Minimize cost to sort given array by sorting unsorted subarrays (original) (raw)

Last Updated : 13 Dec, 2022

Given an array arr[] of size N, the task is to minimize the cost to sort the array by sorting any unsorted subarray where the cost of the operation is the difference between the maximum and minimum element of that subarray. This operation can be performed infinite times including 0.

Examples:

Input: arr[] = {1, 7, 5, 2, 1, 8}
Output: 6
Explanation: The subarray from index [1,4] can be chosen and can be sorted with cost = 7 - 1 = 6

Input: arr[] = { 1, 4, 3, 5, 6 ,13, 10}
Output: 4
Explanation: The subarray from index [1,2] and [5,6] can be sorted with cost of 4 - 3 and 13 - 10 = 1 + 3 = 4

Approach: This can be solved using the greedy approach, the elements already sorted don't require any cost so only the subarrays that have unsorted elements must be considered to sort. Multiset can be used to store the elements that are not in sorted order and the difference between the last and first element of multiset gives the cost.

Follow these steps to solve the above problem:

• Initialize a vector v and copy the arr into it and assign the size of the vector to the variable n.
• Now sort the vector v.
• Initialize 2 multisets m1 and m2 to store the unsorted and sorted subarray respectively and cost = 0 to store the result.
• Now iterate through the range [0,N) and check
• If v[i] is not equal to arr[i]
• Insert arr[i] into m1 and v[i] into m2
• When both the multisets are the same add the difference between the last and first element of the set to the cost.
• Clear both the multisets to make them used by the next unsorted subarray.
• Print the cost.

Below is the implementation of the above approach.

C++ `

// C++ program for the above approach #include <bits/stdc++.h> using namespace std;

// Function to find the minimum cost // to sort the array in ascending order int minimum_cost(vector arr) { // Copy the arr into vector and sort it vector sorted = arr; int n = arr.size(); sort(sorted.begin(), sorted.end());

// Initialize the two multisets to store
// sorted and the unordered elements
multiset<int> m1, m2;

// Initialize cost to store final answer
int cost = 0;

for (int i = 0; i < n; i++) {
    if (sorted[i] != arr[i]) {
        m1.insert(arr[i]);
        m2.insert(sorted[i]);

        // If both the multisets are equal,
        // the unordered subarray is sorted
        if (m1 == m2) {
            // The cost is difference
            // between mini and max
            cost += (*m1.rbegin() - 
                     *m2.begin());

            // Clear the multisets to make
            // use of it again
            m1.clear();
            m2.clear();
        }
    }
}
return cost;

}

// Driver code int main() { // Initialize the queries vector arr = { 1, 7, 5, 2, 1, 8 }; cout << minimum_cost(arr);

return 0;

}

Java

// Java code to implement the above approach import java.util.*;

class GFG {

// Function to find the minimum cost // to sort the array in ascending order public static int minimum_cost(int[] arr) {

// Copy the arr into vector and sort it
int n = arr.length;
int[] sorted = new int[n];
sorted = arr.clone();
Arrays.sort(sorted);

// Initialize the two multisets to store
// sorted and the unordered elements
SortedSet<Integer> m1 = new TreeSet<Integer>();
SortedSet<Integer> m2 = new TreeSet<Integer>();

// Initialize cost to store final answer
int cost = 0;

for (int i = 0; i < n; i++) {
  if (sorted[i] != arr[i]) {
    m1.add(arr[i]);
    m2.add(sorted[i]);

    // If both the multisets are equal,
    // the unordered subarray is sorted
    if (m1.equals(m2))
    {

      // The cost is difference
      // between mini and max
      cost += (Collections.max(m1) - Collections.min(m2));

      // Clear the multisets to make
      // use of it again
      m1.clear();
      m2.clear();
    }
  }
}
return cost;

}

// Driver code public static void main (String[] args) {

// Initialize the queries
int[] arr = { 1, 7, 5, 2, 1, 8 };
System.out.println(minimum_cost(arr));

} }

// This code is contributed by Shubham Singh

Python3

python3 program for the above approach

Function to find the minimum cost

to sort the array in ascending order

def minimum_cost(arr):

# Copy the arr into vector and sort it
sorted = arr.copy()
n = len(arr)
sorted.sort()

# Initialize the two multisets to store
# sorted and the unordered elements
m1 = set()
m2 = set()

# Initialize cost to store final answer
cost = 0

for i in range(0, n):
    if (sorted[i] != arr[i]):
        m1.add(arr[i])
        m2.add(sorted[i])

