2024/11/07

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2024-11-07 17:50:49 +0100 <merijn> Inst: I've used the Haskell map's with 10 thousands of entries in relatively tight loops and the performance was just fine
2024-11-07 17:50:16 +0100merijn(~merijn@77.242.116.146) merijn
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2024-11-07 17:48:03 +0100 <c_wraith> Ah. Well. Solving the halting problem is also trivial is you just start with a table of all the answers.
2024-11-07 17:46:57 +0100 <c_wraith> The funniest counter-argument I heard to "hashing isn't O(1)" was "it is if you cache it."
2024-11-07 17:46:21 +0100 <Inst> like, seq is more efficient than list asymptotically, but benchmarks show that in many practical case list pulls ahead
2024-11-07 17:46:04 +0100 <Inst> tbh at O(ln n) and below, I suspect you can easily have performance dominated by constant factors?
2024-11-07 17:45:50 +0100JamesMowery43(~JamesMowe@ip68-228-212-232.ph.ph.cox.net) JamesMowery
2024-11-07 17:44:28 +0100 <merijn> Most people handwave/ignore the cost of computing a hash of the key, handwave the possibility of complexity degradation due to collisions
2024-11-07 17:42:28 +0100 <merijn> Meanwhile hashmaps are O(1) (with sufficient handwaving) average case and O(n) worst case complexity. And you need a pretty decent amount of memory bloat to avoid hitting the O(n) case
2024-11-07 17:41:35 +0100 <merijn> Inst: Haskell's immutable maps use storage linear in number of elements and are O(log n) average *AND* worst case complexity
2024-11-07 17:41:02 +0100 <merijn> Inst: Bit late, but there's also the issue of 1) memory complexity and 2) avg case vs worst case complexity
2024-11-07 17:40:05 +0100DigitteknohippieDigit
2024-11-07 17:39:51 +0100 <merijn> hashtables are the most overrated datastructure IMHO
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2024-11-07 16:42:23 +0100 <c_wraith> hash tables have more assumptions going into those O(1) claims than most data structures. In fact, those assumptions add up to having a bounded key space, in which case you claim any lookup strategy is O(1)
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