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Reminds me of the Caltech required 3rd year math course "AMa95 Introductory Methods in Applied Mathematics", that came with the dry comment that "Introductory does not mean elementary." It had a reputation as one of the toughest classes.
I don't think that's quite true. The only C++ compilers I know of are:
1. cfront C++ (from which many C++ compilers were derived)
2. Digital Mars C++ (the one I wrote)
3. g++
4. clang++
5. EDG C++ (from which many other C++ compilers are derived)
6. Microsoft C++
7. Taumetric C++
It's a pretty thin list for 30 years. (I apologize if I neglected any.) Contrast that with C - at one time in the 1980s I counted over 30 of them (I'm not sure how many were independent creations).
Edit: removed Borland C++ as that was derived from Taumetric.
I've read the books on both of those lists, among many others. Not because these are all required reading, but because I've had a long career and I've fought to continually improve my knowledge in Computer Science. It seems rather arrogant of the author to decree that one list is canon and the other list is not. Each of those books has its use. Does this mean that someone needs to read any of those books to be good a…
I guess for the same reasons people want to bash the current interviewing institution in place.
Personally, I go with "it is what it is, deal with it if you want THAT job"
Back to the context, I think I find CLRS much more approachable than TAOCP. It relies on pseudo-code and fairly simple mathematical symbology to formalise the algorithms it is treating.
With TAOCP I have tried by can't get past the language of the imaginary CPU which it uses.
Also, there are other simply excellent texts on Algorithms which I personally found much more useful than TAOCP (in decreasing order of "easiness")
- Python Algorithms
- The Algorithm Design Manual
- CLRS
- Algorithms in Java (somehow I found CLRS easier to follow than this)
- And then I found this recently, "Algorithms on Strings,Trees and Sequences: Computer Science and Computational Biology" (one should have at least gone through the first two recommendations before starting with this one to make it easier to read this one)
Disclaimer : I have a growing interest in learning Algorithms due to the requirements of interviews, however I have come to believe that the knowledge will go a long way.
> There's certainly plenty of evidence that someone has read this book, since otherwise we wouldn't have so many C++ compilers to choose from. I don't think that's quite true. The only C++ compilers I know of are: 1. cfront C++ (from which many C++ compilers were derived) 2. Digital Mars C++ (the one I wrote) 3. g++ 4. clang++ 5. EDG C++ (from which many other C++ compilers are derived) 6. Microsoft C++ 7. Taumetric…
> There's certainly plenty of evidence that someone has read this book, since otherwise we wouldn't have so many C++ compilers to choose from. I don't think that's quite true. The only C++ compilers I know of are: 1. cfront C++ (from which many C++ compilers were derived) 2. Digital Mars C++ (the one I wrote) 3. g++ 4. clang++ 5. EDG C++ (from which many other C++ compilers are derived) 6. Microsoft C++ 7. Taumetric…
Earlier quoted context omitted.
He praises almost all of the books on the second list, he just thinks a large percentage of the people that recommend them haven't read much of them. He also doesn't seem to make the claim that the first list represents "canon" and that the second list represents "non-canon". The first list is a list of great books that are easily understood by most students. Many of the books on the second list are far more advanced…
He gives some rather backhanded praise for TAOCP, if you can consider noting that a set of books look impressive on a bookshelf as praise. For instance, "I've used my set several times when I was stuck and couldn't find help anywhere else. But TAOCP is always my reference of last resort. It's very dense and academic, and the examples are all in assembly language." He insinuates pretty strongly that the first set of b…
Isn't that just agreeing with your point here:
> Knuth is an excellent teacher for the right sort of student. He's not for everyone, and there's nothing wrong with that.
I've read the books on both of those lists, among many others. Not because these are all required reading, but because I've had a long career and I've fought to continually improve my knowledge in Computer Science. It seems rather arrogant of the author to decree that one list is canon and the other list is not. Each of those books has its use. Does this mean that someone needs to read any of those books to be good a…
I bought Knuth's TAOCP and the Cormen book in high-school to prepare for programming competitions. I never read TAOCP and probably never will, it was simply too complex for what I needed.
CLRS was significantly easier to understand and I was able to read subchapters.
Today I use neither, but rather recommend Skiena, Sedgewick or other specialised books. Not because TAOCP and CLRS are bad, but because these other books are more approachable and also get the job done.
I've read the books on both of those lists, among many others. Not because these are all required reading, but because I've had a long career and I've fought to continually improve my knowledge in Computer Science. It seems rather arrogant of the author to decree that one list is canon and the other list is not. Each of those books has its use. Does this mean that someone needs to read any of those books to be good a…
I read the Dragon book, actually studied it for a CS course. I read C for the same reason and because it was the time when either you read books or you didn't know stuff. Most of the other books are more recent and the web was already there and good enough, especially for references. Those get old fast on paper.