis, from regular languages and pumping lemmas out through Turing machines and related issues, and on to some interesting language classes (like NP and PSpace-complete).
If there's a single sticking point with the book, it's that it insists on a very strict formalism (ie: everything is proof-based) -- something necessary for the topic, but it sometimes renders the material a bit hard to digest.
Great book on the subject.......2006-12-27
If you are interested in or for other reasons must read a book on this subject, this is the book. I took a class last semester which used Hopcroft as the text and I found myself often turning to this book for better understanding. This book is more intuitive and thus a bit less formal than Hopcroft but when trying to learn, understanding is better than mathematical formalism. If you are new to the subject, Sipser is the book to begin with.
Very readable, diverse, and a little sparse.......2006-11-25
This is a wonderful little gem of a book that presents the theory of computation in a fascinating way. It is targeted at advanced undergraduates in computer science, but assumes remarkably little prior knowledge, making it accessible to nearly anyone. The book covers a lot of ground, including the standard fare of automata, computability, and complexity results, plus some bonus material such as probablistic and parallel complexity, information theory, decidable logical theories, and other topics that are normally left out of introductory books. On top of this, the book is remarkably thin!
The best attribute of Sipser's book, though, is the engaging style. This is an easy book to read. You will not feel like you're running into a brick wall, as is sometimes the case with books on abstract topics. It's not so much that the book is slow or gentle (it's really not) as that it is interesting, engaging, and has a knack for stopping short of getting too caught up in details. A number of small things -- the occasional amusing exercise, the "proof idea" sections, or helpful pictures -- add up to an enjoyable reading experience.
Two cautions are appropriate to students considering this book. First, there are variations between authors in the definitions of various automata (especially PDAs). The differences are trivial, and more a matter of taste than of any real importance; but it could come up if you use Sipser as a supplement to a course that follows a different textbook. Second, the coverage of many topics in Sipser's book is brief and concise, sometimes more than you might like. Some important concepts (for example, pairwise distinguishability of strings) are only mentioned in exercises, not in the main chapter, so at least skim all the exercises even if you don't do them. The sketchy coverage is especially pronounced in advanced topics, so (as always) expect to do some filling in of concepts if you go on into further study of this area.
Most appropriate for CS students.......2006-06-01
As a teacher of the subject, I have had the chance to evaluate numerous books on the theory of computation. Of all the available texts, I think this one is the most appropriate for CS students. In the past I taught out of Dexter Kozen's book, which is incredibly elegant, but had some resistance from the students. Thinking it over I decided that Kozen's text, although beautiful, may be better suited to students pursuing a degree in pure math. Sipser's book, on the other hand, is more gentle. I find that Sipser demands far less mathematical maturity from his readers, and thus allows the difficulty to be shifted from excessive formalism to the inherent challenges present in the material. In addition, following Sipser's treatment, I was able to cover finite state machines and pushdown automata in far less time, thus allowing me to concentrate on computability and beyond. The book really shines in its treatment of computability theory, eloquently directing attention to some of the most beautiful aspects.
Another benefit of Sipser's book is the exercises, of which there are many more in this edition. Someone studying on their own should find the initial group of exercises in each section quite approachable. Even the more challenging problems are not incredibly hard, and typically draw their difficulty from the deeper themes of the chapter instead of obscure details.
If you are looking for an enjoyable, well-paced book with an introduction to computability and complexity that is truly inspiring, this is the one for you. A mathematician looking for a bit more rigor may do better with Kozen.
Book Description
This book provides the first clear, comprehensive, and accessible account of complex adaptive social systems, by two of the field's leading authorities. Such systems--whether political parties, stock markets, or ant colonies--present some of the most intriguing theoretical and practical challenges confronting the social sciences. Engagingly written, and balancing technical detail with intuitive explanations, Complex Adaptive Systems focuses on the key tools and ideas that have emerged in the field since the mid-1990s, as well as the techniques needed to investigate such systems. It provides a detailed introduction to concepts such as emergence, self-organized criticality, automata, networks, diversity, adaptation, and feedback. It also demonstrates how complex adaptive systems can be explored using methods ranging from mathematics to computational models of adaptive agents.
John Miller and Scott Page show how to combine ideas from economics, political science, biology, physics, and computer science to illuminate topics in organization, adaptation, decentralization, and robustness. They also demonstrate how the usual extremes used in modeling can be fruitfully transcended.
Customer Reviews:
Annie Wu -- Book #1.......2007-08-10
I am a purchasing agent who buys books for my faculty, and as far as I know, this faculty member is very impressed with this particular book.
The Emergence of Convergence .......2007-08-04
At the time of writing this review, this book isn't searchable through Amazon, that's too bad because if you're reading the reviews wondering if it's worth buying, just browsing through any page from the intro or appendix B would clearly resolve any remnant hesitation. This book is a must have for anyone even remotely interested in complex adaptive systems. Scott Page and John Miller dress the landscape and state of the art of computational social science, the issues are motivated from the ground up and the existing approaches to resolve them explicitly detailed, yet using clear and jargon free language. For example, descriptions of the many concepts repeatedly used in the scientific method (of CAS et al) such as ergodicity or optimization theory are refreshing and insightful, simply stuff you don't get from textbooks, but rather that one would learn over years of experience doing.
In summary, the authors are handing us an expert summary of literature and developments of a complex field in a concise, fun and delightful read, it would be a shame to miss it.
Amazon.com
This book is a rigorous exposition of formal languages and models of computation, with an introduction to computational complexity. The authors present the theory in a concise and straightforward manner, with an eye out for the practical applications. Exercises at the end of each chapter, including some that have been solved, help readers confirm and enhance their understanding of the material. This book is appropriate for upper-level computer science undergraduates who are comfortable with mathematical arguments.
