Data Modelling & AI Data Structure & Algorithm

Maximize value at Kth index to create N size array with adjacent difference 1 and sum less than M

24 June 2025

3

Given three numbers N, K, and M, the task is to find the maximum value that can be assigned to the Kth index if positive values are assigned to all N indices such that the total sum of values is less than M, and the difference between the values at adjacent positions is atmost 1.

Examples:

Input : N=3, M=10, K=2
Output: 4
Explanation: The optimal way to assign values is {3, 4, 3}. Total sum=3+4+3=10≤M.
Note: {3, 4, 2} is not a valid distribution as 4-2=2>1

Input: N=7, M=100, K=6
Output: 16

Approach : The following observations help in solving the problem:

If X is assigned at the Kth index, then, the optimal distribution is as follows:
1. If X is less than K-1, the optimal distribution on than left would be (X-1), (X-2), …(X-K+1), (1), (1)…
2. Otherwise, it would be (X-1), (X-2), …(X-K+1).
3. If X is less than N-K, the optimal distribution on than left would be (X-1), (X-2), …(X-N+K), 1, 1…
4. Otherwise, it would be (X-1), (X-2), …(X-N+K).
Using the AP series, the sum of (X-1)+(X-2)+(X-3)+…+(X-Y) is Y*(X-1+X-Y)/2 = Y*(2X-Y-1)/2

The maximum value of X can be calculated with the concept of Binary search. Follow the steps below to solve the problem:

Initialize a variable ans to -1, to store the answer.
Initialize two variables low to 0 and high to M, for the purpose of binary searching.
Loop while low is not greater than high and do the following:
1. Calculate mid as the mean of high and low.
2. Initialize a variable val to 0, to store current sum of distribution.
3. Initialize L(number of indices on the left of K) as K-1 and R(number of indices on the right of K) as N-K.
4. Add the value of mid to val.
5. Distribute numbers on the left and right of K as discussed above and add their values to val.
6. If val is less than M, update ans as the maximum of ans and mid. Update low as mid+1.
7. Otherwise, update high as mid-1.
Return ans.

Below is the implementation of the above approach:

C++

// C++ program for the above approach
#include <bits/stdc++.h>
using namespace std;
// Function to calculate maximum value that can be placed at
// the Kth index in a distribution in which difference of
// adjacent elements is less than 1 and total sum of
// distribution is M.
int calculateMax(int N, int M, int K)
{
    // variable to store final answer
    int ans = -1;
    // variables for binary search
    int low = 0, high = M;
    // Binary search
    while (low <= high) {
        // variable for binary search
        int mid = (low + high) / 2;
        // variable to store total sum of array
        int val = 0;
        // number of indices on the left excluding the Kth
        // index
        int L = K - 1;
        // number of indices on the left excluding the Kth
        // index
        int R = N - K;
        // add mid to final sum
        val += mid;
        // distribution on left side is possible
        if (mid >= L) {
            // sum of distribution on the left side
            val += (L) * (2 * mid - L - 1) / 2;
        }
        else {
            // sum of distribution on the left side with
            // (L-mid) 1s
            val += mid * (mid - 1) / 2 + (L - mid);
        }
        // distribution on right side is possible
        if (mid >= R) {
            // sum of distribution on the right side
            val += (R) * (2 * mid - R - 1) / 2;
        }
        else {
            // sum of distribution on the left side with
            // (R-mid) 1s
            val += mid * (mid - 1) / 2 + (R - mid);
        }
        // Distribution is valid
        if (val <= M) {
            ans = max(ans, mid);
            low = mid + 1;
        }
        else
            high = mid - 1;
    }
    // return answer
    return ans;
}
// Driver code
int main()
{
    // Input
    int N = 7, M = 100, K = 6;
 
    // Function call
    cout << calculateMax(N, M, K) << endl;
    return 0;
}

Java

// Java program for the above approach
import java.io.*;
 
class GFG 
{
 
  // Function to calculate maximum value that can be
  // placed at
  // the Kth index in a distribution in which difference
  // of adjacent elements is less than 1 and total sum of
  // distribution is M.
  public static int calculateMax(int N, int M, int K)
  {
 
    // variable to store final answer
    int ans = -1;
 
    // variables for binary search
    int low = 0, high = M;
 
    // Binary search
    while (low <= high)
    {
 
      // variable for binary search
      int mid = (low + high) / 2;
 
      // variable to store total sum of array
      int val = 0;
 
      // number of indices on the left excluding the
      // Kth index
      int L = K - 1;
 
      // number of indices on the left excluding the
      // Kth index
      int R = N - K;
 
      // add mid to final sum
      val += mid;
 
      // distribution on left side is possible
      if (mid >= L)
      {
 
        // sum of distribution on the left side
        val += (L) * (2 * mid - L - 1) / 2;
      }
      else
      {
 
        // sum of distribution on the left side with
        // (L-mid) 1s
        val += mid * (mid - 1) / 2 + (L - mid);
      }
 
      // distribution on right side is possible
      if (mid >= R)
      {
 
        // sum of distribution on the right side
        val += (R) * (2 * mid - R - 1) / 2;
      }
      else
      {
 
        // sum of distribution on the left side with
        // (R-mid) 1s
        val += mid * (mid - 1) / 2 + (R - mid);
      }
 
      // Distribution is valid
      if (val <= M) {
        ans = Math.max(ans, mid);
        low = mid + 1;
      }
      else
        high = mid - 1;
    }
 
    // return answer
    return ans;
  }
 
  // Driver code
  public static void main(String[] args)
  {
    // Input
    int N = 7, M = 100, K = 6;
 
    // Function call
    System.out.println(calculateMax(N, M, K));
  }
}
 
// This code is contributed by lokeshpotta20.

