□ My LinkedIn Showcase page dedicated especially to Help New Data Science Learners. □ If you are interested to learn more about how to start your career in Data Science, then click here, □□ Check out our Corporate Training Media Kit. □ Data Structures are a specialized means of organizing and storing data in computers in such a way that we can perform operations on the stored data more efficiently. Lovina Anthony Ganesh Iyer Kiran Shelke Archana TS Nilesh Magar Gurunath Kadam Rohit Sutare Shweta Prakash Faizul Haque Chittaranjan Ghosh #learning #datastructures EduBridge Learning Pvt. Void quickSort(int arr, int low, int high) While there are worst-case scenarios where the time complexity can be O(n^2), the average performance of Quick Sort makes it a very efficient sorting algorithm. This means that the time required to sort the array grows logarithmically with the number of elements. Hence, the time complexity of Quick Sort is described as O(n log n) on average. This means the number of divisions required to sort the entire array is proportional to the logarithm of the number of elements. ✅ Number of divisions -> As the algorithm continues to recursively divide the sub-arrays, the number of elements in each sub-array decreases by half at each step. In essence, the algorithm divides the problem of sorting the original array into two smaller sub-problems and solves them independently. ✅ Recursive calls -> Quick Sort then recursively sorts each of these sub-arrays. One contains elements less than the pivot and the other contains elements greater than the pivot. This effectively divides the array into two sub-arrays. ✅ Dividing the array -> At each iteration of Quick Sort, the algorithm partitions the array around a chosen pivot element. This represents the number of times the algorithm needs to recursively divide the array in half to sort it. In the time complexity calculation for Quick Sort, "log n" refers to the logarithm of the number of elements in the array (n), typically with base 2 (log2 n).
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