Find the sum of maximum difference possible from all subset of a given array.
We are given an array arr[] of n non-negative integers (repeated elements allowed), find out the sum of maximum difference possible from all subsets of the given array.
Suppose max(s) represents the maximum value in any subset ‘s’ whereas min(s) represents the minimum value in the set ‘s’. We need to find the sum of max(s)-min(s) for all possible subsets.
Examples:
Input : arr[] = {1, 2, 3}
Output : result = 6
Explanation :
All possible subset and for each subset s,
max(s)-min(s) are as :
SUBSET | max(s) | min(s) | max(s)-min(s)
{1, 2} | 2 | 1 | 1
{1, 3} | 3 | 1 | 2
{2, 3} | 3 | 2 | 1
{1, 2, 3} | 3 | 1 | 2
Total Difference sum = 6
Note : max(s) - min(s) for all subset with
single element must be zero.
Input : arr[] = {1, 2, 2}
Output : result = 3
Explanation :
All possible subset and for each subset s,
max(s)-min(s) are as :
SUBSET | max(s) | min(s)| max(s)-min(s)
{1, 2} | 2 | 1 | 1
{1, 2} | 2 | 1 | 1
{2, 2} | 2 | 2 | 0
{1, 2, 2} | 2 | 1 | 1
Total Difference sum = 3
Basic Approach:
Compute the sum of the maximum element of each subset, and the sum of the minimum element of each subset separately, and then subtract the minimum sum from the maximum to get the answer. The sum of the maximum/ minimum element of each subset can be computed easily by iterating through the elements of each subset. But as we have to iterate through all subsets the time complexity for this approach is exponential O(n2^n).
Efficient Approach:
As we have to compute the sum of the maximum element of each subset, and the sum of the minimum element of each subset separately here is an efficient way to perform this calculation.
Sum of Minimum Elements of All Subsets:
Let us say that the elements of arr[] in non-decreasing order are {a1,a2,…, an}. Now, we can partition the subsets of arr[] into the following categories:
- Subsets containing element a1: These subsets can be obtained by taking any subset of {a2,a3,…, an} and then adding a1 into it. The number of such subsets will be 2n-1, and they all have a1 as their minimum element.
- Subsets not containing element a1, but containing a2: These subsets can be obtained by taking any subset of {a3, a4,…,an}, and then adding a2 into it. The number of such subsets will be 2n-2, and they all have a2 as their minimum element.
- …..
- Subsets not containing elements a1, a2,…, ai-1 but containing ai: These subsets can be obtained by taking any subset of {ai+1,ai+2,…, an}, and then adding ai into it. The number of such subsets will be 2n-i, and they all have ai as their minimum element.
it can be seen that the above iteration is complete, i.e., it considers each subset exactly once. Hence, the sum of the minimum element of all subsets will be:
min_sum = a1*2n-1 + a2*2n-2 + … + an*20
This sum can be computed easily in linear time with help of the Horner method…
Similarly, we can compute the sum of the maximum element of all subsets of arr[]. The only difference is that we need to iterate the elements of arr[] in non-increasing order.
Note: We may have a large answer, so we have to calculate the answer with mod 10^9 +7.
C++
Java
Python3
MOD = 1000000007 ;
def maxMin (arr,n):
arr.sort()
min_sum = 0
max_sum = 0
for i in range ( 0 ,n):
max_sum = 2 * max_sum + arr[n - 1 - i];
max_sum % = MOD;
min_sum = 2 * min_sum + arr[i];
min_sum % = MOD;
return (max_sum - min_sum + MOD) % MOD;
arr = [ 1 , 2 , 3 , 4 ]
n = len (arr)
print ( maxMin(arr, n))
|
C#
using System;
class GFG {
public static int MOD = 1000000007;
public static long maxMin ( int []arr, int n)
{
Array.Sort(arr);
long min_sum = 0, max_sum = 0;
for ( int i = 0; i < n; i++)
{
max_sum = 2 * max_sum + arr[n - 1 - i];
max_sum %= MOD;
min_sum = 2 * min_sum + arr[i];
min_sum %= MOD;
}
return (max_sum - min_sum + MOD) % MOD;
}
public static void Main()
{
int []arr = {1, 2, 3, 4};
int n = arr.Length;
Console.Write(maxMin(arr, n));
}
}
|
PHP
<?php
$MOD = 1000000007;
function maxMin ( $arr , $n )
{
global $MOD ;
sort( $arr );
$min_sum = 0; $max_sum = 0;
for ( $i = 0; $i < $n ; $i ++)
{
$max_sum = 2 * $max_sum +
$arr [ $n - 1 - $i ];
$max_sum %= $MOD ;
$min_sum = 2 * $min_sum + $arr [ $i ];
$min_sum %= $MOD ;
}
return ( $max_sum - $min_sum + $MOD ) % $MOD ;
}
$arr = array (1, 2, 3, 4);
$n = count ( $arr );
echo maxMin( $arr , $n );
?>
|
Javascript
<script>
var MOD = 1000000007;
function maxMin (arr, n)
{
arr.sort((a,b)=> a-b);
var min_sum = 0, max_sum = 0;
for ( var i = 0; i < n; i++)
{
max_sum = 2 * max_sum + arr[n-1-i];
max_sum %= MOD;
min_sum = 2 * min_sum + arr[i];
min_sum %= MOD;
}
return (max_sum - min_sum + MOD) % MOD;
}
var arr = [1, 2, 3, 4];
var n = arr.length;
document.write( maxMin(arr,n));
</script>
|
Last Updated :
19 Sep, 2023
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