Book Description
This classic book on formal languages, automata theory, and computational complexity has been updated to present theoretical concepts in a concise and straightforward manner with the increase of hands-on, practical applications. This new edition comes with Gradiance, an online assessment tool developed for computer science.
Gradiance is the most advanced online assessment tool developed for the computer science discipline. With its innovative underlying technology, Gradiance turns basic homework assignments and programming labs into an interactive learning experience for students. By using a series of âroot questionsâ and hints, it not only tests a student’s capability, but actually simulates a one-on-one teacher-student tutorial that allows for the student to more easily learn the material. Through the programming labs, instructors are capable of testing, tracking, and honing their students’ skills, both in terms of syntax and semantics, with an unprecedented level of assessment never before offered.
Customer Reviews:
A Butchered Classic.......2007-09-28
I've heard that the first edition of this book is a classic. Reading the second edition, I can kind of see that -- occasionally there will be a stretch of 5 pages or so that is wonderfully clear, concise, and informative.
But overall, this edition is a disappointment. The explanations tend to be mechanical and unhelpful, and are sometimes confused or just incorrect. New sections on mathematical foundations and applications have been added, but there isn't really adequate space devoted to covering either topic, and the results are so rushed and lacking in context that I can't see those sections being useful to anyone who would need them in the first place. Finally, this edition needs to be proofread for correctness! It contains numerous mistakes, some of them in the presentations of key proofs.
Updated Classic Text.......2007-08-29
The previous edition of this text was published in the late 70's (1979), and it was still in use today in many schools and Universities across the world. For good reason too, the authors of this text really nail down the concept of computability as we understand it today. It is very difficult to find an undergraduate curriculum that does not include a course in Computability or theory of computation, and that is certainly a change from a couple of decades ago where this type of study was left to the Graduate level curricula. What this means to the reader is that one can not be a Computer Scientist without understanding the concepts and theory behind what computability really means.
Things like Context Free languages and grammar are used readily in things like XML and its accompanying standards such as the DTD. So, it makes sense to update a classic text to include such topics and further illustrate to the reader that what once was a theory is now center stage of Computer Science and the IT industry as a whole.
The text starts with the classics such as an introduction to automata theory followed by languages. The authors have taken a more relaxed approach to the topics as the proofs are less formal and easier to follow. Plain text is usually used to informally proof the topic at hand, and the authors go into a more formal approach on selected proofs. This is definitely a better approach than the other texts in the same topic that proofs are center stage of the discussion and the reader gets lost early on in the process. The text is easy to read for students, and easy to explain for the instructors. I remember when I took theory of Computation for my graduate work proofs were so convoluted and difficult to read that I had to spend many of nights trying to understand what the instructor was talking about in the class.
As one would expect, the book then goes into Turning Theory and Machine with the concept to computability and complexity. Well, the good news is that the authors' approach to the topic does not change; lots of explaining of the basics followed by a more detailed formal approach to the topic. All I need to say is that I wish my text was this reader friendly! Chapter 8, Introduction to Turing Machines, sets the ground work for the rest of the text. It explains reducibility and more importantly how to reduce a problem, something I have never seen in any other text in such detail! Automata and its relation to Turing Machine is depicted in detail, so there is no gap between the topics. What is interesting is that the authors close the loop with actually talking about, for example the Halting problem, in the real world with a program.
As one would expect, different classes of problems are explored in detail with many examples (theory and real-world examples) that accompany the topic at hand. Each chapter ends with a summary of topics discussed followed by a set of exercises. There are also a number of exercises at the end of each section in a given chapter in order to reel-in the topic for the reader.
All and all, this is one great text on automata and computation theory. It is easy to read and follow for the students without the loss of content. The authors relate abstract concepts to real-world examples to further illustrate the importance of the topic at hand.
Good, but just it.......2007-06-27
A good book, but just it.
It's like a normal book. It's not bad but not excellent...
Automata theory. The heart of Computer Science.......2007-04-06
Excellent book. Nothing to say for this one.
Eh... Whatever..........2007-01-21
Uhm... I had to buy this book because it was a required text for a required course. Who would buy a book like this otherwise? Duh!
Average customer rating:
- No Ackermann function
- Needlessly cryptic; too clever for its own good
- Great math, bad writing.
- Content left as an exercise
- A good textbook
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Elements of the Theory of Computation (2nd Edition)
Harry R. Lewis , and
Christos H. Papadimitriou
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ASIN: 0132624788 |
Book Description
Lewis and Papadimitriou present this long awaited Second Edition of their best-selling theory of computation. The authors are well-known for their clear presentation that makes the material accessible to a a broad audience and requires no special previous mathematical experience.
In this new edition, the authors incorporate a somewhat more informal, friendly writing style to present both classical and contemporary theories of computation. Algorithms, complexity analysis, and algorithmic ideas are introduced informally in Chapter 1, and are pursued throughout the book. Each section is followed by problems.
Customer Reviews:
No Ackermann function.......2007-10-01
Computability: An Introduction to Recursive Function Theory I have a better book that gives a better introduction to this field. I have read several books (older and newer) along these lines.
While this book gives a bad introduction to the tiling problem,
it ignores what is pretty much a "standard" problem in
modern texts : the Ackermann recursion. The text takes the Mathematical high road and leaves most of the human race out in the cold by lack of good illustrations and explanations.
If I were teaching the course in computation , I might tell students to look this book up in the library, but never make them
spend this much for a text that will fail all but the top 5 % of students.
