we assign a minterm id to each of these classes (e.g., 1 for letters, 0 for non-letters), and then compute derivatives based on these ids instead of characters. this is a huge win for performance and results in an absolutely enormous compression of memory, especially with large character classes like \w for word-characters in unicode, which would otherwise require tens of thousands of transitions alone (there’s a LOT of dotted umlauted squiggly characters in unicode). we show this in numbers as well, on the word counting \b\w{12,}\b benchmark, RE# is over 7x faster than the second-best engine thanks to minterm compressionremark here i’d like to correct, the second place already uses minterm compression, the rest are far behind. the reason we’re 7x faster than the second place is in the \b lookarounds :^).
Additionally, it makes it easy to take the order of options into account or，更多细节参见heLLoword翻译官方下载
Трамп определил приоритетность Украины для США20:32。业内人士推荐safew官方版本下载作为进阶阅读
Названа исполнительница роли Наташи Ростовой в «Войне и мире» Андреасяна14:45