In this question, we will perform the given operation on the array elements and find the final maximum sum.
Here, in each operation, we can select at most X[p] elements from the array and replace them with Y[p] elements to maximize the sum.
In the simple approach, we will find X[p] array elements that are smaller than Y[p] elements and replace them with Y[p].
In an efficient approach, we will use a priority queue to get the maximum sum.
Problem Statement− We are given a nums[] array containing N numbers. At the same time, we are given X[] and Y[] arrays containing M integers. We need to perform the following operations on the nums[] array.
We need to perform M operations on each element of the X[] and Y[] elements. In each operation, we need to select the largest X[p] element from the array nums[] and replace it with Y[p].
The given task is to find the maximum sum of the elements of the nums[] array after performing M operations.
enter
nums[] = {10, 8, 7, 60, 20, 18, 30, 60}; m = 3; x[] = {1, 2, 5}; y[] = {500, 10, 2};
Output
708
Explanation − Let’s perform each operation one by one.
In the first operation, we will replace 7 elements with 500. So, the array becomes {10, 8, 500, 60, 20, 18, 30, 60}.
In the second operation, we can replace up to 2 elements with 10, but we only have 1 element less than 10. So, we replace 8 with 10 and the array becomes {10, 10, 500, 60, 20, 18, 30, 60}.
In the third operation, we can replace up to 5 elements with 2, but there are no elements less than 2 in the array. Therefore, we will not replace any elements.
enter
nums[] = {30, 40, 50, 50, 60}; m = 3; x[] = {2, 3, 6}; y[] = {10, 8, 21};
Output
230
Explanation − All elements of the y[] array are smaller than the elements of the original array. Therefore, we don't need to replace any element of the given array to get the maximum sum.
enter
nums[] = {30, 40, 50, 50, 60}; m = 3; x[] = {2, 4, 5}; y[] = {50, 60, 100};
Output
500
Explanation − Here, we can replace up to x[p] elements in each operation. In the last operation, we can replace each element in the array with 100, resulting in a maximum sum equal to 100.
In this method, we will iterate over the x[] and y[] arrays. In each iteration, we will sort the array to get at most x[p] array elements that are smaller than y[p] elements and replace them with y[p].
Step 1 − Initialize ‘maxSum’ to 0, which is used to store the maximum sum of array elements.
Step 2 − Start traversing the x[] and y[] array elements.
Step 3 − Store the value of x[p] in a temporary variable and sort the nums[] array.
Step 4− Start traversing the sorted array within the loop.
Step 5 − If the temperature is greater than 0 and nums[q] is less than y[p], update nums[q] with y[p] and decrement the temp value by 1.
Step 6− Outside the loop, start traversing the updated array, take out the sum of all array elements and store it in the maxSum variable.
Step 7 − Return maxSum at the end of the function.
#include <bits/stdc++.h> using namespace std; int getMaxSum(int nums[], int n, int q, int x[], int y[]) { int maxSum = 0; // Traverse X[] and Y[] array for (int p = 0; p < q; p++) { // Replacing x[p] number of elements of nums[] array with y[p] if they are lesser than y[p] int temp = x[p]; sort(nums, nums + n); for (int q = 0; q < n; q++) { if (temp > 0 && nums[q] < y[p]) { nums[q] = y[p]; temp--; } } } // Sum of the array for (int p = 0; p < n; p++) { maxSum += nums[p]; } return maxSum; } int main() { int nums[] = {10, 8, 7, 60, 20, 18, 30, 60}; int n = (sizeof nums) / (sizeof nums[0]); int m = 3; int x[] = {1, 2, 5}; int y[] = {500, 10, 2}; cout << "The maximum sum we can get by replacing the array values is " << getMaxSum(nums, n, m, x, y); return 0; }
The maximum sum we can get by replacing the array values is 708
Time complexity− O(M*NlogN), where O(M) is used to traverse all queries and O(NlogN) is used to sort the array.
Space complexity− For sorting an array, the space complexity is O(N).
In this approach, we will use a priority queue to store pairs of array elements and their occurrence times.
For example, we will push the {nums[p], 1} pair into the priority queue for each array element. At the same time, we push the pair {y[p], x[p]} into the priority queue. In a priority queue, pairs will be sorted based on the first element. Therefore, we can take the top N elements from the queue. Here, for the pair {y[p],x[p]}, we can take out the y[p] elements x[p] times, and we need to take out a total of N elements to maximize the sum.
Step 1 − Initialize the ‘maxSum’ with 0 and the priority queue to store the pair of elements and their number of occurrences.
Step 2− For all array elements, insert {nums[p], 1} pairs into the queue.
Step 3 − Then, insert the {y[p], x[p]} pair into the priority queue.
Step 4− Iterate until n is greater than 0.
Step 4.1 − Remove the first element from the priority queue.
Step 4.2 − Add first_ele * max(n, second_ele) to the sum. Here, we use max(n, second_ele) to handle the last case.
Step 4.3 − Subtract second_ele from n.
Step 5− Return maxSum.
#include <bits/stdc++.h> using namespace std; int getMaxSum(int nums[], int n, int m, int x[], int y[]) { int maxSum = 0, p; // To get maximum sum priority_queue<pair<int, int>> p_que; // Insert nums[] array pairs into the queue for (p = 0; p < n; p++) p_que.push({nums[p], 1}); // Push replacement pairs for (p = 0; p < m; p++) p_que.push({y[p], x[p]}); // Add the first N elements of the priority queue in the sum while (n > 0) { // Get top element of priority queue auto temp = p_que.top(); // Remove top element p_que.pop(); // Add value to the sum maxSum += temp.first * min(n, temp.second); // Change N n -= temp.second; } return maxSum; } int main() { int nums[] = {10, 8, 7, 60, 20, 18, 30, 60}; int n = (sizeof nums) / (sizeof nums[0]); int m = 3; int x[] = {1, 2, 5}; int y[] = {500, 10, 2}; cout << "The maximum sum we can get by replacing the array values is " << getMaxSum(nums, n, m, x, y); return 0; }
The maximum sum we can get by replacing the array values is 708
Time complexity - O(N*logN m*logm), where O(N) and O(m) are used to traverse the given array, and O(logN) are used to insert and delete elements in the queue.
Space complexity - O(N M) for storing pairs in the queue.
In the first method, we need to sort the array in each iteration to find the smallest x[p] elements. Use a priority queue to automatically sort elements as they are inserted or removed because it uses the heap data structure. Therefore, it improves the performance of your code.
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