import java.util.*;
class Solution {
private static void merge(int[] arr, int low, int mid, int high) {
ArrayList<Integer> temp = new ArrayList<>();
int left = low;
int right = mid + 1;
while (left <= mid && right <= high) {
if (arr[left] <= arr[right]) {
temp.add(arr[left]);
left++;
} else {
temp.add(arr[right]);
right++;
}
}
while (left <= mid) {
temp.add(arr[left]);
left++;
}
while (right <= high) {
temp.add(arr[right]);
right++;
}
for (int i = low; i <= high; i++) {
arr[i] = temp.get(i - low);
}
}
public static void mergeSort(int[] arr, int low, int high) {
if (low >= high) return;
int mid = (low + high) / 2;
mergeSort(arr, low, mid);
mergeSort(arr, mid + 1, high);
merge(arr, low, mid, high);
}
}
public class tUf {
public static void main(String args[]) {
Scanner sc = new Scanner(System.in);
int n = 7;
int arr[] = { 9, 4, 7, 6, 3, 1, 5 };
System.out.println("Before sorting array: ");
for (int i = 0; i < n; i++) {
System.out.print(arr[i] + " ");
}
System.out.println();
Solution.mergeSort(arr, 0, n - 1);
System.out.println("After sorting array: ");
for (int i = 0; i < n; i++) {
System.out.print(arr[i] + " ");
}
System.out.println();
}
}
import matplotlib.pyplot as plt
# Example data points: input sizes and their corresponding execution times
input_sizes = [1000, 5000, 10000, 50000, 100000]
execution_times_quick = [1.8, 9.5, 21.3, 110.7, 250.3] # Replace these with actual results
plt.plot(input_sizes, execution_times_quick, marker='o', linestyle='-', color='r', label='Quick Sort')
plt.title('Quick Sort Time Complexity')
plt.xlabel('Input Size (n)')
plt.ylabel('Execution Time (ms)')
plt.grid(True)
plt.legend()
plt.show()
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