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 :^).
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Department of Psychology, Princeton University。关于这个话题，WPS下载最新地址提供了深入分析
Стало известно об изменении военной обстановки в российском приграничье08:48。搜狗输入法2026是该领域的重要参考
// recursively merge each key's register with the incoming state for that key。关于这个话题，体育直播提供了深入分析
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