Given an array arr[], the task is to find the number of rotations required to convert the given array to sorted form.
Examples:
Input: arr[] = {4, 5, 1, 2, 3}
Output: 2
Explanation:
Sorted array {1, 2, 3, 4, 5} after 2 anti-clockwise rotations.Input: arr[] = {2, 1, 2, 2, 2}
Output: 1
Explanation:
Sorted array {1, 2, 2, 2, 2} after 1 anti-clockwise rotations.
Naive Approach:
To solve the problem mentioned above the first observation is if we have n elements in the array then after sorting, the largest element is at (n – 1)th position. After k number of anti-clockwise rotations, the largest element will be at index (k – 1) (kth element from start). Another thing to note here is that, after rotation, the next element of the largest element will always be the smallest element, (unless the largest element is at last index, possible if there was no rotation).
Hence,
Number of rotations (k) = index of smallest element (k) in the array
Below is the implementation of the above approach:
Python3
# Python3 program to find the # count of rotations # Function to return the count # of rotations def countRotation(arr, n): for i in range (1, n): # Find the smallest element if (arr[i] < arr[i - 1]): # Return its index return i # If array is not # rotated at all return 0 # Driver Code if __name__ == "__main__": arr1 = [ 4, 5, 1, 2, 3 ] n = len(arr1) print(countRotation(arr1, n)) # This code is contributed by chitranayal |
2
Time Complexity: O(N)
Auxiliary Space: O(1)
Efficient Approach:
To optimize the above approach, we will use Binary Search. We can notice that, after being sorted and rotated, the given array is divided into two halves with non-decreasing elements, which is the only pre-requisite for binary search. Perform a recursive binary search in the array to find the index of the smallest element.
Below is the implementation of the above approach:
Python3
# Python3 program to implement the # above approach # Function to return the # count of rotations def countRotation(arr, low, high): # If array is not rotated if (low > high): return 0 mid = low + (high - low) // 2 # Check if current element is # greater than the next # element if (mid < high and arr[mid] > arr[mid + 1]): # The next element is # the smallest return mid + 1 # Check if current element is # smaller than it's previous # element if (mid > low and arr[mid] < arr[mid - 1]): # Current element is # the smallest return mid # Check if current element is # greater than lower bound if (arr[mid] > arr[low]): # The sequence is increasing # so far # Search for smallest # element on the right # subarray return countRotation(arr, mid + 1, high) if (arr[mid] < arr[high]): # Smallest element lies on the # left subarray return countRotation(arr, low, mid - 1) else: # Search for the smallest # element on both subarrays rightIndex = countRotation(arr, mid + 1, high) leftIndex = countRotation(arr, low, mid - 1) if (rightIndex == 0): return leftIndex return rightIndex # Driver code if __name__ == '__main__': arr1 = [ 4, 5, 1, 2, 3 ] N = len(arr1) print(countRotation(arr1, 0, N - 1)) # This code is contributed by mohit kumar 29 |
2
Time Complexity: O(N)
The complexity will be O(logN) for an array without duplicates. But if the array contains duplicates, then it will recursively call the search for both halves. So the worst-case complexity will be O(N).
Auxiliary Space:O(N)
At worst case, the recursion call stack will have N/2 recursion calls at a time.
Please refer complete article on Count of rotations required to generate a sorted array for more details!
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