I'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.
A Comparison of Four Algorithms Textbooks (2016)
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Re: A Comparison of Four Algorithms Textbooks (2016)
#32Honestly, I'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'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 t…
Where does it say that he has read all these books cover to cover?
Re: A Comparison of Four Algorithms Textbooks (2016)
#33I didn't realize there were that many textbooks that just covered four algorithms.
Re: A Comparison of Four Algorithms Textbooks (2016)
#34The criticism of Knuth's use of MIX seems a bit off because: + 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. + MIX is a good abstraction of the Von Neumann architecture and that's where the analysis of actual programs occurs. + MIX prog…
I never read Knuth but after swimming in FP and above for a while now, one thing that hit me is that:
1) even the most abstract things are still a bunch of tiny operations; but with structure
2) I peeked at Sutherland Sketchpad Thesis, and it's mostly 1).. full of 60s machine assembly but, it's nicely structured, very regular. I had probably more pleasure reading this than all of the OOP book I know combined.
Re: A Comparison of Four Algorithms Textbooks (2016)
#35Honestly, I'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'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 t…
"Common" probably not. Knuth is large, CLRS too but less so. People needing or craving such amount of knowledge aren't a lot.
As other said, lots have CLRS as a reference bible to pick a solution or read about one subject or one idea in particular.
Re: A Comparison of Four Algorithms Textbooks (2016)
#36I’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, Algorithms should be in there too but the author apparently didn’t read that. 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 ha…
The Sedgewick text is quite good but even better are his classes on Coursera! Very accessible and built around practical exercises.
I found the assignments to his class to be really weird and challenging. The first one had you fill in a few functions of a half-complete percolation simulation, but without having written the other part of it, it felt like having to reverse engineer and guess at the solution. There was no real way to measure your progress. Either it runs or it doesn't, and there's no incremental progression towards a solution.
I'm curious if anyone else experienced this or could point out where I went wrong. I really wanted to do the class and I liked the lectures, but i started it after finishing Gardner's Stanford algorithms Coursera class which had some of my favorite exercises ever. They had you write an algorithm in it's entirety, gave you sample data sets to check against, and let you write it in any language. Compared with that, Sedgwick's assignments were like coding with one hand tied behind my back.
Re: A Comparison of Four Algorithms Textbooks (2016)
#37I’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, Algorithms should be in there too but the author apparently didn’t read that. 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 ha…
Oh, dear lord, the algorithm descriptions and pseudocode in research papers... A while back, I played around with Earley parsing. The Earley parsing algorithm, the basic one, is pretty simple. In fact, it's almost recursive descent, if you didn't use recursion, but managed the current state of the parse manually. Unfortunately, the original algorithm was described as dynamic programming, which was the hot new thing a…
I fully agree, having been looking into the same papers. It also doesn't help that each author start out with their own version of the basic implementation which often won't match the implementation the other authors use.
I think I finally managed to get the SPPF to get the right structure now, but it is way harder to penetrate compared to the complexity of the underlying idea.
Now I only have to figure out how to actually iterate over the different parse trees..
Re: A Comparison of Four Algorithms Textbooks (2016)
#38I have Knuth's series on my bookshelf. It looks great!... especially since I haven't broken the bindings by actually reading any of it...
Re: A Comparison of Four Algorithms Textbooks (2016)
#39Which book/website has the best exercises for developing algorithmic problem-solving skill? I've started working through Skiena's exercises, but haven't really looked at much else.
I enjoyed reading Anany Levitin's Introduction to the Design and Analysis of Algorithms. Algorithms in this book are organized around problem-solving techniques - Decrease-and-Conquer, Divide-and-Conquer, Transform-and-Conquer etc - rather than application areas. I found this style very useful to develop an arsenal of techniques I can employ when I come across a new problem.
Also, for practicing programers, studying data structures is more important than algorithms. I often refer to Sally Goldman's book A Practical Guide to Data Structures and Algorithms Using Java for this.
Re: A Comparison of Four Algorithms Textbooks (2016)
#40I'd actually add one thing: coding style.
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/c++ functions gluing the wildcard to the type, then space then boom variable name. I've always been cool with this, even reading things like glibc's code.
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'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 is a great book really!). I'm actually jealous of those more advanced human beings who are able to make an abstraction of that when reading a coding book!