Needlessly cryptic; too clever for its own good.......2007-06-30
This book claims to "make the essentials of the subject accessible to a broad undergraduate audience in a way that is mathematically sound but presupposes no special mathematical experience." On this count, the book fails miserably. The book starts easily enough with an introduction to sets and languages, but by the time Chapter 3 rolls around, the writing degenerates into the hectoring style of Russell and Whitehead... pages and pages of non-intuitive academic proofs, and making simple concepts needlessly complex in the name of Formality. The concepts the authors are presenting are fascinating, but in order to get to them you have to spend way too much time shuffling symbols. By the time we've made it to Chapters 6 and 7 (the good ones) we've lost most of the students in the muddy bootcamp of Chapters 3 and 4.
Don't buy this book, and don't use it to teach; the Sipser book is the way to go.
Great math, bad writing........2007-06-10
I read this book while taking a bachelor level course in computer science. I am not many many years beyond that degree and thought it would be nice to reflect on it as a working professional.
I now understand the math much better, and I can now somewhat read through this book... However, when I recall my days as a student, this book simply did not serve well as a text book (nor does it serve as recreational reading).
The author obviously knows his set theory and discrete mathematics... The writing is just so poor and hard to read that it makes the book relatively worthless.
Good books take a (potentially) complicated subject and make it easy to understand. The subject may look complicated, but it really isn't that hard to grasp once you develop a general understanding of it. If it isn't explained well, then the subject matter seems to be written in a different language. That is what happens with this book.
Explanations are often given a line or two before the author continues to build upon the material. Similarly to a calculus course, the information you just learned will be used in a more advanced manner, increasing in complexity as you move forward. That is what happens here. The problem is that the few sentences aren't always enough to "get" or understand the material. If you don't quite grasp what is happening, then you immeidately become lost as the author moves on.
The examples aren't always that helpful, and the information is just presented in a non reader-friendly fashion that it is exceptionally easy to get lost and lose your way...
The book has the potential to be very good... but it would probably take 600 pages of writing instead of 300. If the author spent more time "hammering" in the facts of the topic, it would be way more effective as a learning tool..
Content left as an exercise.......2005-06-08
I had the "pleasure" of being exposed to this nightmare of a book in a bachelor level course. I am told that it is normal to use this book on masters-degree level, so maybe it's because I wasn't "prepared" enough for this book that my take on it is so negative. The book does have it's moments, where things are understandable, but thats mainly if the stuff is easy. There is a lack of explenations throughout the entire book. It seems the author(s) view of "explenation" is the words "it is easy to see..." or "left as an exercise"....The proof of my feelings toward this book, can be seen on the teeth-marks that decorate the books cover. Im not writing this, because I need to vent steam...I passed the course on the first try, and it is now behind me, but I advice anyone faced with this book, to seek alternatives..this book is not a teaching book, it is a telling book.
The best way to describe the book, is if you imagine a book, that on page 2 reads "content left as an exercise"...and then its blank for the rest.
A good textbook.......2005-04-25
I taught a couple of classes from the first edition of this textbook, and my students did fairly well. On the whole, they were able to understand the material and solve the homework problems. I certainly wouldn't mind teaching a class on this subject from the second edition as well, which I feel is a mild improvement over the first one.
The chapter on finite automata is excellent. And the material on context-free languages is thorough and well written. So is the introduction to Turing machines.
Of course, the book then spends a fair amount of time on recursive function theory. That is exactly what I want it to do. And I think the chapter on unsolvability, starting with the Halting Problem, is excellent.
The style, especially of the first edition, is a little formal. But this is serious mathematical material, and I think it is not asking too much to require students to handle this subject in such a manner.
Book Description
Agent-based computational modeling is changing the face of social science. In Generative Social Science, Joshua Epstein argues that this powerful, novel technique permits the social sciences to meet a fundamentally new standard of explanation, in which one "grows" the phenomenon of interest in an artificial society of interacting agents: heterogeneous, boundedly rational actors, represented as mathematical or software objects. After elaborating this notion of generative explanation in a pair of overarching foundational chapters, Epstein illustrates it with examples chosen from such far-flung fields as archaeology, civil conflict, the evolution of norms, epidemiology, retirement economics, spatial games, and organizational adaptation. In elegant chapter preludes, he explains how these widely diverse modeling studies support his sweeping case for generative explanation.
This book represents a powerful consolidation of Epstein's interdisciplinary research activities in the decade since the publication of his and Robert Axtell's landmark volume, Growing Artificial Societies. Beautifully illustrated, Generative Social Science includes a CD that contains animated movies of core model runs, and programs allowing users to easily change assumptions and explore models, making it an invaluable text for courses in modeling at all levels.
Customer Reviews:
Annie Wu -- Book #2.......2007-08-10
I am a purchasing agent who buys books for my faculty, and as far as I know, this faculty member is very impressed with this book.
Excellent example of cross-disciplinary social science using theory.......2007-08-07
It's refreshing and exciting, in a quiet intellectual kind of way, to encounter a book that includes philosophy of science, music theory, Anasazi disappearance mysteries, ethnic cleansing, and an explanation of why CEOs exist. Josh has produced the book I've been wanting to read any time during the last 20 years, which have been a bit barren from the theory and modeling perspective in social science. He also makes clear the mathematical and philosophical basis of the agent-based approach, producing a baseline both for future work in the field and for competing paradigms such as systems dynamics, discrete simulations, and cellular automata (Wolfram's New Kind of Science), however incommensurable. I was particularly interested in the occasional use of probability modeling (negative exponential distributions generated through simple rules are a very interesting advance in understanding the waiting times between civil violence outbursts) and I'd love to see a deeper relationship established, say between Bayesian models of dynamic systems and agent-based models. Keep up the great work, Josh! Also, kudos to the publisher for the sheer quality of the book: excellent paper, great color plates, and priced to sell rather than as the work of art it is.
