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by afiodorov
524 days ago
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All I need is the proportion of the qualifying numbers to the input array to run the algorithm and the number of samples. Then we can sample min, max index of the qualifying array and return their difference without having to sample many times if we can derive the joined min max distribution conditional on the Bernoulli. In other words the procedure can take any input array and qualifying criteria. The joint distribution is relatively simple to derive. (This is related to the fact that min, max of continuous uniform on 0, 1 are Beta distributions.) |
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The O(1) method based on statistics only works when the function making this calculation can hide the array (or lack of array) behind a curtain the entire time. If it has to take an array as input, or share its array as output, the facade crumbles.
The prompt is not "generate this many random numbers and then say max qualifying minus min qualifying". If it was, your method would give valid solutions. But the prompt starts with "Given a list".
In the article, we let ChatGPT generate the random numbers as a matter of convenience. But the timing results are only valid as long as it keeps that part intact and isolated. We have to be able to swap it out for any other source of random numbers. If it invents a method that can't do that, it has failed.