        # If both the multisets are equal,
        # the unordered subarray is sorted
        if (m1 == m2):
            # The cost is difference
            # between mini and max
            cost += (list(m1)[len(list(m1)) - 1] -
                     list(m2)[0])

            # Clear the multisets to make
            # use of it again
            m1.clear()
            m2.clear()

return cost

Driver code

if name == "main":

# Initialize the queries
arr = [1, 7, 5, 2, 1, 8]
print(minimum_cost(arr))

# This code is contributed by rakeshsahni

C#

// C# code to implement the above approach using System; using System.Collections.Generic;

public class GFG{

// Function to find the minimum cost // to sort the array in ascending order public static int minimum_cost(int[] arr) {

// Copy the arr into vector and sort it
int n = arr.Length;
int[] sorted = new int[n];
Array.Copy(arr, 0, sorted, 0, n);
Array.Sort(sorted);

// Initialize the two multisets to store
// sorted and the unordered elements
SortedSet<int> m1 = new SortedSet<int>();
SortedSet<int> m2 = new SortedSet<int>();

// Initialize cost to store final answer
int cost = 0;

for (int i = 0; i < n; i++) {
  if (sorted[i] != arr[i]) {
    m1.Add(arr[i]);
    m2.Add(sorted[i]);

    // If both the multisets are equal,
    // the unordered subarray is sorted
    if (m1.SetEquals(m2))
    {

      // The cost is difference
      // between mini and max
      cost += (m1.Max - m2.Min);

      // Clear the multisets to make
      // use of it again
      m1.Clear();
      m2.Clear();
    }
  }
}
return cost;

}

// Driver code public static void Main() { // Initialize the queries int[] arr = { 1, 7, 5, 2, 1, 8 }; Console.Write(minimum_cost(arr));

} }

// This code is contributed by Shubham Singh

JavaScript

`

Time Complexity: O(N * logN )
Auxiliary Space: O(N)

Method 2:

Problem: Given an array A consisting of N integers, the task is to find the minimum cost to sort the array in increasing order and we can perform the following operation any number of times such that:

• Choose a continuous subarray A[L, R] and sort the array in increasing order, while keeping other elements unchanged. The cost to perform this operation is max(AL…R) − min(AL…R).

Examples:

Input: S = {10, 1, 4}
Output: 9
Explanation: We can apply the operation on A[1…3] which converts A into [1, 4, 10] which is sorted in increasing order. So, the total cost is 10 − 1 = 9. It can be shown that we can't sort A with less than 9 total cost.

Input: S = {3, 1, 3, 3, 4, 3}
Output: 3

Approach: The problem can be solved based on the following observation:

• The most basic solution would be a single operation where the whole array is covered. It’d cost max(A) − min(A) and sorts the array immediately. So we always have a solution to sort the array.
• Secondly, we can observe that if there are two operations A[l1…r1] and A[l2…r2], where intervals [l1, r1] and [l2, r2] even share an endpoint, applying one operation on range [min(l1, l2) ,max(r1, r2)] is sufficient to sort the both subarray, with lower cost. Hence, no position might be covered in more than one operation.
• Now, the task becomes to partition A into continuous partitions, where the operation would be applied on each partition once. The partitions would be chosen so as to maximize the number of partitions while ensuring that once all partitions are sorted, A becomes sorted.
• For example, for A = [1, 2, 5, 6, 4, 3, 7, 9, 8], we can partition it like {[1], [2], [5, 6, 4, 3], [7], [9, 8]} and apply operation on each partition, leading to {[1], [2], [3, 4, 5, 6], [7], [8, 9]} in cost 6 − 3 + 9 − 8 = 4.
• In order to find these partitions, we need to find for each x, whether the first x elements in A form the same multiset as the smallest x elements of A. In the above example, positions satisfying that condition are 1, 2, 6, 7, 9 each of which forms a partition ending at that position.

Below is the implementation of the above approach:

C++ `

// C++ code to implement the approach #include <bits/stdc++.h> using namespace std;

// Function to find minimum cost // to sort the array void minCost(int array[], int n) { int* max = new int[n]; int* min = new int[n]; min[n - 1] = array[n - 1]; int temp = array[n - 1]; max[0] = array[0];

for (int i = n - 2; i >= 0; i--) { if (array[i] < temp) { temp = array[i]; } min[i] = temp; } temp = array[0];

for (int i = 1; i < n; i++) { if (array[i] > temp) { temp = array[i]; } max[i] = temp; }

int ans = 0; int l = 0; int r = 0;

for (int i = 0; i < n - 1; i++) { if (max[i] <= min[i + 1]) { r = i; int p = max[r] - min[l]; ans += p;

  l = r + 1;
}

} ans += max[n - 1] - min[l];

cout << ans << endl; }

// Driver Code int main() { int N = 6; int A[] = { 1, 8, 5, 2, 1, 7 };

// Function Call minCost(A, N); }

// This code is contributed by garg28harsh.