Excellent survey of the author's work.......2007-07-27
This book did a good job of introducing me to the current state of agent-based modeling. It also, perhaps inadvertently, highlighted some of the current weaknesses of the field. In particular, the models shown in each paper rarely shared common features, and there was little consistency in method.
Epstein argues persuasively that agent-based modeling is a tool, not a methodological approach, and you should no sooner expect consistent usage here than with differential calculus. That said, it was a bit disconcerting.
Also, while the goal espoused here was to use the bare minimum of constraints that retain explanatory power, I was disappointed that relevant work from other fields was often abstracted away. For example, a few models used social networks; but the networks presented were static, not dynamic, and were not built around power-law ratios. Such additional complexity may well have distracted from the main point; but it would have been nice to see at least some discussion of why the models were simplified.
Regardless, I was very pleased with the book and would highly recommend it.
A Landmark Publication.......2007-03-08
Josh Epstein's new Opus is a landmark publication in the emerging field of multiagent-based simulation of dynamic social systems. Since Josh is not only one of this still nascent (though burgeoning) field's ablest and most creative practitioners, but also among its most thoughtful critics, the reader of has two treats in store: (1) a generous, and wide-ranging, sampling of case studies (including social networks and evolution, population growth, emergence of economic classes, civil unrest, timing of retirement, the dynamics of adaptive organizations and the spread of infectious disease), and (2) a cogent "meta" discussion of what multiagent models ARE, ARE NOT and how (when their properties and limitations are *not* properly taken account of) they can easily be MISAPPLIED.
Far from suggesting that multiagent-based models are a panacea solution to all (or most) social dynamical systems, Josh's book carefully articulates the conditions for which such an approach IS (and is NOT) appropriate; an approach rarely taken by other, similar, overviews of the field. Indeed, the cogent philosophical discussion in Chapter One - alone! - in which the generativist's position is defined and put into a broader modeling/simulation context, is worth the price of admission; I have not seen a better "manifesto" of multiagent-based modeling elsewhere.
Finally, without taking away any of the inherent "beauty" (in the technical sense) of the often exaggerated concept of "emergence," Josh succeeds admirably in both defining the term, and de-mystifying it, stripping it of some of its unnecessary "quasi-mystical" baggage (at least as it is often portrayed in lay publications).
Anyone who is interested in understanding how agent models may be used to help explore the dynamics of social dynamical systems, should have this book firmly on top of their "must read" list! Josh has generously provided future generations of agent explorers their go-to source of both inspiration and ideas. Well done Josh!
Amazon.com
Languages and Machines is a user-friendly text that covers the key ideas of the theory of computation clearly and thoroughly. Examples and numerous diagrams, including diagrams that illustrate the principle of induction, aid in the understanding of the material. Relative to other books containing similar information, this text contains in-depth coverage of languages and parsing.
Customer Reviews:
No Examples , No Answers, No Hints.......2006-11-20
Besides the fact that the book is "dry", in which most Math theory based books are, the examples are just the basis step towards solving a problem. I equate it to teaching a child how to add, and just giving them the example "1 + 0", then assume they can figure out the rest. There are no answers, either in the back of the book for particular exercies, nor was a study guide made available. What is really shocking is that it's the most expensive book out there! Not to mention that there isn't any programming steps made available. Great text for a Math major ... horrible textbook for Computer Science Majors, mainly because computer science majors would want to see programming examples and may not be as strongly math oriented as a Math major would be.
A Good Book for a Tough Subject.......2006-02-15
Abstract language theory is hard, but Languages and Machines does a very good job of explaining the subject step by step. The topics are covered extremely thoroughly and with just the right amount of rigor. As for those who claim it's not exciting enough, you can't get blood out of a stone. Only the most dedicated computer scientist and mathematicians will find this topic interesting. Even so, this book does a superb job of tying theory to application (e.g., the machines one can use language theory to build) for even the most obscure concepts (like the Greibach Normal Form).
That being said, there are a few problems. First, the author's claim that this is a book for undergrads is not credible (except perhaps at MIT or CalTech). Even my graduate students have to read sections multiple times to "get it". Second, the author needs to provide solutions to selected problems at the back of the textbook. Most theory books do this, but not this one. This is a major weakness, especially given the difficulty of the material. Lastly, Sudkamp's proofs are extremely dry and very difficult to follow. He should take a cue from Sipser's excellent book (Intro to Theory of Computation) and introduce "proof ideas" to give the big picture for important proofs.
emphasises the Turing machine.......2005-09-29
[A review of the 3RD EDITION, 2005.]
Sudkamp gives a formal and rigorous explanation of what constitutes a language. Where this is deliberately taken to include both natural (spoken) languages and programming languages. To do this, you should note that the treatment is necessarily non-trivial. It is not a lightweight book, conceptually.
The book summarises decades of work in this field, that have attempted to reduce human languages to a form that could be "understood" by a machine. So he explains the various techniques that have arisen. Like finite state machines (finite automata).
Notably, he discusses what is a Turing machine. A universal computing engine, that all other computers can map to. Such a Turing machine might be deterministic or non-deterministic. You can learn very powerful unifying ideas.
