Comment by jasim | Hacker News Reader

Comment by jasim | Hacker News Reader

I'd tried studying from both CLRS and this text (S. Dasgupta, C. H. Papadimitriou, and U. V. Vazirani) some years back.

I had a visceral reaction against CLRS when I saw the standard pseudo-code the book uses. But as I tried implementing some algorithms in C, I found that the algorithms were so precise and detailed that there was no better way to represent it (apart from giving the C code directly).

I've heard claims that CLRS' pseudo-code could be presented in a higher level manner - but it defeats the purpose of an Algorithms text. Algorithms should be correct, fast and consume the least memory possible - this requires you to think about low level memory management, cost of comparisons etc.

However, the density and detail of CLRS forced me to look for other books which presents the topic in a better manner. Vazirani is an excellent choice in this regard. The language is clear and easy to understand, and it gives a better high-level picture than CLRS. I remember reading Chain Matrix Multiplication (page 184, 6.5) in Vazirani and being overwhelmed by the simplicity of the presentation compared to the dry formalisms used in CLRS.

From my experience, I recommend using CLRS as an aid for rigorous study and Vazirani for less rigorous, but very effective learning. Also take a look at Sedgewick's Algorithms in Java series - http://www.cs.princeton.edu/~rs/ (he has C and C++ series, but Java is what I've used). It is more detailed than Vazirani but at the same time more approachable than CLRS.

Another good algorithms text is Skiena's Algorithm Design Manual. It's not as rigorous as CLRS (it doesn't spend as much time on proving correctness mathematically), but as an implementer of algorithms, I find Skiena to be more useful on a day-to-day basis than CLRS. Skiena talks about caveats and pitfalls that come up when attempting to implement the algorithm on real hardware (e.g. cache-locality issues, high constant factors, etc.) that CLRS skims over.

Another vote for Skiena; CLRS for reference, Skiena for how to think about solving problems with algorithms.

For me, Skiena had the added bonus of being an enjoyable read which isn't always the case for algorithm books. I highly recommend it.

Another excellent algorithms book that never seems to get any attention is Udi Manber's "Introduction to Algorithms: A Creative Approach". Unlike the standard "algorithm catalog" books, where the standard algorithms are merely presented, it really gives you an idea of how one could come up with them in the first place, focusing on arguments by mathematical induction which then naturally translate into recursion/iteration. It's really invaluable for being able to come up with your own algorithms, when a non-standard problem hits you.

The slight downside is that sometimes the given algorithms are not quite as worked out in detail as in some other textbooks. Yet it's probably the algorithms book that has taught me the most.

I came here to make exactly the same recommendation about Udi Manber's book.

When designing an algorithm, you ought to make sure that it actually works. At some point, then, you're going to have to prove it. Which for most people is hard, in part because most algorithms books present the proofs as a fait accompli. They just show up, fully formed.

But Manber argues, and demonstrates convincingly in his book, that the design and the proof both become easier if you do them together. The design guides the proof, the proof the design.

He's right. Since reading the book, I don't do it any other way.

The book, tragically, costs about $110. But if you care about this stuff, it's worth it.

For those who don't know: "CLRS" is "Introduction to Algorithms" by Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest and Clifford Stein. http://en.wikipedia.org/wiki/Introduction_to_Algorithms

Though I agree to most of what you said, for learning algorithms you need not think about low level memory management.

I had used CLRS many times to explain algorithms to non-CS students and collaborators ( who almost always preferred dynamically typed scripting languages like python and to be honest were scared of ints and floats ). At the same-time some of them were very good in algorithm design, and came up with clever (algorithmic) ways to solve the problem at hand much more efficiently.

I had this experience when I was studying about Red-Black Trees. These are data structures made to make data access efficient. I'd argue that in such cases, it is important to limit the number of memory swaps and comparisons you make to keep the algorithm efficient.

But your argument does hold true when one is dealing with things like sorting or graph traversal algorithms. There the concepts are most important and you can do well without worrying about the low level details.

Yet another good book is Algorithm Design by Jon Kleinberg, Éva Tardos. What I like most about this book is that it contains tons of interesting, real-world examples and exercises that make the theory so much more fun.

The pseudo language used in CLRS has been updated and made a little more readable in 3rd edition.

Are you kidding me? The Vazirani/Dasgupta book is a joke compared to CLRS.

I learned algorithms from CLRS (as most students have), and it is bar-none, the best data structures/algorithms book on the market. The explanations are clear, detailed and rigorous.

The Vazirani/Dasgupta book does not go into as much detail. You don't believe me that this book is bad? Read some of the Amazon reviews: http://www.amazon.com/Algorithms-Sanjoy-Dasgupta/product-rev...

I don't see where we are disagreeing here - Vazirani/Dasgupta is not as detailed/rigorous as CLRS and that is precisely the point. The detail of CLRS comes at the cost of readability.

I'm not denying that I enjoyed learning from CLRS - but I recollect having to take more effort to parse its detailed pseudo-code than what a higher level of abstraction would've taken.

This is where a book with less detail like Vazirani can help. I would never recommend relying on a single text for studying anything - least of all Algorithms. Each of these complement the others in a nice way and being able to look at the same concept from the perspective of different authors always help.

> The Vazirani/Dasgupta book does not go into as much detail. You don't believe me that this book is bad? Read some of the Amazon reviews: http://www.amazon.com/Algorithms-Sanjoy-Dasgupta/product-rev....

You've pointed to the 3 one-star reviews instead of the 17 five-star reviews of the book. Was that a mistake? Joking aside, those reviews seem to be from people who've tried to use Vazirani as their sole Algorithms text. Having seen CLRS first, I've always approached Vazirani as a complement to CLRS and that worked.

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