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Last year, two in five Americans did not read a single book, and reading for pleasure has plummeted about 40% over the past two decades. Yet many of the world’s most successful people credit reading as central to their curiosity, critical thinking, and leadership. A JPMorgan survey released last year of more than 100 billionaires found that reading ranked as the top habit elite achieves have in common.
NFAs are cheaper to construct, but have a O(n*m) matching time, where n is the size of the input and m is the size of the state graph. NFAs are often seen as the reasonable middle ground, but i disagree and will argue that NFAs are worse than the other two. they are theoretically “linear”, but in practice they do not perform as well as DFAs (in the average case they are also much slower than backtracking). they spend the complexity in the wrong place - why would i want matching to be slow?! that’s where most of the time is spent. the problem is that m can be arbitrarily large, and putting a large constant of let’s say 1000 on top of n will make matching 1000x slower. just not acceptable for real workloads, the benchmarks speak for themselves here.