From the construct of a Turing machine, the book uses this to delve into problems that are NP complete or P complete. The implementation of a solution as steps to be done by a Turing machine are elegant, and show how such a machine, while an idealisation, can be used to give provable results.
horrified.......2005-09-22
The book is incredibly boring. If you're condemned to read it (say, it's required reading for your qualifiers), I strongly recommend that you find a group of people to study w/ and pool your resources to only buy a single copy. The lack of answers at the back of the book makes self study difficult, and groupwork might be the only way to stay awake.
I wish to God I could think of another book to recommend over this one. I imagine any will do.
Taught by the author!.......2004-09-21
Hey,
I was fortunate enough to learn this course from the author of the book. The book by itself might seem tough. The fault lies in the fact that subject matter is not altogether too simple to understand without someone teaching it to you!
With the help of the instructor, we did learn a lot about formal languages, finite automaton, regular grammer, etc.
The key to understanding this material (and using this book effectively) is solving as many problems as possible, preferably in a group setting so that solutions can be discussed.
Note: For most problems, there exists multiple solutions, and the approach is what needs to be learned and discussed.
Recommended, with some reservations...Good luck!
Book Description
Clearly written graduate-level text considers the Soviet ellipsoid algorithm for linear programming; efficient algorithms for network flow, matching, spanning trees, and matroids; the theory of NP-complete problems; approximation algorithms, local search heuristics for NP-complete problems, more. "Mathematicians wishing a self-contained introduction need look no further." — American Mathematical Monthly. 1982 edition.
.
Customer Reviews:
Excellent book!.......2007-06-05
This book is very good. However, it's dense, so you'll have to parse it carefully and never in a hurry.
Well written.......2007-04-06
I bought this book because I wanted to have theory on linear programming including duality, integer linear programming, typical graph algorithms and matroid theory in one book. Up to now I have read only most of the chapter on matroids and I would like to say a big thanks to the author.
Although you will not solve the world's problems with greedy algorithms, my mathematical part of the heart was pleased and satisfied by the theory which explained the very nice relation between matroids and greedy algorithms.
Maybe I will tell you more in a few months
Combinatorial Optimization: Algorithms and Complexity.......2007-02-18
The book's state is very good, so I am satisfied with it.
A classic..........2007-01-11
I won't lie to you: this book is well written but relatively hard to read. The subject is inherently difficult, after all! I highly suggest it, though, because the author is a recognized expert on the field and the price is relatively low. It's worth it even if you enjoy a few pages...
Mmm, algorithms...........2006-11-12
This is a very nice, self-contained introduction to linear programming, algorithm design and analysis, and computational complexity. The contents are as follows:
Chap. 1 Optimization Problems 1.1 Introduction; 1.2 Optimization Problems; 1.3 Neighborhoods; 1.4 Local and Global Optima; 1.5 Convex Sets and Functions; 1.6 Convex Programming Problems
Chap. 2 The Simplex Algorithm 2.1 Forms of the Linear Programming Problem; 2.2 Basic Feasible Solutions; 2.3 The Geometry of Linear Programs; 2.3.1 Linear and Affine Spaces; 2.3.2 Convex Polytopes; 2.3.3 Polytopes and LP; 2.4 Moving from bfs to bfs; 2.5 Organization of a Tableau; 2.6 Choosing a Profitable Column; 2.7 Degeneracy and Bland's Anticycling Algorithm; 2.8 Beginning the Simplex Algorithm; 2.9 Geometric Aspects of Pivoting
Chap. 3 Duality 3.1 The Dual of a Linear Program in General Form; 3.2 Complementary Slackness; 3.3 Farkas' Lemma; 3.4 The Shortest-Path Problem and Its Dual; 3.5 Dual Information in the Tableau; 3.6 The Dual Simplex Algorithm; 3.7 Interpretation of the Dual Simplex Algorithm
Chap. 4 Computational Considerations for the Simplex Algorithm 4.1 The Revised Simplex Algorithm; 4.2 Compuational Implications of the Revised Simplex Algorithm; 4.3 The Max-Flow Problem and Its Solution by the Revised Method; 4.4 Dantzig-Wolfe Decomposition
Chap. 5 The Primal-Dual Algorithm 5.1 Introduction; 5.2 The Primal-Dual Algorithm; 5.3 Comments on the Primal-Dual Algorithm; 5.4 The Primal-Dual Method Applied to the Shortest-Path Problem; 5.5 Comments on Methodology; 5.6 The Primal-Dual Method Applied to Max-Flow
Chap. 6 Primal-Dual Algorithms for Max-Flow and Shortest Path: Ford-Fulkerson and Dijkstra 6.1 The Max-Flow, Min-Cut Theorem; 6.2 The Ford and Fulkerson Labeling Algorithm; 6.3 The Question of Finiteness of the Labeling Algorithm; 6.4 Dijkstra's Algorithm; 6.5 The Floyd-Warshall Algorithm
Chap. 7 Primal-Dual Algorithms for Min-Cost Flow 7.1 The Min-Cost Flow Problem; 7.2 Combinatorializing the Capacities--Algorithm Cycle; 7.3 Combinatorializing the Cost--Algorithm Buildup; 7.4 An Explicit Primal-Dual Algorithm for the Hitchcock Problem--Algorithm Alphabeta; 7.5 A Transformation of Min-Cost Flow to Hitchcock; 7.6 Conclusion
