Search results
Results from the WOW.Com Content Network
The worstsort algorithm is based on a bad sorting algorithm, badsort. The badsort algorithm accepts two parameters: L , which is the list to be sorted, and k , which is a recursion depth. At recursion level k = 0 , badsort merely uses a common sorting algorithm, such as bubblesort , to sort its inputs and return the sorted list.
This popular sorting algorithm has an average-case performance of O(n log(n)), which contributes to making it a very fast algorithm in practice. But given a worst-case input, its performance degrades to O(n 2). Also, when implemented with the "shortest first" policy, the worst-case space complexity is instead bounded by O(log(n)).
Timsort has been Python's standard sorting algorithm since version 2.3 (since version 3.11 using the Powersort merge policy [5]), and is used to sort arrays of non-primitive type in Java SE 7, [6] on the Android platform, [7] in GNU Octave, [8] on V8, [9] Swift, [10] and inspired the sorting algorithm used in Rust.
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.
In computer science, smoothsort is a comparison-based sorting algorithm.A variant of heapsort, it was invented and published by Edsger Dijkstra in 1981. [1] Like heapsort, smoothsort is an in-place algorithm with an upper bound of O(n log n) operations (see big O notation), [2] but it is not a stable sort.
Proportion extend sort was published by Jing-Chao Chen in 2001 [2] as an improvement on his earlier proportion split sort design. [3] Its average-case performance, which was only experimentally measured in the original paper, was analyzed by Richard Cole and David C. Kandathil in 2004 [4] and by Chen in 2006, [5] and shown to require log 2 n + O(n) comparisons on average.
Merge sort's main advantages are that it is a stable sort and has excellent worst-case performance. The main disadvantage of merge sort is that it is an out-of-place algorithm, so when operating on arrays, efficient implementations require O ( n ) auxiliary space (vs. O (log n ) for quicksort with in-place partitioning and tail recursion, or O ...
The resulting algorithm is called partial quicksort and requires an expected time of only O(n + k log k), and is quite efficient in practice, especially if a selection sort is used as a base case when k becomes small relative to n. However, the worst-case time complexity is still very bad, in the case of a bad pivot selection.