Textbook on Theoretical Computer Science by Boaz Barak. For prior versions of the book, see the repository release page. The most updated version of this. The new edition deals with all aspects of theoretical computer science, namely automata provision of detailed solutions (at the end of the book) to chapter-end . Finite State SystemsDFA, NDFA and there equivalence. Conversion of NDFA, DFA, DFA with E-Moves, Two-way Finite Automata, Finite Automata with output.
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This is a list of important books to learn theory CS. They may not be “must-read” but certainly very useful. (Undergrad Books) 1. Introduction to. Well, I guess Garey and Johnson's Computers and Intractability: A Guide to the Theory of NP-Completeness will be found among the top books. You don't need to be in college to learn theoretical computer science, you can do it from the comfort of your home. 12 free online books to help.
This is a textbook in preparation for an introductory undergraduate course on theoretical computer science. I am using this text for Harvard CS If you have any comments, suggestions, typo fixes , etc.. You can also post comments on each chapter in the links below. The repository github.
Wilf, is an excellent introduction to the theory of generating functions, written in a smooth way and packed with exercises.
If he writes all his books like that, I can't wait to get started on another one. Enumerative Combinatorics by Richard Stanley is a great reference advanced. Applied Combinatorics by Roberts and Tesman is an encyclopaediac reference on applied combinatorics.
This is a verbatim of my answer to this question. Knowledge of basic mathematical logic is, in my opinion, a plus. You can have a look at the two books by Cori and Lascar. Mathematical Logic: A Course with Exercises Part I. I found several books frequently cited in many papers. They are excellent on the subject, but some of them are quite old. Here's a list of what I recall:. Berge C. Troelstra and Schwichtenberg's book Basic Proof Theory is the de facto text on the topic now.
For introduction to graph theory: Introduction to Graph Theory by West. More about graph theory: Graph Theory by Bondy and Murty.
The comprehensive book which contains new developments as well as old classic results in graph theory:. Graphs On Surfaces. Theory, Algorithms and Applications. I'm not into hardware. Here they are:. Thank you for your interest in this question.
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What Books Should Everyone Read? Ask Question.
The books can be math-oriented, yet you may find it great for a computer scientist. There are books that everyone in field X should read. This question seeks all 3 cases. In other words, it is NOT specific to the latter case. Related topics: References for TCS proof techniques Books on automata theory for self-study Books for probability Favorite popular math book Beginner's guide to derandomization References on circuit lower bounds Survey article on the theory of recursive functions Books on Programming Language Semantics What are the recent TCS books whose drafts are available online Books on probability.
Discrete Mathematics - TTC: Discrete Mathematics by Arthur T. It's a lectures bundle on various topics from Set Theory to Graphs and Probability. There is some overlap, but luckily the differences are sufficiently significant.
If you are looking for recent complexity textbooks. The following two are must have. A Conceptual Perspective by Oded Goldreich Textbook homepage The majority of the content between these two books is comparable.
Another option, an older but timeless textbook in complexity is: Another comparatively old, but quite notable classic is: The Nature of Computation by Cristopher Moore and Stephan Mertens Scott Aaronson writes that this book has "the fun of a popular book with the intellectual heft of a textbook.
For example, he uses "recognizable" instead of "semi-decidable". But I guess since the textbook is so widely used, it might just become as standard by now. I took it, and really loved the book.
I found it much more approachable than CLRS. As in previous comment, it's more mathematically matured. For specific topics, you may consult: I found gems like 'resorvoir sampling' in this book just to mention one example.
Finite Model Theory We want to learn techniques that deal with finite structures. For this area, I recommend the following excellent books: The definitive reference for descriptive complexity is: Neil Immerman, Descriptive Complexity.
Proof Complexity Another important area of logic in computer science is Proof Complexity , a study of three way relationship among complexity classes, weak logical systems, and propositional proof system.
For excellent surveys on proof complexity, I recommend the following two books: I found it excellent. Thanks to Aaron for mentioning it here. Dousti Nov 22 '10 at Other excellent books are: Handbook of Applied Cryptography by Menezes et al. Chapter are available online. Theory and Practice by Stinson. Specific Topics: Books pertaining to advanced composition of security protocols: Composition of Secure Multi-Party Protocols: A Comprehensive Study by Lindell.
Concurrent Zero-Knowledge by Rosen. I don't know a book that covers this area appropriately. Suggestions are welcome. The transition from textbook to crypto literature was rough because of this specific reason.
Also, Sadeq, out of curiosity: Do any of the books you've read have good coverage of rewinding? Regarding the "rewinding" question: I don't know a book about it, yet I think I've completely grasped it. Could you please elaborate what part seems problematic to you?