Chap. 8 Algorithms and Complexity 8.1 Computability; 8.2 Time Bounds; 8.3 The Size of an Instance; 8.4 Analysis of Algorithms; 8.5 Polynomial-Time Algorithms; 8.6 Simplex Is Not a Polynomial-Time Algorithm; 8.7 The Ellipsoid Algorithm; 8.7.1 LP, LI, and LSI; 8.7.2 Affine Transformations and Ellipsoids; 8.7.3 The Algorithm; 8.7.4 Arithmetic Precision
Chap. 9 Efficient Algorithms for the Max-Flow Problem 9.1 Graph Search; 9.2 What Is Wrong With the Labeling Algorithm; 9.3 Network Labeling and Digraph Search; 9.4 An O(|V|²) Max-Flow Algorithm; 9.5 The Case of Unit Capacities
Chap. 10 Algorithms For Matching 10.1 The Matching Problem; 10.2 A Bipartite Matching Algorithm; 10.3 Bipartite Matching and Network Flow; 10.4 Nonbipartite Matching: Blossoms; 10.5 Nonbipartite Matching: An Algorithm
Chap. 11 Weighted Matching 11.1 Introduction; 11.2 The Hungarian Method for the Assignment Problem; 11.3 The Nonbipartite Weighted Matching Problem; 11.4 Conclusions
Chap. 12 Spanning Trees and Matroids 12.1 The Minimum Spanning Tree Problem; 12.2 An O(|E|log|V|) Algorithm for the Minimum Spanning Tree Problem; 12.3 The Greedy Algorithm; 12.4 Matroids; 12.5 The Intersection of Two Matroids; 12.6 On Certain Extensions of the Matroid Intersection Problem; 12.6.1 Weighted Matroid Intersection; 12.6.2 Matroid Parity; 12.6.3 The Intersection of Three Matroids
Chap. 13 Interger Linear Programming 13.1 Introduction; 13.2 Total Unimodularity; 13.3 Upper Bounds for Solutions of ILPs
Chap. 14 A Cutting-Plane Algorithm for Integer Linear Programs 14.1 Gomory Cuts; 14.2 Lexicography; 14.3 Finiteness of the Fractional Dual Algorithm; 14.4 Other Cutting-Plane Algorithms
Chap. 15 NP-Complete Problems 15.1 Introduction; 15.2 An Optimization Problem Is Three Problems; 15.3 The Classes P and NP; 15.4 Polynomial-Time Reductions; 15.5 Cook's Theorem; 15.6 Some Other NP-Complete Problems: Clique and the TSP; 15.7 More NP-Complete Problems: Matching, Covering, and Partitioning
Chap. 16 More About NP-Completeness 16.1 The Class co-NP; 16.2 Pseudo-Polynomial Algorithms and "Strong" NP-Complete Problems; 16.3 Special Cases and Generalizations of NP-Complete Problems; 16.3.1 NP-Completeness By Restriction; 16.3.2 Easy Special Cases of NP-Complete Problems; 16.3.3 Hard Special Cases of NP-Complete Problems; 16.4 A Glossary of Related Concepts; 16.4.1 Polynomial-Time Reductions; 16.4.2 NP-Hard problems; 16.4.3 Nondeterministic Turing Machines; 16.4.4 Polynomial-Space Complete Problems; 16.5 Epilogue
Chap. 17 Approximation Algorithms 17.1 Heuristics for Node Cover: An Example; 17.2 Approximation Algorithm for the Traveling Salesman Problem; 17.3 Approximation Schemes; 17.4 Negative Results
Chap. 18 Branch-and-Bound and Dynamic Programming 18.1 Branch-and-Bound for Integer Linear Programming; 18.2 Branch-and-Bound in a General Context; 18.3 Dominance Relations; 18.4 Branch-and-Bound Strategies; 18.5 Application to a Flowshop Scheduling Problem; 18.6 Dynamic Programming
Chap. 19 Local Search 19.1 Introduction; 19.2 Problem 1: The TSP; 19.3 Problem 2: Minimum-Cost Survivable Networks; 19.4 Problem 3: Topology of Offshore Natural Gas Pipeline Systems; 19.5 Problem 4: Uniform Graph Partitioning; 19.6 General Issues in Local Search; 19.7 The Geometry of Local Search; 19.8 An Example of a Large Minimal Exact Neighborhood; 19.9 The Complexity of Exact Local Search for the TSP
All chapters have problem sets and notes and references.
As can be seen, this book has a mighty amount of information, and it is amazingly well-explained. Of course, you need a firm grasp of your linear algebra, and some knowledge of very elementary calc./real analysis and graph theory (although most of the graph theory needed, technically speaking, is supplied in an appendix). You don't even really need to know a programming language, since the authors use a "pidgin algol," explained in yet another appendix, for most of the algorithm stuff; all it takes is an orderly thought process to follow it.
Despite the book's age, it mostly holds up very well in terms of topics and presentation. In the preface to the Dover edition, the authors briefly discuss some more current topics not dealt with in the text and make some (probably also out of date!) referrals for those wishing to "catch up." All in all, this book is a great value both as a text and a reference.
Book Description
This introductory text covers the key areas of computer science, including recursive function theory, formal languages, and automata. It assumes a minimal background in formal mathematics. The book is divided into five parts: Computability, Grammars and Automata, Logic, Complexity, and Unsolvability.
* Computability theory is introduced in a manner that makes maximum use of previous programming experience, including a "universal" program that takes up less than a page.
* The number of exercises included has more than tripled.
* Automata theory, computational logic, and complexity theory are presented in a flexible manner, and can be covered in a variety of different arrangements.
Customer Reviews:
Pure mathematical view of Computability and Complexity.......2002-02-14
This is not a common book on Computability and Complexity as Hopcroft-Ullman, Sipser or Papadimitrou. You won't find here too many words describing topics: you'll find the power and elegance of a superlative mathematical approach from one the best authors of the century in the field. Conversely, you'll find here a detailed and elegant treatment of the whole history of computational models that starts at the Primitive Recursive Functions, something you won't find in the other books above mentioned.
