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📜  通过删除两个最大的元素并用它们的绝对差代替它们的不相等来剩余最后一个元素

📅  最后修改于: 2021-04-29 03:02:49             🧑  作者: Mango

给定一个由N个元素组成的数组arr [] ,任务是执行以下操作:

  • 从数组中选择两个最大的元素,然后删除这些元素。如果元素不相等,则将元素的绝对差值插入数组。
  • 执行上述操作,直到l数组中包含1个元素或其中没有元素为止。如果数组仅剩一个元素,则打印该元素,否则打印“ -1”。

例子:

方法:为了解决上述问题,我们将使用优先级队列数据结构。步骤如下:

  1. 将所有数组元素插入优先级队列。
  2. 由于优先级队列是基于Max-Heap的实现的。因此,从中删除元素会得到最大的元素。
  3. 直到优先级队列的大小不小于2,从其中删除两个元素(例如X&Y ),然后执行以下操作:
    • 如果X和Y都不一样然后将X和Y的绝对值为优先级队列。
    • 否则返回步骤3。
  4. 如果堆只有一个元素,则打印该元素。
  5. 否则打印“ -1”。

下面是上述方法的实现:

C++
// C++ program for the above approach
#include 
using namespace std;
 
// Function to print the remaining element
int final_element(int arr[], int n)
{
 
    // Priority queue can be used
    // to construct max-heap
    priority_queue heap;
 
    // Insert all element of arr[]
    // into priority queue
    for (int i = 0; i < n; i++)
        heap.push(arr[i]);
 
    // Perform operation until heap
    // size becomes 0 or 1
    while (heap.size() > 1) {
 
        // Remove largest element
        int X = heap.top();
        heap.pop();
 
        // Remove 2nd largest element
        int Y = heap.top();
        heap.pop();
 
        // If extracted element
        // are not equal
        if (X != Y) {
 
            // Find X - Y and push
            // it to heap
            int diff = abs(X - Y);
            heap.push(diff);
        }
    }
 
    // If heap size is 1, then
    // print the remaining element
    if (heap.size() == 1) {
 
        cout << heap.top();
    }
 
    // Else print "-1"
    else {
        cout << "-1";
    }
}
 
// Driver Code
int main()
{
    // Given array arr[]
    int arr[] = { 3, 5, 2, 7 };
 
    // Size of array arr[]
    int n = sizeof(arr) / sizeof(arr[0]);
 
    // Function Call
    final_element(arr, n);
    return 0;
}


Java
// Java program for the above approach
import java.io.*;
import java.util.Collections;
import java.util.*;
 
class GFG{
     
// Function to print the remaining element    
static public int final_element(Integer[] arr, int n)
{
    if(arr == null)
    {
        return 0;
    }
     
    // Priority queue can be used
    // to construct max-heap
    PriorityQueue heap = new
    PriorityQueue(Collections.reverseOrder());
     
    // Insert all element of arr[]
    // into priority queue
    for(int i = 0; i < n; i++)
    {
       heap.offer(arr[i]);
    }
     
    // Perform operation until heap
    // size becomes 0 or 1
    while (heap.size() > 1)
    {
         
        // Remove largest element
        int X = heap.poll();
         
        // Remove 2nd largest element
        int Y = heap.poll();
         
        // If extracted element
        // are not equal
        if (X != Y)
        {
            // Find X - Y and push
            // it to heap
            int diff = Math.abs(X - Y);
            heap.offer(diff);
        }
    }
     
    // If heap size is 1, then
    // print the remaining element
    // Else print "-1"
    return heap.size() == 1 ? heap.poll() : -1;
}
 
// Driver code
public static void main (String[] args)
{
    // Given array arr[]
    Integer arr[] = new Integer[] { 3, 5, 2, 7};
     
    // Size of array arr[]
    int n = arr.length;
     
    // Function Call
    System.out.println(final_element(arr, n)); 
}
}
 
// This code is contributed by deepika_sharma


Python3
# Python3 program for the above approach
from queue import PriorityQueue
 
# Function to print the remaining element
def final_element(arr, n):
     
    # Priority queue can be used
    # to construct max-heap
    heap = PriorityQueue()
     
    # Insert all element of
    # arr[] into priority queue.
    # Default priority queue in Python
    # is min-heap so use -1*arr[i]
    for i in range(n):
        heap.put(-1 * arr[i])
     
    # Perform operation until heap
    # size becomes 0 or 1
    while (heap.qsize() > 1):
 
        # Remove largest element
        X = -1 * heap.get()
 
        # Remove 2nd largest element
        Y = -1 * heap.get()
 
        # If extracted elements
        # are not equal
        if (X != Y):
 
            # Find X - Y and push
            # it to heap
            diff = abs(X - Y)
            heap.put(-1 * diff)
 
    # If heap size is 1, then
    # print the remaining element
    if (heap.qsize() == 1):
        print(-1 * heap.get())
 
    # Else print "-1"
    else:
        print("-1")
 
# Driver Code
if __name__ == '__main__':
     
    # Given array arr[]
    arr = [ 3, 5, 2, 7 ]
 
    # Size of array arr[]
    n = len(arr)
 
    # Function call
    final_element(arr, n)
 
# This code is contributed by himanshu77


C#
// C# program for the above approach
using System;
using System.Collections.Generic;
 
class GFG{
     
// Function to print the remaining element
static void final_element(int[] arr, int n)
{
     
    // Priority queue can be used
    // to construct max-heap
    List heap = new List();
   
    // Insert all element of arr[]
    // into priority queue
    for(int i = 0; i < n; i++)
        heap.Add(arr[i]);
   
    // Perform operation until heap
    // size becomes 0 or 1
    while (heap.Count > 1)
    {
         
        // Remove largest element
        heap.Sort();
        heap.Reverse();
        int X = heap[0];
        heap.RemoveAt(0);
   
        // Remove 2nd largest element
        int Y = heap[0];
        heap.RemoveAt(0);
   
        // If extracted element
        // are not equal
        if (X != Y)
        {
             
            // Find X - Y and push
            // it to heap
            int diff = Math.Abs(X - Y);
            heap.Add(diff);
        }
    }
   
    // If heap size is 1, then
    // print the remaining element
    if (heap.Count == 1)
    {
        heap.Sort();
        heap.Reverse();
        Console.Write(heap[0]);
    }
   
    // Else print "-1"
    else
    {
        Console.Write(-1);
    }
}
 
// Driver code
static void Main()
{
     
    // Given array arr[]
    int[] arr = { 3, 5, 2, 7 };
     
    // Size of array arr[]
    int n = arr.Length;
     
    // Function Call
    final_element(arr, n);
}
}
 
// This code is contributed by divyeshrabadiya07


输出:
1

时间复杂度: O(N * log(N))
辅助空间复杂度: O(N)