Algorithms 1st Edition

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 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.

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 networksWeak 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.

I don't learn by textbook very well, but this textbook has been great! The writing style is clear and it's one of those books that tries its hardest to not lose you, rather than to provide the absolute most detailed proofs and technicalities. There is also some great MIT opencourseware lectures over the book as well, I would recommend!