Python3

# Python3 program for the above approach
 
# Function to calculate maximum value 
# that can be placed at the Kth index 
# in a distribution in which difference 
# of adjacent elements is less than 1 
# and total sum of distribution is M.
def calculateMax(N, M, K):
     
    # Variable to store final answer
    ans = -1
     
    # Variables for binary search
    low = 0
    high = M
     
    # Binary search
    while (low <= high):
         
        # Variable for binary search
        mid = (low + high) / 2
         
        # Variable to store total sum of array
        val = 0
         
        # Number of indices on the left excluding 
        # the Kth index
        L = K - 1
         
        # Number of indices on the left excluding
        # the Kth index
        R = N - K
         
        # Add mid to final sum
        val += mid
         
        # Distribution on left side is possible
        if (mid >= L):
             
            # Sum of distribution on the left side
            val += (L) * (2 * mid - L - 1) / 2
         
        else:
             
            # Sum of distribution on the left side
            # with (L-mid) 1s
            val += mid * (mid - 1) / 2 + (L - mid)
         
        # Distribution on right side is possible
        if (mid >= R):
             
            # Sum of distribution on the right side
            val += (R) * (2 * mid - R - 1) / 2
         
        else:
             
            # Sum of distribution on the left side with
            # (R-mid) 1s
            val += mid * (mid - 1) / 2 + (R - mid)
         
        # Distribution is valid
        if (val <= M):
            ans = max(ans, mid)
            low = mid + 1
        else:
            high = mid - 1
     
    # Return answer
    return int(ans)
 
# Driver code
 
# Input
N = 7
M = 100
K = 6
 
# Function call
print(calculateMax(N, M, K));
 
# This code is contributed by sanjoy_62

C#

// C# program for the above approach
using System;
class GFG
{
 
  // Function to calculate maximum value that can be
  // placed at
  // the Kth index in a distribution in which difference
  // of adjacent elements is less than 1 and total sum of
  // distribution is M.
  public static int calculateMax(int N, int M, int K)
  {
 
    // variable to store final answer
    int ans = -1;
 
    // variables for binary search
    int low = 0, high = M;
 
    // Binary search
    while (low <= high)
    {
 
      // variable for binary search
      int mid = (low + high) / 2;
 
      // variable to store total sum of array
      int val = 0;
 
      // number of indices on the left excluding the
      // Kth index
      int L = K - 1;
 
      // number of indices on the left excluding the
      // Kth index
      int R = N - K;
 
      // add mid to final sum
      val += mid;
 
      // distribution on left side is possible
      if (mid >= L)
      {
 
        // sum of distribution on the left side
        val += (L) * (2 * mid - L - 1) / 2;
      }
      else
      {
 
        // sum of distribution on the left side with
        // (L-mid) 1s
        val += mid * (mid - 1) / 2 + (L - mid);
      }
 
      // distribution on right side is possible
      if (mid >= R)
      {
 
        // sum of distribution on the right side
        val += (R) * (2 * mid - R - 1) / 2;
      }
      else
      {
 
        // sum of distribution on the left side with
        // (R-mid) 1s
        val += mid * (mid - 1) / 2 + (R - mid);
      }
 
      // Distribution is valid
      if (val <= M) {
        ans = Math.Max(ans, mid);
        low = mid + 1;
      }
      else
        high = mid - 1;
    }
 
    // return answer
    return ans;
  }
 
 
// Driver code
static void Main()
{
   
    // Input
    int N = 7, M = 100, K = 6;
 
    // Function call
    Console.Write(calculateMax(N, M, K));
 
}
}
 
// This code is contributed by code_hunt.

Javascript

<script>
        // JavaScript program for the above approach
 
        // Function to calculate maximum value that can be placed at
        // the Kth index in a distribution in which difference of
        // adjacent elements is less than 1 and total sum of
        // distribution is M.
        function calculateMax(N, M, K)
        {
         
            // variable to store final answer
            var ans = -1;
             
            // variables for binary search
            var low = 0, high = M;
             
            // Binary search
            while (low <= high)
            {
             
                // variable for binary search
                var mid = parseInt((low + high) / 2);
                 
                // variable to store total sum of array
                var val = 0;
                 
                // number of indices on the left excluding the Kth
                // index
                var L = K - 1;
                 
                // number of indices on the left excluding the Kth
                // index
                var R = N - K;
                 
                // add mid to final sum
                val += mid;
                 
                // distribution on left side is possible
                if (mid >= L)
                {
                 
                    // sum of distribution on the left side
                    val += (L) * ((2 * mid - L - 1) / 2);
                }
                else
                {
                 
                    // sum of distribution on the left side with
                    // (L-mid) 1s
                    val += mid * parseInt((mid - 1) / 2) + (L - mid);
                }
                 
                // distribution on right side is possible
                if (mid >= R)
                {
                 
                    // sum of distribution on the right side
                    val += (R) * (2 * mid - R - 1) / 2;
                }
                else
                {
                 
                    // sum of distribution on the left side with
                    // (R-mid) 1s
                    val += mid * parseInt((mid - 1) / 2) + (R - mid);
                }
                 
                // Distribution is valid
                if (val <= M)
                {
                    ans = Math.max(ans, mid);
                    low = mid + 1;
                }
                else
                    high = mid - 1;
            }
             
            // return answer
            return ans;
        }
         
        // Driver code
 
        // Input
        let N = 7, M = 100, K = 6;
 
        // Function call
        document.write(calculateMax(N, M, K));
 
// This code is contributed by lokeshpotta20.
    </script>

Output

Time Complexity: O(LogM)
Auxiliary Space: O(1)

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Maximize value at Kth index to create N size array with adjacent difference 1 and sum less than M

C++

Java

Python3

C#

Javascript

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