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Quicksort was developed by British computer scientist Tony Hoare in 1959 [1] and published in 1961. [2] It is still a commonly used algorithm for sorting. Overall, it is slightly faster than merge sort and heapsort for randomized data, particularly on larger distributions. [3] Quicksort is a divide-and-conquer algorithm. It works by selecting a ...
qsort is a C standard library function that implements a sorting algorithm for arrays of arbitrary objects according to a user-provided comparison function. It is named after the "quicker sort" algorithm [1] (a quicksort variant due to R. S. Scowen), which was originally used to implement it in the Unix C library, although the C standard does not require it to implement quicksort.
Sorting algorithms are prevalent in introductory computer science classes, where the abundance of algorithms for the problem provides a gentle introduction to a variety of core algorithm concepts, such as big O notation, divide-and-conquer algorithms, data structures such as heaps and binary trees, randomized algorithms, best, worst and average ...
Multi-key quicksort, also known as three-way radix quicksort, [1] is an algorithm for sorting strings.This hybrid of quicksort and radix sort was originally suggested by P. Shackleton, as reported in one of C.A.R. Hoare's seminal papers on quicksort; [2]: 14 its modern incarnation was developed by Jon Bentley and Robert Sedgewick in the mid-1990s. [3]
Samplesort is a generalization of quicksort. Where quicksort partitions its input into two parts at each step, based on a single value called the pivot, samplesort instead takes a larger sample from its input and divides its data into buckets accordingly. Like quicksort, it then recursively sorts the buckets.
In analysis of algorithms, probabilistic analysis of algorithms is an approach to estimate the computational complexity of an algorithm or a computational problem. It starts from an assumption about a probabilistic distribution of the set of all possible inputs.
A randomized algorithm is an algorithm that employs a degree of randomness as part of its logic or procedure. The algorithm typically uses uniformly random bits as an auxiliary input to guide its behavior, in the hope of achieving good performance in the "average case" over all possible choices of random determined by the random bits; thus either the running time, or the output (or both) are ...
As a baseline algorithm, selection of the th smallest value in a collection of values can be performed by the following two steps: . Sort the collection; If the output of the sorting algorithm is an array, retrieve its th element; otherwise, scan the sorted sequence to find the th element.