If you want the n largest item i would recommend quicksort but at each partition you only recurse into the side of the pivot that has the values you care about. This is easy to determine because you know how many items are on either side of the pivot and you know that you want the nth item. This makes it take N+1/2N +1/4N... time, which telscopes to 2N or O(N) rather than n log(n) of sorting algorithms. I've found empirically this beats the pants of quicksorting and taking the nth value. I don't know of a way to do this in numpy. I think it would require adding a cfunction to numpy. Perhaps an "argnth" function? Does anyone else know of an existing mechanism? On 12/12/06, asaf david <asafdav2@gmail.com> wrote:

Hello Let's say i have an N sized array, and i want to get the positions of the K largest items. for K = 1 this is simply argmax. is there any way to generalize it for k !=1? currently I use argsort and take only K items from it, but I'm paying an additional ~lg(N)...

Thanks in advance, asaf

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