Introsort

Last updated
Introsort
Class Sorting algorithm
Data structure Array
Worst-case performance O(n log n)
Average performance O(n log n)
Optimalyes

Introsort or introspective sort is a hybrid sorting algorithm that provides both fast average performance and (asymptotically) optimal worst-case performance. It begins with quicksort, it switches to heapsort when the recursion depth exceeds a level based on (the logarithm of) the number of elements being sorted and it switches to insertion sort when the number of elements is below some threshold. This combines the good parts of the three algorithms, with practical performance comparable to quicksort on typical data sets and worst-case O(n log n) runtime due to the heap sort. Since the three algorithms it uses are comparison sorts, it is also a comparison sort.

Contents

Introsort was invented by David Musser in Musser (1997), in which he also introduced introselect, a hybrid selection algorithm based on quickselect (a variant of quicksort), which falls back to median of medians and thus provides worst-case linear complexity, which is optimal. Both algorithms were introduced with the purpose of providing generic algorithms for the C++ Standard Library which had both fast average performance and optimal worst-case performance, thus allowing the performance requirements to be tightened. [1] Introsort is in-place and a non-stable algorithm.

Pseudocode

If a heapsort implementation and partitioning functions of the type discussed in the quicksort article are available, the introsort can be described succinctly as

procedure sort(A : array):     maxdepth ← ⌊log2(length(A))⌋ × 2     introsort(A, maxdepth)  procedure introsort(A, maxdepth):     n ← length(A)     if n < 16:         insertionsort(A)     else if maxdepth = 0:         heapsort(A)     else:         p ← partition(A)  // assume this function does pivot selection, p is the final position of the pivot         introsort(A[1:p-1], maxdepth - 1)         introsort(A[p+1:n], maxdepth - 1)

The factor 2 in the maximum depth is arbitrary; it can be tuned for practical performance. A[i:j] denotes the array slice of items i to j including both A[i] and A[j]. The indices are assumed to start with 1 (the first element of the A array is A[1]).

Analysis

In quicksort, one of the critical operations is choosing the pivot: the element around which the list is partitioned. The simplest pivot selection algorithm is to take the first or the last element of the list as the pivot, causing poor behavior for the case of sorted or nearly sorted input. Niklaus Wirth's variant uses the middle element to prevent these occurrences, degenerating to O(n2) for contrived sequences. The median-of-3 pivot selection algorithm takes the median of the first, middle, and last elements of the list; however, even though this performs well on many real-world inputs, it is still possible to contrive a median-of-3 killer list that will cause dramatic slowdown of a quicksort based on this pivot selection technique.

Musser reported that on a median-of-3 killer sequence of 100,000 elements, introsort's running time was 1/200 that of median-of-3 quicksort. Musser also considered the effect on caches of Sedgewick's delayed small sorting, where small ranges are sorted at the end in a single pass of insertion sort. He reported that it could double the number of cache misses, but that its performance with double-ended queues was significantly better and should be retained for template libraries, in part because the gain in other cases from doing the sorts immediately was not great.

Implementations

Introsort or some variant is used in a number of standard library sort functions, including some C++ sort implementations.

The June 2000 SGI C++ Standard Template Library stl_algo.h implementation of unstable sort uses the Musser introsort approach with the recursion depth to switch to heapsort passed as a parameter, median-of-3 pivot selection and the Knuth final insertion sort pass for partitions smaller than 16.

The GNU Standard C++ library is similar: uses introsort with a maximum depth of 2×log2n, followed by an insertion sort on partitions smaller than 16. [2]

LLVM libc++ also uses introsort with a maximum depth of 2×log2n, however the size limit for insertion sort is different for different data types (30 if swaps are trivial, 6 otherwise). Also, arrays with sizes up to 5 are handled separately. [3] Kutenin (2022) provides an overview for some changes made by LLVM, with a focus on the 2022 fix for quadraticness. [4]

The Microsoft .NET Framework Class Library, starting from version 4.5 (2012), uses introsort instead of simple quicksort. [5]

Go uses a modification of introsort: for slices of 12 or less elements it uses insertion sort, and for larger slices it uses pattern-defeating quicksort and more advanced median of three medians for pivot selection. [6] Prior to version 1.19 it used shell sort for small slices.

Java, starting from version 14 (2020), uses a hybrid sorting algorithm that uses merge sort for highly structured arrays (arrays that are composed of a small number of sorted subarrays) and introsort otherwise to sort arrays of ints, longs, floats and doubles. [7]

Variants

pdqsort

Pattern-defeating quicksort (pdqsort) is a variant of introsort incorporating the following improvements: [8]

pdqsort is used by Rust, GAP, [9] and the C++ library Boost. [10]

fluxsort

fluxsort is a stable variant of introsort incorporating the following improvements: [11]

Improvements introduced by fluxsort and its unstable variant, crumsort, were adopted by crumsort-rs, glidesort, ipnsort, and driftsort. The overall performance increase on random inputs compared to pdqsort is around 50%. [12] [13] [14] [15] [16]

Related Research Articles

<span class="mw-page-title-main">Heapsort</span> A sorting algorithm which uses the heap data structure

In computer science, heapsort is a comparison-based sorting algorithm which can be thought of as "an implementation of selection sort using the right data structure." Like selection sort, heapsort divides its input into a sorted and an unsorted region, and it iteratively shrinks the unsorted region by extracting the largest element from it and inserting it into the sorted region. Unlike selection sort, heapsort does not waste time with a linear-time scan of the unsorted region; rather, heap sort maintains the unsorted region in a heap data structure to efficiently find the largest element in each step.

