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Introduction to Algorithms [Anglais] [Broché]

Thomas H. Cormen , Charles E. Leiserson , Ronald L. Rivest
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Détails sur le produit

  • Broché: 1048 pages
  • Editeur : MIT Press; Édition : New edition (29 août 1990)
  • Collection : MIT Electrical Engineering and Computer Science
  • Langue : Anglais
  • ISBN-10: 0262530910
  • ISBN-13: 978-0262530910
  • Dimensions du produit: 4,5 x 0,3 x 5,3 cm
  • Moyenne des commentaires client : 5.0 étoiles sur 5  Voir tous les commentaires (1 commentaire client)
  • Classement des meilleures ventes d'Amazon: 756.867 en Livres anglais et étrangers (Voir les 100 premiers en Livres anglais et étrangers)
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1 internautes sur 3 ont trouvé ce commentaire utile 
5.0 étoiles sur 5 Excellent book, 26 juillet 2001
This book is an excellent reference/guide to many common data structures, as well as other odds and ends. Great stuff!
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Amazon.com: 4.4 étoiles sur 5  70 commentaires
47 internautes sur 52 ont trouvé ce commentaire utile 
5.0 étoiles sur 5 The best textbook I have ever seen 7 janvier 2000
Par Ashwin Rao - Publié sur Amazon.com
I was the instructor for a junior/senior course on Algorithms at the University of Southern California and I used this book as the textbook. Unfortunately, many of the students didn't like this book because they did not appreciate the mathematical flavor of the book. A course on Algorithms is useless without a sound background in discrete mathematics. Hence, this book assumes that you are reasonably strong in Discrete Mathematics.
I haven't seen a better textbook ! Here are some reasons:
1. The discrete mathematics foundations are present in the first few chapters of this book and so, you can quickly brush up on any discrete math background that you may require while using this book.
2. The style of writing is very light and at the same time, rigorous - almost as if you are in the middle of a lecture while reading the book.
3. The material is comprehensive and serves as an excellent reference for other courses and in your future career.
4. The exercises and problems provide a very good learning experience.
5. It's a good-looking book !
59 internautes sur 67 ont trouvé ce commentaire utile 
5.0 étoiles sur 5 A Very Solid Introduction to Algorithms 8 décembre 2000
Par Donovan Rebbechi - Publié sur Amazon.com
It's a good thing that this book has a hard cover (make sure you get the hard cover edition, huh?), because otherwise mine would be in pieces. This book is my favourite book on algorithms. All the others seem somewhat unsatisfactory to me -- they are tied to particular programming languages, they are paperback, and they are for the most part less comprehensive than this book. (except Knuth, which is somewhat more advanced). See the summary of the TOC below for an outline of what the book covers. I guess Sedgewicks new title has been getting better reviews, but it's still not hard cover (-;
This covers a lot of topics, and covers them in some level of mathematical rigor. For example, all assertions about algorithm efficiency are backed up with *proofs*, and key concepts like asymptotics, and big-O notation are covered. To those who think proofs are not essential -- as a mathematician, I'd counter that proofs are absolutely necessary, because you don't know something until you've proven it -- it's easy to make wrong "guesses", or even wrong hand-waving arguments. The examples are all in pseudo-code. Personally, I liked this as it makes implementing the data structures an interesting exercise that forces the reader to think.
The subject matter covered is quite broad, see below. There are some interesting topics that don't get covered (eg AVL trees), but this book does a good job at laying down the foundation.
Some might be intimidated by the theoretical approach, but I for one like it. It's written for computer scientists (or "software engineers"), not get-rich-quick wannabees. This book will force you to think, and if you don't like that, well you can (and should) buy "learn algorithms in 21 seconds" from SAMS or something.
You'll need some background to digest this material. Someone with a year of programming and some discreet math should be ready for it. Note that you won't learn any programming *language* from this book (unless you count pseudo-coed), so you'd better know some before starting !
Summary: PartI: Intro, Growth of functions,Summations, Recurrences, Sets, Counting and Probability
Part II: Heapsort,Quicksort, Sorting in linear time, Medians/order statistics
Part III: Stacks/Queues/Linked lists, Hash tables, Binary search trees, Red-Black trees, Augmented data structures
Part IV: Dynamic programming,Greedy algorithms, Amortized analysis
Part V: B-trees, Binomial heaps, fibonacci heaps, data structures for disjoint sets
Part VI: Elementary graph algorithms, Minimal spanning trees, single-source shortes paths, all pairs shortest paths, maximum flow
Part VII: sorting networks, arithmatic circuits, algorithms for parallel computers, matrix operations, polynomials and fft, number theoretic algorithms, string matching, computational geometry, NP-completeness, Approximation algorithms.