Java

// Java code to implement the approach

import java.io.; import java.util.;

public class GFG {

// Function to find minimum cost
// to sort the array
public static void minCost(int array[], int n)
{
    int[] max = new int[n];
    int[] min = new int[n];
    min[n - 1] = array[n - 1];
    int temp = array[n - 1];
    max[0] = array[0];

    for (int i = n - 2; i >= 0; i--) {
        if (array[i] < temp) {
            temp = array[i];
        }
        min[i] = temp;
    }
    temp = array[0];

    for (int i = 1; i < n; i++) {
        if (array[i] > temp) {
            temp = array[i];
        }
        max[i] = temp;
    }

    int ans = 0;
    int l = 0;
    int r = 0;

    for (int i = 0; i < n - 1; i++) {
        if (max[i] <= min[i + 1]) {
            r = i;
            int p = max[r] - min[l];
            ans += p;

            l = r + 1;
        }
    }
    ans += max[n - 1] - min[l];

    System.out.println(ans);
}

// Driver Code
public static void main(String[] args)
{
    int[] A = { 1, 8, 5, 2, 1, 7 };
    int N = A.length;

    // Function Call
    minCost(A, N);
}

}

Python3

Python code to implement the approach

Function to find minimum cost

to sort the array

def minCost(array, n): max = [0]*n min = [0]*n min[n - 1] = array[n - 1] temp = array[n - 1] max[0] = array[0] for i in range(n-2, -1, -1): if (array[i] < temp): temp = array[i] min[i] = temp temp = array[0] for i in range(1, n): if (array[i] > temp): temp = array[i] max[i] = temp

ans = 0
l = 0
r = 0
for i in range(n-1):
    if (max[i] <= min[i + 1]):
        r = i
        p = max[r] - min[l]
        ans += p
        l = r + 1

ans += max[n - 1] - min[l]

print(ans)

Driver Code

A = [1, 8, 5, 2, 1, 7] N = len(A)

Function Call

minCost(A, N)

This code is contributed by vikkycirus.

C#

// C# code to implement the approach using System; using System.Collections.Generic;

public class GFG {

// Function to find minimum cost // to sort the array static void minCost(int[] array, int n) { int[] max = new int[n]; int[] min = new int[n]; min[n - 1] = array[n - 1]; int temp = array[n - 1]; max[0] = array[0];

for (int i = n - 2; i >= 0; i--) {
  if (array[i] < temp) {
    temp = array[i];
  }
  min[i] = temp;
}
temp = array[0];

for (int i = 1; i < n; i++) {
  if (array[i] > temp) {
    temp = array[i];
  }
  max[i] = temp;
}

int ans = 0;
int l = 0;
int r = 0;

for (int i = 0; i < n - 1; i++) {
  if (max[i] <= min[i + 1]) {
    r = i;
    int p = max[r] - min[l];
    ans += p;

    l = r + 1;
  }
}
ans += max[n - 1] - min[l];

Console.Write(ans);

}

// Driver code public static void Main(string[] args) { int N = 6; int[] A = { 1, 8, 5, 2, 1, 7 };

// Function Call
minCost(A, N);

} }

// This code is contributed by garg28harsh.

JavaScript

// Javascript code to implement the approach

// Function to find minimum cost // to sort the array function minCost(array, n) { let max = new Array(n); let min = new Array(n); min[n - 1] = array[n - 1]; let temp = array[n - 1]; max[0] = array[0];

for (let i = n - 2; i >= 0; i--) {
    if (array[i] < temp) {
        temp = array[i];
    }
    min[i] = temp;
}
temp = array[0];

for (let i = 1; i < n; i++) {
    if (array[i] > temp) {
        temp = array[i];
    }
    max[i] = temp;
}

let ans = 0;
let l = 0;
let r = 0;

for (let i = 0; i < n - 1; i++) {
    if (max[i] <= min[i + 1]) {
        r = i;
        let p = max[r] - min[l];
        ans += p;

        l = r + 1;
    }
}
ans += max[n - 1] - min[l];

console.log(ans);

}

// Driver Code let A = [1, 8, 5, 2, 1, 7]; let N = A.length;

// Function Call minCost(A, N);

// This code is contributed by saurabh_jaiswal.

`

Time Complexity: O(N)
Auxiliary Space: O(N)