<span class="mw-page-title-main">Insertion sort</span> Sorting algorithm

Insertion sort is a simple sorting algorithm that builds the final sorted array (or list) one item at a time by comparisons. It is much less efficient on large lists than more advanced algorithms such as quicksort, heapsort, or merge sort. However, insertion sort provides several advantages:

<span class="mw-page-title-main">Merge sort</span> Divide and conquer sorting algorithm

In computer science, merge sort is an efficient, general-purpose, and comparison-based sorting algorithm. Most implementations produce a stable sort, which means that the relative order of equal elements is the same in the input and output. Merge sort is a divide-and-conquer algorithm that was invented by John von Neumann in 1945. A detailed description and analysis of bottom-up merge sort appeared in a report by Goldstine and von Neumann as early as 1948.

In computer science, radix sort is a non-comparative sorting algorithm. It avoids comparison by creating and distributing elements into buckets according to their radix. For elements with more than one significant digit, this bucketing process is repeated for each digit, while preserving the ordering of the prior step, until all digits have been considered. For this reason, radix sort has also been called bucket sort and digital sort.

<span class="mw-page-title-main">Sorting algorithm</span> Algorithm that arranges lists in order

In computer science, a sorting algorithm is an algorithm that puts elements of a list into an order. The most frequently used orders are numerical order and lexicographical order, and either ascending or descending. Efficient sorting is important for optimizing the efficiency of other algorithms that require input data to be in sorted lists. Sorting is also often useful for canonicalizing data and for producing human-readable output.

<span class="mw-page-title-main">Bucket sort</span> Sorting algorithm

Bucket sort, or bin sort, is a sorting algorithm that works by distributing the elements of an array into a number of buckets. Each bucket is then sorted individually, either using a different sorting algorithm, or by recursively applying the bucket sorting algorithm. It is a distribution sort, a generalization of pigeonhole sort that allows multiple keys per bucket, and is a cousin of radix sort in the most-to-least significant digit flavor. Bucket sort can be implemented with comparisons and therefore can also be considered a comparison sort algorithm. The computational complexity depends on the algorithm used to sort each bucket, the number of buckets to use, and whether the input is uniformly distributed.

In computer science, an in-place algorithm is an algorithm that operates directly on the input data structure without requiring extra space proportional to the input size. In other words, it modifies the input in place, without creating a separate copy of the data structure. An algorithm which is not in-place is sometimes called not-in-place or out-of-place.

<span class="mw-page-title-main">Self-balancing binary search tree</span> Any node-based binary search tree that automatically keeps its height the same

In computer science, a self-balancing binary search tree (BST) is any node-based binary search tree that automatically keeps its height small in the face of arbitrary item insertions and deletions. These operations when designed for a self-balancing binary search tree, contain precautionary measures against boundlessly increasing tree height, so that these abstract data structures receive the attribute "self-balancing".

In computer science, a selection algorithm is an algorithm for finding the th smallest value in a collection of ordered values, such as numbers. The value that it finds is called the th order statistic. Selection includes as special cases the problems of finding the minimum, median, and maximum element in the collection. Selection algorithms include quickselect, and the median of medians algorithm. When applied to a collection of values, these algorithms take linear time, as expressed using big O notation. For data that is already structured, faster algorithms may be possible; as an extreme case, selection in an already-sorted array takes time .

<span class="mw-page-title-main">Quickselect</span> Algorithm for the kth smallest element in an array

In computer science, quickselect is a selection algorithm to find the kth smallest element in an unordered list, also known as the kth order statistic. Like the related quicksort sorting algorithm, it was developed by Tony Hoare, and thus is also known as Hoare's selection algorithm. Like quicksort, it is efficient in practice and has good average-case performance, but has poor worst-case performance. Quickselect and its variants are the selection algorithms most often used in efficient real-world implementations.

<span class="mw-page-title-main">Quicksort</span> Divide and conquer sorting algorithm

Quicksort is an efficient, general-purpose sorting algorithm. Quicksort was developed by British computer scientist Tony Hoare in 1959 and published in 1961. 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.

sort is a generic function in the C++ Standard Library for doing comparison sorting. The function originated in the Standard Template Library (STL).

