A Comparison of Four Algorithms Textbooks (2016)

The criticism of Knuth&#x27;s use of MIX seems a bit off because:<p>+ Assembly language level instruction execution is a good way of talking about the running time of algorithms at a finer level of detail than Big O notation. Finer grain than Big O is helpful when analyzing and optimizing programs.<p>+ MIX is a good abstraction of the Von Neumann architecture and that&#x27;s where the analysis of actual programs occurs.<p>+ MIX programs are still accessible to a general auidence 50 years after they were written. No actual programming language available in 1967 is still similarly accessible to such a degree.<p>+ MMIX is the successor of MIX...but in so far as learning MIX is an issue, it&#x27;s more complex child probably is not an improvement.<p>The art of MIX is that it keeps the reader&#x27;s focus on the fact that computer programs ultimately load registers, read memory, execute mathematical primatives, and branch no matter what object-oriented, functional, or type safe abstractions a high level language might offer.

I’m surprised, but I like this (I typically don’t like or agree with textbook comparisons, but I think this tour is mostly right). The Sedgewick text, <i>Algorithms</i> should be in there too but the author apparently didn’t read that.<p>My own experience basically agrees: I’ve read and enjoy Skiena, it’s written in clear style and it’s the “cover to cover” text for a working developer or for interview practice. But I also have TAOCP and CLRS on my shelf, and I haven’t read either of them. I’ve certainly <i>used</i> both of them a lot, but I simply haven’t read all of them because I use them more as reference texts.<p>Personally I find it bothersome that these textbooks are written with idiosyncratic pseudocode. In my opinion, many of these authors lose their grasp of common implementation difficulties if they don’t provide students with working code that will compile and run. It’s easy to throw down the theory and some okay-ish pseudocode while in effect saying, “...and the rest is just a minor implementation detail, which I leave as an exercise to the reader...”

Honestly, I&#x27;m blown away that someone has actually read cover to cover Knuth, CLRS, Dasgupta and Skiena. Is this a common thing? Has anyone here done something similar? For years I&#x27;ve had the textbooks of CLRS and Skiena at home (and a pdf of Dasgupta) but they are used only in the event I need to drill down to understand a particular algorithm to solve a particular problem. I feel that the most effective use of my time is to have a birds view of the landscape (i.e understand the categories of algorithms and the problems they solve) and dive down only when I actually need to know the nitty gritty to implement a solution for a real problem. I can understand the joy of learning them all just for fun, but who has the time? I wonder if he did all the exercises too... :-)

Many grad level algorithm courses still use Dexter C. Kozen&#x27;s book The Design and Analysis of Algorithms, Springer-Verlag, 1992. It&#x27;s a timeless book with clear analysis.<p>There&#x27;s also this excellent free draft on analysis of common undergrad algorithms for parallelism <a href="http:&#x2F;&#x2F;www.parallel-algorithms-book.com&#x2F;" rel="nofollow">http:&#x2F;&#x2F;www.parallel-algorithms-book.com&#x2F;</a><p>TAOCP is more than an algorithms book, Knuth even have strategies for writing lengthy programs from scratch, how to build test libraries, how to optimize a program to make it cache memory friendly ect.

I became curious about quantum computing a few years ago. Googling around, I came across the QC chapter in Dasgupta. I found their description of the topic to be excellent, exactly what I was searching for. They provide a substantive presentation of QC, as contrasted with many &quot;pop sci&quot; hand-wavy impressionistic descriptions of QC. They tactfully avoid a few of the extremely difficult proofs, but they do give a solid mathematical underpinning for the subject. And, they convey in prose what the point of the whole thing is. Also, they forthrightly express the mysterious nature of what seems to be going on here. (If you have an N-qubit quantum computer, the universe seems to somehow maintain and manipulate a vector of 2^N complex elements as a quantum computation proceeds.)<p>They give a brilliant presentation of Shor&#x27;s factorization algorithm, and how quantum computers offer an amazingly natural way to do FFT&#x27;s (a key aspect of the Shor algorithm).<p>I would not contest the OP&#x27;s questioning of the choice of a chapter on QC in an undergrad algorithms textbook. I would, however, offer the standard &quot;your mileage may vary&quot; caveat to the OP&#x27;s very negative characterization. I had no idea what QC was about, and this chapter provided me with a great &quot;on-ramp&quot; to understanding what QC is all about.

