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However, the other books are probably a better match for a standard university algorithm course curriculum.
They give a brilliant presentation of Shor's factorization algorithm, and how quantum computers offer an amazingly natural way to do FFT's (a key aspect of the Shor algorithm).
I would not contest the OP's questioning of the choice of a chapter on QC in an undergrad algorithms textbook. I would, however, offer the standard "your mileage may vary" caveat to the OP's very negative characterization. I had no idea what QC was about, and this chapter provided me with a great "on-ramp" to understanding what QC is all about.
Which 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.
1. Breaking down a problem statement into a heuristic pattern,
2. Analyzing the best, average and worst case complexities of possible solutions,
3. Mapping the heuristic(s) to the data structures and algorithms with the most optimal complexity,
4. Implementing the code and having it successfully run, with runtime performance feedback.
I think the aforementioned competition websites are better than things like Project Euler because solving the problem requires running actual code instead of just giving a correct answer. That makes them much more interactive (and harder, in my opinion), because you might have to obey particular performance or complexity constraints, and you can receive feedback about how efficient your solution is.
I wouldn't say that practicing these problems will make you a better software developer, in the sense that you can develop maintainable software to solve business problems in a team setting with a large codebase, and I make no comment on whether these sorts of problems are optimal filters for tech interviews (that's a dead horse). But much like mathematics, programming (and specifically algorithm analysis) is not a spectator sport. You can't efficiently learn the material just by reading it, you have to do it, just as mathematical maturity comes about by solving many mathematical problems in different domains. To that particular end, I would say that competitive programming is about as perfect a formulation of practice as you can get for improving algorithmic problem-solving skills.
Which 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.
If you're actually interested in drilling algorithm exercises - in which you map a problem statement to a particular algorithm and time/space complexity, then implement the solution - competitive programming websites are the best way to practice. There's a good number of them now, including TopCoder, LeetCode, CodeForces, SPOJ, HackerRank, etc. You can typically sort the problems by difficulty or solution acceptance…
I know. Apart from it being fun, I'm mostly interested for the social-proof value in interviews. I don't want to beat the dead horse either, but it's definitely a socially useful skill.
Thanks for the suggestions.
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, 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…
Honestly, 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…
I agree that Sedgewick belongs in this comparison, but I can't fault the author for not having read it. I think it's the book I would most easily recommend to others; it'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.
It'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!). I agree that Sedgewick belongs in this comparison, but I can't fault the author for not having read it. I think it's the book I would most easily recommend to others; it's quite clear and has lots of very nice visualizations.…
Skiena on the other hand does a nice job of both describing the algorithm in a straightforward manner and also getting you into the algorithm headspace.
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, 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.