Compression, synchronization and backup systems often use rolling hash to implement "content-defined chunking", an effective form of deduplication.
In optimized implementations, Rabin-Karp is likely to be the bottleneck. See for instance https://github.com/facebook/zstd/pull/2483 which replaces a Rabin-Karp variant by a >2x faster Gear-Hashing.
> I would love to hear of other low(er) barrier-to-entry ways to use LaTeX, because it’s a pretty steep commitment for someone who isn’t a professional writer.
I have been working on and off on a fork of LaTeX with real-time feedback: you can see the document and error messages rendered and updated live. It also supports SyncTeX (going from a source line to the corresponding output and vice-versa).
I added vim support recently, you can see it in action there: https://github.com/let-def/texpresso.vim
There is also emacs support in the main repo (https://github.com/let-def/texpresso). That's all quite experimental, but I hope to do a first release next year. It should work on most unix, I test with Linux and macOS.
I think what you suggest is possible, derivation might even be well suited for this application, however I can't tell if it would be better than existing approaches.
There are some chances that it might be interesting in practice, since it seems that this application of derivatives has not been much studied, but that's highly speculative.
> Does there exist a regex engine I can try that uses derivatives and supports large Unicode classes and purports to be usable for others? :-)
I don't know any besides ocaml-re that Drup already linked, sorry :).
And sorry that my comment is hard to decipher. I think the core point is that the "character set" can be an abstract type from the point of view of the derivation algorithm.
So it doesn't matter how they are represented, nor "how big" a character set is.
With Antimirov's derivative (which produces an NFA), there is no constraint on this type.
With Brzozowski's derivative, you need at least the ability to intersect two character sets. So the type should implement a trait with an intersection function (in Rust syntax, `trait Intersect fn intersect(self, Self) -> Self`). That's necessary for any implementation generating a DFA anyway.
And if you also want to deal with complementation, then a second method `fn negate(self) -> Self` is necessary.
I don't think you should be worried about Unicode in particular. Although the derivation formula on paper is parameterized by a character, you don't have to compute the derivative for every character separately.
It's actually easy to compute classes of characters that have the same derivative (it's done in the linked "Regular-expression derivative re-examined" paper, although their particular implementation favors simplicity over efficiency), and it's not even necessary when using Antimirov's partial derivatives.
Actually, the complexity of the derivation is independent of the size of the alphabet. You could even define derivation on an arbitrary semi-lattice, not necessarily a set of characters. (Or a boolean algebra if you care about negation/complementation).
The difficulty in handling unicode has more to do with the efficiency of the automaton representation and manipulation rather than in turning the RE in an NFA or DFA.
Of course, this is nitpicking :). For all practical purpose, this O(log n) is O(1).
If you are interested, I can try to recover the proof that the rotation step can be done in O(n), thus allowing to apply the master theorem on the main recursion and getting the O(n log n) result.
I wrote a similar sorting algorithm around 14 years ago (fast, inplace, adaptive merge sort algorithm using rotation/swap, and I think, stable). This is all I remember from the time...
I was able to prove (on paper) that the whole algorithm was actually O(n log n). The moves seems to have a bad recursion pattern, but assuming my proof was right, it is possible to have a well behaved implementation in practice.
However, the recursion pattern didn't fit the master theorem, I remember having to do a taylor expansion of some formula giving an upper bound on the number of computation steps to prove the O(n log n) bound.
And runtime measurements graph were showing the expected O(n log n) curve, as in your cases. So maybe they are not O(n log n ^ 2) as you feared...
I did not keep much information from that time :P.