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on October 6, 2016
I think this is a pretty good book that is easy to read if you have a strong background in proof-based math.

- The introduction (Chapters 1-4) is really good and does a good job setting up all the fundamental concepts of algorithms. I think a lot of people tend to skip over introductions because they think they know all of it already, but this is an introduction that I recommend reading the whole way through.
- The book is a pretty light read (none of the math is too difficult) and each chapter is a good length.
- I think the material on dynamic programming and greedy algorithms was particularly enlightening, and if you read it the whole way through you actually learn how to prove that greedy algorithms work, instead of just being like "let's use a greedy algorithm because it seems right"
- I was able to copy a lot of CLRS code almost verbatim in my programming interviews and pass them.
- The figures are really well done and informative.

- The pseudocode has a lot of one-letter variable names, and while this follows the tradition of pure math, it also makes understanding the algorithms more difficult than it should be.
- Sometimes the pseudocode is not the "easiest" possible pseudocode (for example, merge sort), and I think it would be better if the authors presented a simpler version of the pseudocode first and then extended it to the optimal version. But then I guess CLRS would be even longer than it already is.
- The arrays are 1-indexed, which makes it trickier to convert to code. Also there are some sections of the textbook (the counting sort section) where some of the arrays are 0-indexed and other arrays are 1-indexed, which is just weird.
- I think the material on graphs, particularly the derivations, could be done in a more engaging and intuitive way. The derivations in Chapters 22-24 were a long series of small uninteresting lemmas, instead of a small number of harder, more insightful theorems. I found derivations elsewhere on the internet that were a lot more interesting and built more intuition about why the procedures worked. I feel like the rest of the book is pretty good though, so maybe all the graph stuff was written by a separate person who is not very good at explaining things.
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on November 9, 2015
Just bought the Third edition Hardcover. And I find out that all the mathematical symbols in pictures becomes small squares. That's very hard to read especially some Greek letters are missing.
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on November 14, 2013
This book was written for professors and PhD teaching assistants, but not for the student. The best analogy I can think of is trying to learn math theorems by reading their formal proofs. It's cryptic, difficult to understand, and you'll need to browse around online for better explanations. CLRS is a great reference book, but a terribly abstruse and incomprehensible book for student that are starting to learn algorithms. If you are new to computer algorithms I suggest reading some books with titles along the lines of "Algorithms for Dummies" or somesuch before tackling the CLRS book.
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on August 19, 2016
An impressive volume that covers many many algorithmic strategies and is surely a major achievement in completeness and accuracy, and should also be pretty unique in its range from mathematical representations to concrete pseudo-code for algorithm implementation. Certainly a book to admire, featuring some excellent buildups and explanation paths. I imagine it to be highly esteemed as reference for undergraduate year-long series of lectures on algorithms in a university setting.

As such it probably earns more than the 3 stars I will give it from my own perspective. My reasons for this are inherent in my own relationship to the topic - as a "user" if you will.

I bought this book in order to dig deeper into my own conceptual understanding of algorithms, working by my myself.
For this purpose I found the book to be quite tedious. Disadvantageous are especially the fetishization of compact representations, a tendency mathematicians are known to have.
It really doesn't pull a lot of punches when it comes to dishing out mathematical expressions for the reader to patiently unfold for themselves in order to really grasp the meaning of the surrounding text - Ingestion of the material can become a true test of perseverance this way.

It also needs to be said that this is one of these books that bears "Introduction" in the title when it works in fact more like an encyclopedic heavyweight 1200 page quasi-complete survey of the field.
I'm writing this so you know what you are getting into. Be sure this is what you're looking for and what you need.
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on August 10, 2016
I enjoy math, but not to a degree that this book requires. With that being said, this is a spectacular book. If you take your time and read everything, you will know the every single "why" behind every algorithm in this book. It breaks them down so well and in such great detail that it's absolutely impossible not to know enough to understand them in the end (you might not understand all the details of explanations, but that's besides the point). It can definitely get very painful, at times, because the math and the theoretical text gets very heavy, but I think it's well worth it. For me, this is definitely not one of those books I can read in a week, like I do with many other computer science books, but the sheer satisfaction of reading just a tiny bit is immense. Well worth every single penny ten times over.
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on January 22, 2016
My primary intent of buying the book was to implement efficient data processing algorithms for Data warehouse application in my project and in personal big data project i am working on. I see each algorithm has detailed mathematical proof, which i think you can skip and focus on just implementing the algorithm. I am sure this book is valuable for understanding the algorithm and you would do good job in tech interview. I see that most of IT companies such as Google, Apple etc.. focus more on how well the candidate understands and implements algorithm. So its more of back to basics :).
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on June 3, 2016
This is probably the most well known and most used textbook on the subject, and with good reason. An excellent resource, covering just about everything you need to know for a good understanding of Algorithms. (side tip, my friends in the industry call this the "How To Pass a Google Interview" book).

My only complaint is that the binding has completely stated disintegrating after only 9 weeks of use. All of chapters 15 and 16 are completely falling out of my copy (and this is getting worse). Very disappointing as I plan on using it for a long time.
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on January 22, 2014
I could not rave more about this textbook. I read it over the course of about 9 months and would literally read a proof and look around in amazement at how much sense it made. I learned so much from this book and most importantly, I learned it *well*.

There are tons of other algorithm textbooks out there but don't let the other ones fool you; this is the omnibus and the leader. I've read others (Skiena, Algorithms in a Nutshell) and although they are for different audiences, I'd still recommend CLRS in every case.

Don't be fooled by the Intro in the title. It is pretty math heavy and works a lot with proofs. Most of them are explained well but sometimes they need to be read a few times because they are just more difficult material.

Either way, get this book if you want to learn algorithms. I'd eat a rock if you read it and it didn't help you significantly in understanding algorithms.
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on March 22, 2015
This is the quintessential text for algorithms and many universities use this book in their "Computer Science 101" course. It was required in my first year at the University of Toronto (late 90's). It presents a treatment of the most common algorithms and the techniques needed to analyze them.

I recall struggling with the subject matter, despite having worked with computers since childhood and completing the requisite high-school mathematics courses. The approach taken by the authors is fairly direct; there is little hand-holding, although there are 100 pages of appendices covering pre-requisites.

I had my moments of wanting to hurl it across the room, but it remained amongst the few texts I held onto, thinking it might be useful or enlightening in the future, relative to the burden of lugging it around. I've since upgraded from the 2nd edition to the 3rd, and occasionally pick it up, read a few sections or a chapter and complete some exercises.

Each topic in the book is covered in the same way:
- Explanation of problem or concept.
- Example(s) and/or diagram(s)
- Mathematical proofs, where applicable.
- Problem set

Tip: Concrete Mathematics by Knuth is good primer, establishing the specific discrete and continuous mathematical techniques underpinning the algorithms, and filling several knowledge gaps. Also, Cormen has published a more approachable version of this text ("Algorithms Unlocked") which which be more appropriate to some.

None of the solutions to the exercises are provided in the book, but a few are available at the books website: mitpress.mit.edu/books/introduction-algorithms.
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on October 1, 2015
The printing is not clear.
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