It&#x27;s odd to me that the author describes CLRS as graduate-level. It (back when it was CLR and in its first edition) was the text in my undergrad Introduction to Algorithms class (taught by L!).<p>I agree that Sedgewick belongs in this comparison, but I can&#x27;t fault the author for not having read it. I think it&#x27;s the book I would most easily recommend to others; it&#x27;s quite clear and has lots of very nice visualizations. I do think that Skiena deserves a special mention for explicitly being about how to come up with your own fundamental algorithms and data structures rather than just plug an existing one into your program.

Kleinberg&#x2F;Tardos book that I am reading right now needs trimming for brevity. A good math text editor could, probably, easily lop off 1&#x2F;3 of it without affecting its content. Otherwise I prefer it to most other algo books because its proofs are closest to how mathematicians approach their proofs. By contrast, I am confused as to why CLRS contains proofs at all. At the beginning of the book, the authors say something to the effect of actual proofs being too messy&#x2F;hard so they simply wave their hands through them. But if most any intro discrete math books can do these proofs, why can&#x27;t CLRS? To me it&#x27;s a turn-off.

Which book&#x2F;website has the best exercises for developing algorithmic problem-solving skill? I&#x27;ve started working through Skiena&#x27;s exercises, but haven&#x27;t really looked at much else.

Skiena is very good - Algorithms are lucidly explained - Actual code is provided - Its relatively newer but highly recommended<p>Have read only parts of Knuth &#x2F; CLRS - They are good, but for real problems analysis , would prefer Sienna

I&#x27;d actually add one thing: coding style.<p>Shame on me for admitting this! In fact, despite having personal CS favors like everyone, the painfully obvious subjectivity of the whole matter has always striked me as entirely futile to be taken into account for basically everything. I even worked for years of uninterrupted peace with people who would, for example, prototype pointer arguments of c&#x2F;c++ functions gluing the wildcard to the type, then space then boom variable name. I&#x27;ve always been cool with this, even reading things like glibc&#x27;s code.<p>This liberalism however has two exceptions: my own code obviously, a dictatorship where merciless enforcement of inflexible and rigorous coding style is accepted. But unfortunately, in algorithm books too! I know it is completely idiotic but I&#x27;d be in denial not to admit how much of a total turn off the coding style of code in algorithm books can be to me. Segdewick for example, such a wonderful book, the prose is excellent, the content really is outstanding (probably one of the most comprehensive I own), the editions I have even has a superb typeface and paper quality, unfortunately the C coding style of this book has this effect on me which makes me want to close the book immediately. I feel the same deep pain every time I have to look at it (which does happen a lot since it <i>is</i> a great book really!). I&#x27;m actually jealous of those more advanced human beings who are able to make an abstraction of that when reading a coding book!

I&#x27;ve used dasgupta in my ug algorithms class at Berkeley, which was actually taught by papaD one of the coauthors. I love the text because it is so concise. A great book to get started on algorithms. A good exercise is to implement them in the language of your choice.

As a self-taught developer, I prefer the Skiena book. The topics are ordered in a way that make sense to me, and the examples are practical.<p>However, the other books are probably a better match for a standard university algorithm course curriculum.

Can we get a comparison of Algorithms undergrad courses? :)

I have Knuth&#x27;s series on my bookshelf. It looks great!... especially since I haven&#x27;t broken the bindings by actually reading any of it...

This is an interesting read because Dasgupta is what the current OMSCS algorithms class is using.

I didn&#x27;t realize there were that many textbooks that just covered four algorithms.