A special note goes to the chapter on Blum's complexity, which is about the only good place where I found it and from where I studied for my course on Complexity I.
For this reason the book requires quite more attention than others, but it really worths all the time one can spend reading it. Truly understanding Computability and Complexity as Professor Davis teaches them with this book is in my opinion a definitely high achievement, bringing the sensation that you grasp it totally, with no space for ambiguity or weakness.
Beautiful overview.......2001-07-11
The authors of this book define theoretical computer science as the mathematical study of models of computation, and they do an excellent job of detailing the major results in the theory of computation as related to mathematical logic. Mathematicians, programmers, and philosophers will find the book an effective one in which to learn computability theory, and it serves well as a textbook for courses in the subject.
After a brief review of elementary mathematics and mathematical logic in chapter 1, the authors move right into the consideration of computable functions in chapter 2. They choose a particular abstract programming language in which to study the computability theory, which is built from variables, and programs that can be built from lists of instructions. Examples of programs are given, which have a Fortran flavor, with examples of computing partial functions. Unfortunately, a plethora of GOTO statements appear in the programs, and throughout the rest of the book, which is surprising given the publishing date. The use of these GOTO statements in the book is a major annoyance.
Then in chapter 3, the authors discuss primitive recursive functions, beginning with a treatment of composition, followed by the all-important concept of recursion. The class (PRC) of primitive recursive functions is introduced, and shown to be computable. The primitive recursive predicates are introduced, followed by a proof that the existential and universal quantifiers over an element of a PRC class are also PRC. This is followed by a discussion of minimalization and Godel numbers.
The next chapter is very interesting, wherein the famous halting problem is discussed and related to Church's thesis. The authors stress, most importantly, that an algorithm cannot be defined outside of the choice of a language, and therefore Church's thesis cannot be proved as a theorem. The authors also introduce recursively enumerable sets and show, via diagonalization, that non-recursively enumerable sets exist. They give an interesting example of a function that is computable but not primitive recursive.
The next chapter extends the results to strings of symbols instead of just numbers, and the authors introduce programming languages for doing string computations. One of these is the famous Post-Turing language, which they use to discuss the halting problem, with a variant used in the next chapter on Turing machines. The authors discuss the famous halting problem for Turing machines in this chapter. This is followed in chapter 7 by a discussion of productions and simulation of nondeterministic Turing machines. A very lucid treatment of Post's correspondence problem is given.
Things get somewhat more complicated in chapter 8, where the authors attempt to classify unsolvable problems. It contains one of the best discussions I have seen in the literature on oracles, and the authors give a very clear treatment of arithmetic hierarchies.
The second part of the book reads more like a book on compilers, as the authors delve into the area of grammars and automata. Regular languages, deterministic and non-deterministic finite automata are discussed, and Kleene's theorem, which states that regular languages and finite automata define the same languages, is proven. The context-free languages, so familiar from the study of compilers, are discussed also, along with a proof that a context-free grammar can be reduced to a Chomsky normal form grammar. Pushdown automata, needed for accepting context-free languages, are treated in detail. The authors give a good explanation here as to the additional facilities needed for a finite automaton to decide if a word belongs to a "bracket" language. Chomsky hierarchies are also discussed, and the authors motivate nicely the need for a linear bounded automaton to accept context sensitive languages.
Part three of the book is an overview of mathematical logic, and begins with a treatment of the propositional calculus. The satisfiability problem is discussed for this system, along with how to reduce formulas to normal form. The important compactness theorem is given a very detailed proof. Predicate calculus is then discussed, and Herbrand's theorem, which effectively reduces logical inference in predicate calculus to a problem of satisfiability of universal sentences, is proven. This theorem is fascinating and has important applications to automated theorem proving, as it ties together semantic and syntactical properties of a formal system. The Godel incompleteness theorem and the unsolvability of the satisfiability problem in predicate logic is proven.
In part 4, issues in computational complexity are addressed, the measure of complexity given in terms of the Blum axioms. This is a very abstract way of introducing complexity theory, as it introduces measures of complexity that more general than time and space complexity. The fascinating gap theorem, comparing program performance on two computing machines via complexity measures, is proven. This is followed by a detailed discussion of the speedup theorem, which essentially states that there is a wildly complicated recursive function such that for any program computing this function, there exists another program computing the function that works a lot faster for almost every input. The polynomial-time computability is discussed along with the famous P vs NP problem, with the discussion given in terms of Turing machines. Examples of NP-complete problems are given.
The last part of the book covers semantics, with operational and denotational semantics defined and compared. The emphasis in this part is on programming languages and constructions that one would actually find in practice, and so the preceding chapters on computable functions must be extended. The concept of an approximate ordering is introduced to allow for the instantaneous of a computation at some point before its completion. The denotational semantics of recursion equations and infinitary data structures are discussed, with the latter put it in to deal with the sophisticated systems that are constructed here. The discussion here is very involved, but the authors do a fair job of explaining the need for these types of data structures. The same is done for operational semantics, and the authors finally show that the computable numerical functions are actually partially computable. They then show the existence of computable irrational numbers.
My favorite book on the theory of computation.......2000-05-11
I first learned computability from this book and I loved every minute of it. It has lots of material and is superbly written. In fact, I think the chapters on logic are the most painless way to learn that subject. There are many other books around on this subject, but this is the ultimate!
CS Theory at it's best.......2000-03-30
I haven't found a better book on the Theoretical foundations of Computer Science. However since this IS theory the text can be a bit cryptic. Still, I'd recomend this book to any PhD Candidate or full Professor. Even a lowly Master's student like myself could use it.