<span class="mw-page-title-main">Tree sort</span> Type of sorting algorithm

A tree sort is a sort algorithm that builds a binary search tree from the elements to be sorted, and then traverses the tree (in-order) so that the elements come out in sorted order. Its typical use is sorting elements online: after each insertion, the set of elements seen so far is available in sorted order.

Spreadsort is a sorting algorithm invented by Steven J. Ross in 2002. It combines concepts from distribution-based sorts, such as radix sort and bucket sort, with partitioning concepts from comparison sorts such as quicksort and mergesort. In experimental results it was shown to be highly efficient, often outperforming traditional algorithms such as quicksort, particularly on distributions exhibiting structure and string sorting. There is an open-source implementation with performance analysis and benchmarks, and HTML documentation .

In computer science, introselect is a selection algorithm that is a hybrid of quickselect and median of medians which has fast average performance and optimal worst-case performance. Introselect is related to the introsort sorting algorithm: these are analogous refinements of the basic quickselect and quicksort algorithms, in that they both start with the quick algorithm, which has good average performance and low overhead, but fall back to an optimal worst-case algorithm if the quick algorithm does not progress rapidly enough. Both algorithms were introduced by David Musser in, with the purpose of providing generic algorithms for the C++ Standard Library that have both fast average performance and optimal worst-case performance, thus allowing the performance requirements to be tightened.

Flashsort is a distribution sorting algorithm showing linear computational complexity O(n) for uniformly distributed data sets and relatively little additional memory requirement. The original work was published in 1998 by Karl-Dietrich Neubert.

Samplesort is a sorting algorithm that is a divide and conquer algorithm often used in parallel processing systems. Conventional divide and conquer sorting algorithms partitions the array into sub-intervals or buckets. The buckets are then sorted individually and then concatenated together. However, if the array is non-uniformly distributed, the performance of these sorting algorithms can be significantly throttled. Samplesort addresses this issue by selecting a sample of size s from the n-element sequence, and determining the range of the buckets by sorting the sample and choosing p−1 < s elements from the result. These elements then divide the array into p approximately equal-sized buckets. Samplesort is described in the 1970 paper, "Samplesort: A Sampling Approach to Minimal Storage Tree Sorting", by W. D. Frazer and A. C. McKellar.

In computer science, partial sorting is a relaxed variant of the sorting problem. Total sorting is the problem of returning a list of items such that its elements all appear in order, while partial sorting is returning a list of the k smallest elements in order. The other elements may also be sorted, as in an in-place partial sort, or may be discarded, which is common in streaming partial sorts. A common practical example of partial sorting is computing the "Top 100" of some list.

Multi-key quicksort, also known as three-way radix quicksort, 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; its modern incarnation was developed by Jon Bentley and Robert Sedgewick in the mid-1990s. The algorithm is designed to exploit the property that in many problems, strings tend to have shared prefixes.

Proportion extend sort is an in-place, comparison-based sorting algorithm which attempts to improve on the performance, particularly the worst-case performance, of quicksort.

References

  1. ↑ "Generic Algorithms", David Musser
  2. ↑ libstdc++ Documentation: Sorting Algorithms
  3. ↑ libc++ source code: sort
  4. ↑ Kutenin, Danila (20 April 2022). "Changing std::sort at Google's Scale and Beyond". Experimental chill.
  5. ↑ Array.Sort Method (Array)
  6. ↑ Go 1.20.3 source code
  7. ↑ Java 14 source code
  8. ↑ Peters, Orson R. L. (2021). "orlp/pdqsort: Pattern-defeating quicksort". GitHub. arXiv: 2106.05123 .
  9. ↑ "slice.sort_unstable(&mut self)". Rust. The current algorithm is based on pattern-defeating quicksort by Orson Peters, which combines the fast average case of randomized quicksort with the fast worst case of heapsort, while achieving linear time on slices with certain patterns. It uses some randomization to avoid degenerate cases, but with a fixed seed to always provide deterministic behavior.
  10. ↑ Lammich, Peter (2020). Efficient Verified Implementation of Introsort and Pdqsort. IJCAR 2020: Automated Reasoning. Vol. 12167. pp. 307–323. doi: 10.1007/978-3-030-51054-1_18 .
  11. ↑ van den Hoven, Igor (2021). "fluxsort". GitHub.
  12. ↑ van den Hoven, Igor (2022). "crumsort". GitHub.
  13. ↑ Tiselice, Dragoș (2022). "crumsort-rs". GitHub.
  14. ↑ Peters, Orson (2023). "Glidesort: Efficient In-Memory Adaptive Stable Sorting on Modern Hardware".
  15. ↑ Bergdoll, Lukas (2024). "ipnsort: an efficient, generic and robust unstable sort implementation".
  16. ↑ Bergdoll, Lukas (2024). "driftsort: an efficient, generic and robust stable sort implementation".

General

This page is based on this Wikipedia article
Text is available under the CC BY-SA 4.0 license; additional terms may apply.
Images, videos and audio are available under their respective licenses.