Quick Sort

November 3, 2009

Quick sort was invented by Sir Charles Antony Richard “Tony” Hoare, a British computer scientist and winner of the 1980 Turing Award, in 1960, while he was a visiting student at Moscow State University. Though it has an annoying quadratic worst-case performance, quick sort has expected O(n log n) performance and is significantly faster in practice than most other O(n log n) sorting algorithms, and it is possible to arrange that the worst-case performance almost never happens for real-world data.

Quick sort works as follows: First, an element, called the pivot, is chosen from the array. Then the array is partitioned around the pivot by reordering the array so that all elements less than the pivot come before it and all items greater than the pivot come after it; this puts the pivot element in its final position in the array. Finally, quick sort is called recursively on the less-than and greater-than partitions; the base of the recursion is arrays of zero or one element.

There are many ways to choose the pivot element. Some algorithms choose the first element of the array, or the last; others choose the median of three elements (first, middle, last). Our preference is to choose an element at random, since that virtually eliminates the possibility of quadratic performance (unless there is collusion between the random-number generator and the data).

Likewise, there are many ways to perform the partitioning. One approach, due to Nick Lomuto, uses a single pointer to run through the sub-array, swapping the current element for the last element of the sub-array, which is then decremented, if it is greater than the pivot. Another approach uses two pointers that approach each other, swapping elements when the two pointers cross over the pivot element. Lomuto’s partition is simpler; dual pointers is quicker, but the details are hard to get right.

Your task is to write a function that sorts an array using the quick sort algorithm, using the conventions of a previous exercise. When you are finished, you are welcome to read or run a suggested solution, or to post your own solution or discuss the exercise in the comments below.

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