This is a wonderful text about the theory of computation........1999-02-25
It taught me how to think about the theory of computation. The exercises added to the second edition are a big improvement over the first editon.
Customer Reviews:
Complete Scam.......2007-02-18
Papadimitriou's book is a classic, but many of my students find it difficult. This book is clearly not written by Papadimitriou. When I saw this book (Cram101) I didn't know what to expect, but hoped for some sort of easy presentation of similar material. This book has nothing to do with Papadimitriou's Computational Complexity. Half the pages are intentionally blank. The other half have definitions of terms (most repeated over and over) that have nothing or virtually nothing to do with the topic. For example "mean" is defined (poorly) 17 times and "sets" is defined (poorly) 12 times.
The book is simply not useful.......2006-02-06
If your purpose is to learn something. This book is really bad at teaching you.
The author assumes many things. He has no idea of building things in a gradient. He leaves out the details of how something was arrived at.
If his purpose is to show off, then he has achieved. If his purpose is to create a text that is readable and understandable. He has failed.
Good overall........2004-02-06
A well-written book that teaches you how to think about complexity theory instead of just a flat summary of results. Something like Lewis and Papadimitriou's _Elements of the Theory of Computation_ would be more than enough preparation for this (note that the style of these books is quite different- this one is more informal and descriptive). Covers all the material you need in a first text. Has a good little introduction to mathematical logic in it, including a nice succinct version of Godels Incompleteness Theorem. Lots of interesting exercises.
All in one roof, but presentation very poor.......2003-06-03
I agree with the review by Arthur Fischer. Papadimitriou might
be an excellent researcher, but his communication skills are
hopeless and horrible. The typos make learning even harder.
Perhaps someone like Michael Sipser should take up the task of
rewriting this book.
It is hard to catch some ideas, but it is worth reading.......2003-06-02
Yes,it is generally "hard" for undergradute students even grad. students. If you are taking course "Theory of computation", I would like to recommend the Sipser's or Cohen's books for reading supplement. But you should keep reading this book ! IMO, this book covers so many topics, that it becomes too dense to read. It means you should read it carefully and slowly. For example, it introduces the "reduction" in some previous chapters but without precise defintion and therefore misses the more important part :how to do the reduction correctly and what is the "reseasonable" reduction ? You will find the concept of "reduction" is not very easy to catch if you refer to the Sipser's or Ullman's books. Many friends and me could not go through more than 20 pages of this book in the beginning. But we were keeping on reading and surveying some "easy books". Finally, we understood most half parts of this book. Moreover, if some readers prepare to study more advanced and recent topics, this book is the must.
Average customer rating:
- An excellent book for research on motion estimation in MPEG4
- professional book for implementation
- Exactly what you need to know
|
Algorithms, Complexity Analysis and VLSI Architectures for MPEG-4 Motion Estimation
Peter M. Kuhn
Manufacturer: Springer
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Binding: Hardcover
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Book Description
MPEG-4 is the multimedia standard for combining interactivity, natural and synthetic digital video, audio and computer-graphics. Typical applications are: internet, video conferencing, mobile videophones, multimedia cooperative work, teleteaching and games. With MPEG-4 the next step from block-based video (ISO/IEC MPEG-1, MPEG-2, CCITT H.261, ITU-T H.263) to arbitrarily-shaped visual objects is taken. This significant step demands a new methodology for system analysis and design to meet the considerably higher flexibility of MPEG-4.
Motion estimation is a central part of MPEG-1/2/4 and H.261/H.263 video compression standards and has attracted much attention in research and industry, for the following reasons: it is computationally the most demanding algorithm of a video encoder (about 60-80% of the total computation time), it has a high impact on the visual quality of a video encoder, and it is not standardized, thus being open to competition.
Algorithms, Complexity Analysis, and VLSI Architectures for MPEG-4
Motion Estimation covers in detail every single step in the design of a MPEG-1/2/4 or H.261/H.263 compliant video encoder:
- Fast motion estimation algorithms
- Complexity analysis tools
- Detailed complexity analysis of a software implementation of MPEG-4 video
- Complexity and visual quality analysis of fast motion estimation algorithms within MPEG-4
- Design space on motion estimation VLSI architectures
- Detailed VLSI design examples of (1) a high throughput and (2) a low-power MPEG-4 motion estimator.
Algorithms, Complexity Analysis and VLSI Architectures for MPEG-4
Motion Estimation is an important introduction to numerous algorithmic, architectural and system design aspects of the multimedia standard MPEG-4. As such, all researchers, students and practitioners working in image processing, video coding or system and VLSI design will find this book of interest.
Customer Reviews:
An excellent book for research on motion estimation in MPEG4.......2000-12-13
For mpeg4 motion estimation, it is the only book on the market that well explains and covers with complete algorithms analysis. The references of the papers for each chapter give you more clues about the each algorithm implementation. I would recommend also reference to ISO/IEC 14496-5 MPEG4 codec software and specs to help you understand MPEG4 codec in digital video processing and implementation on streaming media
professional book for implementation.......2000-05-10
an exceleent book with deep investigation on motion estimation. you can find most algorithms proposed before with complete references. The author also provides a profiling tool which is so efficent for complexity analysis. you will be able to benefit it definitly :)
Exactly what you need to know.......2000-04-10
You're looking for the alogritm of making video confrence, orany video transfer, this is the book for you! While reading youunderstand where the price coming from! The Author knows the matterialvery well, and lets the reader to understand the mpeg-4 technology!
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