Analysis of Algorithms

The worst case of QuickSort occurs when the picked pivot is always one of the corner elements in sorted array. In worst case, QuickSort recursively calls one subproblem with size 0 and other subproblem with size (n-1). So recurrence is T(n) = T(n-1) + T(0) + O(n) The above expression can be rewritten as T(n) = T(n-1) + O(n) void exchange(int *a, int *b) { int temp; temp = *a; *a = *b; *b = temp; } int partition(int arr[], int si, int ei) { int x = arr[ei]; int i = (si - 1); int j; for (j = si; j <= ei - 1; j++) { if(arr[j] <= x) { i++; exchange(&arr[i], &arr[j]); } } exchange (&arr[i + 1], &arr[ei]); return (i + 1); } /* Implementation of Quick Sort arr[] --> Array to be sorted si --> Starting index ei --> Ending index */ void quickSort(int arr[], int si, int ei) { int pi; /* Partitioning index */ if(si < ei) { pi = partition(arr, si, ei); quickSort(arr, si, pi - 1); quickSort(arr, pi + 1, ei); } } [/sourcecode]

If we use median as a pivot element, then the recurrence for all cases becomes T(n) = 2T(n/2) + O(n) The above recurrence can be solved using Master Method. It falls in case 2 of master method.

1) to sort the array firstly create a min-heap with first k+1 elements and a separate array as resultant array. 2) because elements are at most k distance apart from original position so, it is guranteed that the smallest element will be in this K+1 elements. 3) remove the smallest element from the min-heap(extract min) and put it in the result array. 4) Now,insert another element from the unsorted array into the mean-heap, now,the second smallest element will be in this, perform extract min and continue this process until no more elements are in the unsorted array.finally, use simple heap sort for the remaining elements Time Complexity ------------------------ 1) O(k) to build the initial min-heap 2) O((n-k)logk) for remaining elements... 3) 0(1) for extract min so overall O(k) + O((n-k)logk) + 0(1) = O(nlogk)

See http://www.geeksforgeeks.org/lower-bound-on-comparison-based-sorting-algorithms/ for point A. See http://www.geeksforgeeks.org/stability-in-sorting-algorithms/ for B. C is true, count sort is an Integer Sorting algorithm.

For a input integer n, the innermost statement of fun() is executed following times. n + n/2 + n/4 + ... 1 So time complexity T(n) can be written as T(n) = O(n + n/2 + n/4 + ... 1) = O(n) The value of count is also n + n/2 + n/4 + .. + 1

The time complexity can be calculated by counting number of times the expression "count = count + 1;" is executed. The expression is executed 0 + 1 + 2 + 3 + 4 + .... + (n-1) times. Time complexity = Theta(0 + 1 + 2 + 3 + .. + n-1) = Theta (n*(n-1)/2) = Theta(n²)

Following are the steps to follow to solve Tower of Hanoi problem recursively.

Let the three pegs be A, B and C. The goal is to move n pegs from A to C.
To move n discs from peg A to peg C:
    move n-1 discs from A to B. This leaves disc n alone on peg A
    move disc n from A to C
    move n?1 discs from B to C so they sit on disc n

The recurrence function T(n) for time complexity of the above recursive solution can be written as following. T(n) = 2T(n-1) + 1

The worst case time complexity is always greater than or same as the average case time complexity.

The order of growth of option c is n^2.5 which is higher than n².

  f1(n) = 2^n
  f2(n) = n^(3/2)
  f3(n) = nLogn
  f4(n) = n^(Logn)

Except f3, all other are exponential. So f3 is definitely first in output. Among remaining, n^(3/2) is next. One way to compare f1 and f4 is to take Log of both functions. Order of growth of Log(f1(n)) is Θ(n) and order of growth of Log(f4(n)) is Θ(Logn * Logn). Since Θ(n) has higher growth than Θ(Logn * Logn), f1(n) grows faster than f4(n). Following is another way to compare f1 and f4. Let us compare f4 and f1. Let us take few values to compare

n = 32, f1 = 2^32, f4 = 32^5 = 2^25
n = 64, f1 = 2^64, f4 = 64^6 = 2^36
...............
............... 

Also see http://www.wolframalpha.com/input/?i=2^n+vs+n^%28log+n%29 Thanks to fella26 for suggesting the above explanation.