Q. Compare and contrast various sorting techniques or methods with respect to the memory space and the computing time.
Ans:
Insertion sort:- Because of the presence of nested loops, each of which can take n iterations, insertion sort is O(n2). This bound is very tight, because the input in reverse order can actually achieve this bound. So complexity is equal to= (n(n-1))/2 = O(n2). Shellsort: - The running time of Shellsort depends on the option of increment sequence. The worst-case running time of the Shellsort, using the Shell's increments, is (n2).
Heapsort:- The basic approach is to build a binary heap of the n elements. This stage takes the O(n) time. We then perform n delete_min operations on it. The elements leave the heap smallest first, in the sorted order. By recording these elements in the second array and then copying the array back again, we sort the n elements. Since each delete_min takes O(log n) time, the total running time becoms O(n log n).
Mergesort:- Mergesort is a the example of the techniques which is used to analyze recursive routines. We may assume that n is a power of 2, so that we always divide into even halves. For n = 1, the time to mergesort is constant, to which we will
The two recursive mergesorts of size n/2, in addition the time to merge, which is linear. The equations below say this exactly:
T(1) = 1
T(n) = 2T(n/2) + n
Quicksort:- Similar to mergesort, quicksort is recursive, and hence, its analysis needs solving a recurrence formula. We will do the analysis for a quicksort, assuming a random pivot (no median-of-three partitioning) and no cutoff for such small files. We will take T(0) = T(1) = 1, as in mergesort. The running time of quicksort is equal to the running time of the two recursive calls an addition to the linear time spent in the partition (the pivot selection takes some constant time). This gives the basic quicksort relation as follows
T(n) = T(i) + T(n - i - 1) + cn