Slow Sort Algorithm
The Slow Sort Algorithm is an intentionally inefficient sorting algorithm based on the concept of multiply and surrender, a tongue-in-cheek term referring to its recursive partitioning of the input data set into smaller pieces and then repeatedly surrendering and reconstructing the problem in a more complex manner. This algorithm was designed as a joke by Andrei Broder and Jorge Stolfi in their 1986 paper "Pessimal Algorithms and Simplexity Analysis," which aimed to investigate the "simplexity" of algorithms, i.e., the worst-case complexity of a given algorithm. Slow Sort is derived from a combination of two other, more efficient algorithms, namely Merge Sort and Insertion Sort, but with a twist that results in an incredibly inefficient sorting process.
The basic idea behind Slow Sort is to recursively divide the input array into two halves, similar to Merge Sort, and then find the maximum and minimum elements within each half. The maximum and minimum elements are then swapped, effectively placing the minimum element in its correct position and the maximum element in its correct position. The process is then repeated for the remaining unsorted elements, as in Insertion Sort, until the entire array is sorted. However, unlike Merge Sort and Insertion Sort, which have average and worst-case time complexities of O(n log n) and O(n^2), respectively, Slow Sort has a much higher time complexity, with a lower bound of O(n^(log n)). Due to its intentional inefficiency, Slow Sort is rarely, if ever, used in practical applications, serving primarily as a humorous example of how not to approach algorithm design.
//Returns the sorted vector after performing SlowSort
//It is a sorting algorithm that is of humorous nature and not useful.
//It's based on the principle of multiply and surrender, a tongue-in-cheek joke of divide and conquer.
//It was published in 1986 by Andrei Broder and Jorge Stolfi in their paper Pessimal Algorithms and Simplexity Analysis.
//This algorithm multiplies a single problem into multiple subproblems
//It is interesting because it is provably the least efficient sorting algorithm that can be built asymptotically,
//and with the restriction that such an algorithm, while being slow, must still all the time be working towards a result.
#include <iostream>
using namespace std;
void SlowSort(int a[], int i, int j)
{
if (i >= j)
return;
int m = i + (j - i) / 2; //midpoint, implemented this way to avoid overflow
int temp;
SlowSort(a, i, m);
SlowSort(a, m + 1, j);
if (a[j] < a[m])
{
temp = a[j]; //swapping a[j] & a[m]
a[j] = a[m];
a[m] = temp;
}
SlowSort(a, i, j - 1);
}
//Sample Main function
int main()
{
int size;
cout << "\nEnter the number of elements : ";
cin >> size;
int arr[size];
cout << "\nEnter the unsorted elements : ";
for (int i = 0; i < size; ++i)
{
cout << "\n";
cin >> arr[i];
}
SlowSort(arr, 0, size);
cout << "Sorted array\n";
for (int i = 0; i < size; ++i)
{
cout << arr[i] << " ";
}
return 0;
}
