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The final algorithm takes the six most significant bits of the size of the array, adds one if any of the remaining bits are set, and uses that result as the minrun.

This algorithm works for all arrays, including those smaller than 64; for arrays of size 63 or less, this sets minrun equal to the array size and Timsort reduces to an insertion sort.

This is done by merging an identified subsequence, called a run, with existing runs until certain criteria are fulfilled.

Timsort has been Python's standard sorting algorithm since version 2.3.

Say, for example, two runs A and B are to be merged, with A as the smaller run.

In this case a binary search examines A to find the first position larger than the first element of B(aʹ). When aʹ is found, the algorithm can ignore elements before that position while inserting B.

First Timsort performs a binary search to find the location in the first run of the first element in the second run, and the location in the second run of the last element in the first run.

A reference to each run is then pushed onto a stack.

using insertion sort to combine runs smaller than the minimum run size (minrun), and merge sort otherwise.

It uses techniques from Peter Mc Ilroy's "Optimistic Sorting and Information Theoretic Complexity", in Proceedings of the Fourth Annual ACM-SIAM Symposium on Discrete Algorithms, pp. It was implemented by Tim Peters in 2002 for use in the Python programming language.

The algorithm finds subsequences of the data that are already ordered, and uses that knowledge to sort the remainder more efficiently.

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