Algorithms

This book is quite a nice read. It explains algorithms clearly and is comprehensible, though it does require work on the reader's part. If you really want to understand algorithms, you're going to have to think through the problems within the book, which may arguably mean that the book isn't doing its job, but I feel that if you are to fully understand a subject, you're going to have to grapple with it. Furthermore, the problems in the back of the book are great to work with, though it is a little unfortunate that I haven't been able to find a solutions manual or anything of the sort. Regardless, it's a good book to pick up, and not a very dense read through. At least, this is speaking from the perspective of someone who actually is interested in algorithms and has some discrete mathematics knowledge. If you are looking for something with more general knowledge (though I'm not really sure what I mean by that), this might be a tad bit hard to read, but I feel it's still worth the challenge.

Used this for a grad school algos class. This textbook is short, packed full of info, and wastes no words in getting you to understand the concepts. There are plenty of good examples, and the text itself is probably one of the most readable textbooks I've ever encountered. Most example problems have solutions posted on YouTube or various blogs. It's one of only a handful in my life where I can claim I did actually utilize the entire textbook. Pair it with something like the Algorithm Design Manual and you've got everything you need to succeed.

It is funny how the relatively small and inexpensive textbooks are often the ones most worth keeping... The book itself is very nice, with easy to read diagrams and such. I would highly recommend getting it if you can afford it, as flipping through the book to refer to various figures and look up things is so much easier than navigating a PDF. The writing itself was excellent: It was easy to follow what the author was saying, and everything seemed to make sense when I was finished with a section. My only criticism would be with the level of detail. The descriptions of the more complicated algorithms are sometimes a bit vague, and Big O times are rarely stated explicitly. Although not having enough details on some sections can be aggravating, the alternative would be a much larger and denser book. I think the trade-off is worth it. Just know that you may have to occasionally refer to an outside source or spend some time working something out yourself. I used this book in an introduction to algorithms course. If you are taking something a bit more advanced, you may want to find a different book that assumes you know more.

This was the primary textbook for my Georgia Tech OMSCS class, CS6515: Intro to Graduate Algorithms. Strong points: * Very affordable, for a textbook * Extremely concise: all chapters are short so it's easy to read the entire relevant chapter while the class covers the corresponding material * Eloquent & pleasant to read, considering the technical mathematical subject * Rigorous, creative, numerous problems at the end of every chapter * Excellent coverage of underrepresented topics like dynamic programming & flow networks Weak points: * No official problem solutions. This is disappointing because the exercises are packed with excellent additional content & applications. Crowdsourced solutions simply aren't sufficient. * Inconsistent rigor. While Dynamic Programming is well-covered, Graphs & Trees arguably need more than 1 chapter to cover the full breadth of variability. I'd argue that Algorithms by Sedgewick et al. has more comprehensive exploration of these topics. * Mediocre quality. The pages are somewhat thin & crease easily. The printing is offset on pages, almost to the point of cropping the page numbers. I suppose this is the tradeoff for the price. * No programming solutions. This can be problematic for interview preparation, so you'll need to supplement with additional textbooks or websites like LeetCode. * No updates. They only released 1 major revision of the textbook, 15 years ago. The errata on Dasgupta's website hasn't been updated in 14 years. The material hasn't changed, but I'm sure there are many opportunities to improve the execution with minimal effort. I fear this book will slowly become irrelevant due to neglect. The textbook was created in the context of undergraduate teaching at UC San Diego & Berkeley, so it's not surprising that the best use-case is alongside a formal class. The lack of solutions & code samples damages the self-study potential. That said, this is a crowded subject with many established texts (CLRS for reference material, Sedgewick for data structures, dozens of coding interview books & websites) so in some ways this book fills an important and useful niche. With that understanding, I still cannot give a perfect rating because the lack of any official solutions is really inconvenient and puts the responsibility on the professor to help students learn from the exercises. I think the context of online solutions has changed dramatically since 2006 and I wish this book evolved with that reality.

Some people are complaining that this book glosses over a lot of details. That's partially true - you'll find yourself re-reading the same passages over and over to really understand the material. However, I think it elegantly describes concepts, and when you realize how simple and intuitive they are, you'll really appreciate how concise the book is. Also, the book has a very logical flow that resembles a narrative. It incorporates the history of algorithms into the discussion, and important people along the way. Overall, an amazing text book, which cannot be compared to the wordy and encyclopedic MIT book.

This book is dense but fairly interesting. The one thing I did not like is the code examples are often so concise that they leave out details. Since this book is targeted at learners, leaving out essential details is not a great strategy in my mind. Easy Theory on YouTube was a great supplement to some parts of this.

This is a well known landmark book. There may be other books which can be used a sort of dictionary of algorithms. But this one gets you thinking which is far more important. Complete this with adequate Math background. Your problem solving approach and perspective will drastically improve,.

Aunque hubiese agradecido que estuviese traducido al español, es un buen libro que me ha ayudado a entender muchos conceptos y con el que he podido aprobar la asignatura de diseño de algoritmos. Creo que sin esta lectura me hubiese sido mucho más difícil aprobar la carrera... Se echa en falta que los ejercicios estén resueltos...

Ottimo libro per imparare i principali algoritmi. Me lo ha consigliato il mio prof di algoritmi dell'Università. É fatto davvero bene anche se é in inglese

Ce livre est une excellente introduction à l'informatique théorique. Les choix pédagogiques sont très pertinents et la lecture de ce livre n'en est que plus agréable. Quiconque voulant s'initier à l'informatique théorique pourra acquérir un base solide en la matière en lisant activement ce livre. Sans être exhaustif sur le sujet mais cependant très riche et très complet, l'ouvrage aborde les sujets classiques parmi lesquels la récursivité, les graphes, les algorithmes gloutons, la programmation dynamique, la NP-complétude. Il propose un chapitre terminal sur le thème des algorithmes quantiques et peut constituer en cela une introduction à ce sujet. La complexité est omniprésente dans l'analyse des algorithmes. De nombreux exemples illustrent les concepts et des anecdotes rendent la lecture de ce livre passionnante. Enfin, les nombreux exercices complètent très largement l'ouvrage. Un livre très pédagogique pour tous les passionnés de l'informatique théorique...

El papel es muy delgado es probable que se rompa si no se tiene cuidado con el.