39 internautes sur 43 ont trouvé ce commentaire utile 
5.0 étoiles sur 5 Rigorous coverage of the most widely used algorithms 5 décembre 1999
Par Steven Nicolaou - Publié sur Amazon.com
I personally bought this book in preparation for the International Olympiad in Informatics (IOI), and it helped me immensely in getting off the ground with the algorithms I had to learn, especially the chapter on Dynamic Programming. Since then, however it has remained a priceless companion during my studies and at home.
This is the definitive reference for algorithms with a firm theoretical and mathematical foundation. Algorithms are treated with a thorough theoretical introduction often with a complete mathematical walkthrough, a clearly thought out solution, a discussion of its pros and cons, lots of clear and consisive diagrams, a pseudocode implementation, and a good deal of serious optimisation discussion. It's written in an accessible manner, starting with the elementary issues, progressing to the advanced and complex thinking needed to conquer them, so you'll find you have to give it your full concentration.
This book will not disappoint. Its explanations are rigorous and its coverage spans all the general purpose algorithms with little focus on their applications but rather on the algorithms themselves. The book covers such major areas as sorting, data structures, advanced design and analysis techniques, graphs, each about a hundred pages on average, and a selection of specialised algorithms such as parallel programming, string matching and computational geometry. Because these algorithms are used everywhere, from games, graphics and simulations to electrical engineering it will have a broad audience and will find a home almost anywhere there is serious programming involved. Each chapter is a unit in itself which means you don't need to read it cover to cover, since they all start off smoothly and handhold you through. Clearly written by professionals, this is the book I know contains the information that I can't find elsewhere.
67 internautes sur 77 ont trouvé ce commentaire utile 
5.0 étoiles sur 5 Complete, thorough... 3 août 1999
Par Un client - Publié sur Amazon.com
Quote from a previous review:
Instead of touching on new technologies, such as AI, graphics, or anything else remotely relevant to today's demands on programmers and designers, this book, faithful to its MIT roots, gives a pompous, eggheaded distortion to the field of computers as a whole. Its focus is mainly on such trivialities as algorithm analysis, offering about 10 pages of proofs for each simple assertion. The points that the authors hope to make have no relevance whatsoever in a world in which processor power, not meticulous code optimization, reigns.
I've had Cormen (one of the authors) as a professor in class, and my algorithms class uses this book, so admittedly my view might be a bit biased. But if you read the above (quoted) review, you might have gotten the wrong impression about this book. Cormen et. al. *intentionally* left "AI and graphics" algorithms to other authors; this isn't the place to cover those topics enough to do them justice. And as someone who has actually read the book, each proof is *not* 10 pages long. The examples are usually quite good, and concisely (if thoroughly explained). Finally, prof. Cormen always explains to his intro CS students why the study of algorithms is important, even as computers get faster and faster: some problems, poorly implemented, just *will not* run as well on a machine of today compared to a much older machine running a better algorithm. There will *always* be a justified place for the study and analysis of algorithms. Had the previous reviewer actually had met Prof. Cormen, he wouldn't be able to write the book off with the title of "pompous" or "eggheaded" either...
43 internautes sur 50 ont trouvé ce commentaire utile 
5.0 étoiles sur 5 What every computer scientist should have 18 janvier 2001
Par Jose Berlin - Publié sur Amazon.com
If one were to make a list of the 100 best books in computer science, then winnow that list down to 10 books, and then again down to 1 book, surely this would be that book.
Known in computer science circles as CLR (for the authors) or simply, "The White Book", Introduction to Algorithms by Cormen, Leiserson, and Rivest is the de-facto standard text for algorithms and data structures. It covers all the basic subjects (big-O notation, trees, graphs, etc...) as well as a few intermediate subjects (amortized analysis, matroids, etc...). Of course, this book is not the be-all and end-all of computer science nor does it pretend to be. It touches on NP-completeness only lightly and all but omits randomization; but if you wanted a text on NP-completeness, you would be reading Garey & Johnson and if you wanted randomization you'd go to Motwani & Raghavan. But if you need a reference on data structures and algorithms, this is the book for you.
Now, some have complained that while this book is an excellent reference that it is a poor text to learn from. I beg to differ. I concede that it is certainly more demanding than many other introductory texts, but this is a boon not a curse. By remaining true to computer science's mathematical heritage, Cormen et al. force the reader to become accustomed to rigourous, formal reasoning, something which is unfortunately absent in many computer science curricula. The authors present the concepts cleanly and clearly, without the distraction of any specific programming language/paradigm. Perhaps it is this removal from a familiar C/C++/Java/flavour-of-the-month/etc... milieu which makes some readers nervous. But it is precisely this separation which forces the reader up into the realm of abstraction where computer science truly resides.
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