You can even ask it on a separate topic. Dousti Nov 24 '10 at 8: The problematic part is that rewinding wasn't in the textbook used in the crypto course that I took i. I'm aware it shows up regularly in crypto literature, but as someone not actively involved in crypto research, I doubt I'll be reading enough papers to gain a solid understanding of rewinding just by encountering it enough.
Maybe I could ask a question about rewinding's origins The introduction of my book con concurrent zero-knowledge explains rewinding and the difficulties induced by it in the context of protocol composition. Other sources are: SIAM J. Concurrent zero-knowledge. ACM 51 6: However, data structures for these languages do not always translate well to functional languages. Functional Programming: Practice and Theory by Bruce J.
Despite its name, this book is more theory-oriented than practice-oriented. Those who read this book will have a much better view of the subject than those who learn it by ad-hoc programming. A brand-new exposition on the subject, which takes the problem-solution approach, and shows the beauty of the field by exhibiting attractive ideas in the design of functional algorithms.
Examples are extensive and easy-to-follow. Here are comparisons by two reviewers: Any of the following books can serve as an excellent introduction to the subject: Handbook of Discrete and Combinatorial Mathematics by Rosen. Discrete and Combinatorial Mathematics: An Applied Introduction by Grimaldi. See also the solution manual. The first book is more comprehensive as well as classical. The second book has considerable overlap with the first, but the emphasis is a lot more on modern counting methods.
Topics, Techniques, Algorithms by Cameron. This book has a lot of nice exercises. It's simply a masterpiece on enumerative combinatorics; very challenging, very deep. Combinatorial Algorithms: More suited to computer science applications of combinatorics. A very useful reference when facing a combinatorial problem related to number theory. Clarke Jr. Apt, Frank S. Modelling and Reasoning about Systems by M.
Huth and M. Alas, there is no up-to-date text available. Also look at recent automatic verification tools like Facebook's "infer" and the theory behind it. For someone who isn't very familiar with all the rigorous math in CS it's a good start. It is the first book I read about the formal side of CS and got me hooked ever since! It's also the first textbook that I actually finished all the readings. Since the book lacks worked examples, I suggest the following book: For starters, the following book is suggested: While elementary and easy to learn, some people find it "filled with errors".
In this regard, the book's errata comes handy. Two other excellent introductory books on the subject are: You should only recommend books or articles that you have read quite carefully and narrow down to your absolute favorites! You are right. I think the list should be narrowed down. I'm personally responsible for bloating the list! However, this is a community wiki post. I added a long list suggesting what candidates are.
Please trim the list to include only the most appropriate books. Dousti Nov 29 '10 at I'm afraid it's rarely the case that one person will trim down another person's list. As long as the post is still in the current form, it's completely worthless with respect to the purpose of post. But, since I'm not an expert in "info theory," I cannot trim the list myself.
I can: What do you suggest? Dousti Nov 30 '10 at I don't work on information theory either, otherwise I would help to trim the list. I know that the book "Thomas M. Cover, Joy A. Elements of information theory" is recommended by many including Lance Fortnow. But I'm not sure whether everyone should read it or not.
I think we should respect the original poster since the book might be his most favorite. So deleting the whole list is a good option.
I really apologize for being too straightforward. Also could you ask people to explain why they suggest their books? Fundamentals, Simulations, and Advanced Topics by Hagit Attiya and Jennifer Welch This contains advanced materials, suitable for PhD level courses; both message-passing and shared-memory models are discussed Design and Analysis of Distributed Algorithms By Nicola Santoro A relatively recent book, may be used both at the undergraduate and PhD level; materials are presented with an emphasis on protocol design.
Brass, Moser, and Pach book on open problems in discrete geometry is also very good. We'll begin with a brief overview of the basic issues. We're going to be talking about very fundamental questions relating to computation, like what can a computer do or if we have limited resources what can we do with a computer.
There's a very general approach that mathematicians and computer scientists have used over the last five or six decades actually. First of all, we don't want to talk about specific machines or problems. We want to consider what's called abstract machines that have very minimal capabilities and we'll see some examples of that in this lecture. So, this is an example of a machine that we'll see at the end.
And we'll consider very general classes of problems. So, the idea is to come up with the simplest machine that shares the characteristics of the actual machines that we use and the classes of problems that embody the classes of problems that we care about and try to say general statements.
And the surprising outcome of this approach is, that we're going to have sweeping and very relevant statements about all computers. It's quite actually an amazing outcome that we'll get to in the next couple of lectures.