Find the XOR of the elements in the given range [L, R] with the value K for a given set of queries
Given an array arr[] and Q queries, the task is to find the resulting updated array after Q queries. There are two types of queries and the following operation is performed by them:
- Update(L, R, K): Perform XOR for each element within the range L to R with K.
- Display(): Display the current state of the given array.
Examples:
Input: arr[] = {1, 2, 3, 4, 5, 6}, Q = 2
Query 1: Update(1, 5, 3)
Query 2: Display()
Output: 2 1 0 7 6 6
Explanation:
The update query performs XOR of all elements in the range [1, 5].
After performing the update query, the above array is displayed for display query because:
1 ^ 3 = 2
2 ^ 3 = 1
3 ^ 3 = 0
4 ^ 3 = 7
5 ^ 3 = 6
Input: arr[] = {2, 4, 6, 8, 10}, Q = 3
Query 1: Update(1, 3, 2)
Query 2: Update(2, 4, 3)
Query 3: Display()
Output: 0 5 7 11 10
Explanation:
There are two update queries.
After performing the first update query, the given array is changed to {0, 6, 4, 8, 10}. This is because:
2 ^ 2 = 0
4 ^ 2 = 6
6 ^ 2 = 4
After obtaining this array, this array further gets changed for the second query as the {0, 5, 7, 11, 10}. This is because:
6 ^ 3 = 5
4 ^ 3 = 7
8 ^ 3 = 11
Naive Approach: The naive approach for this problem is for every update query, run a loop from L to R and perform the XOR operation with the given K with all the elements from arr[L] to arr[R]. In order to perform the display query, simply print the array arr[]. The time complexity for this approach is O(NQ) where N is the length of the array and Q is the number of queries.
Efficient Approach: The idea is to use a kadane’s algorithm for this problem.
- An empty array res[] is defined with the same length as the original array.
- For every update query {L, R, K}, the res[] array is modified as:
- XOR K to res[L]
- XOR K to res[R + 1]
- Whenever the user gives a display query:
- Initialize a counter variable ‘i’ to the index 1.
- Perform the operation:
res[i] = res[i] ^ res[i - 1]
- Initialize a counter variable ‘i’ to the index 0.
- For every index in the array’s the required answer is:
arr[i] ^ res[i]
Below is the implementation of the above approach:
C++
#include <bits/stdc++.h>
using namespace std;
void update( int res[], int L, int R, int K)
{
L -= 1;
R -= 1;
res[L] ^= K;
res[R + 1] ^= K;
}
void display( int arr[], int res[], int n)
{
for ( int i = 1; i < n; i++) {
res[i] = res[i] ^ res[i - 1];
}
for ( int i = 0; i < n; i++) {
cout << (arr[i] ^ res[i]) << " " ;
}
cout << endl;
}
int main()
{
int arr[] = { 2, 4, 6, 8, 10 };
int N = sizeof (arr) / sizeof (arr[0]);
int res[N];
memset (res, 0, sizeof (res));
int L = 1, R = 3, K = 2;
update(res, L, R, K);
L = 2;
R = 4;
K = 3;
update(res, L, R, K);
display(arr, res, N);
return 0;
}
|
Java
class GFG{
static void update( int res[], int L,
int R, int K)
{
L -= 1 ;
R -= 1 ;
res[L] ^= K;
res[R + 1 ] ^= K;
}
static void display( int arr[],
int res[], int n)
{
int i;
for (i = 1 ; i < n; i++)
{
res[i] = res[i] ^ res[i - 1 ];
}
for (i = 0 ; i < n; i++)
{
System.out.print((arr[i] ^ res[i]) + " " );
}
System.out.println();
}
public static void main(String []args)
{
int arr[] = { 2 , 4 , 6 , 8 , 10 };
int N = arr.length;
int res[] = new int [N];
int L = 1 , R = 3 , K = 2 ;
update(res, L, R, K);
L = 2 ;
R = 4 ;
K = 3 ;
update(res, L, R, K);
display(arr, res, N);
}
}
|
Python3
def update(res, L, R, K):
L = L - 1
R = R - 1
res[L] = res[L] ^ K
res[R + 1 ] = res[R + 1 ] ^ K
def display(arr, res, n):
for i in range ( 1 , n):
res[i] = res[i] ^ res[i - 1 ]
for i in range ( 0 , n):
print (arr[i] ^ res[i], end = " " )
arr = [ 2 , 4 , 6 , 8 , 10 ]
N = len (arr)
res = [ 0 ] * N
L = 1
R = 3
K = 2
update(res, L, R, K)
L = 2
R = 4
K = 3
update(res, L, R, K)
display(arr, res, N)
|
C#
using System;
class GFG{
static void update( int []res, int L,
int R, int K)
{
L -= 1;
R -= 1;
res[L] ^= K;
res[R + 1] ^= K;
}
static void display( int []arr,
int []res, int n)
{
int i;
for (i = 1; i < n; i++)
{
res[i] = res[i] ^ res[i - 1];
}
for (i = 0; i < n; i++)
{
Console.Write((arr[i] ^ res[i]) + " " );
}
Console.WriteLine();
}
public static void Main()
{
int []arr = { 2, 4, 6, 8, 10 };
int N = arr.Length;
int []res = new int [N];
int L = 1, R = 3, K = 2;
update(res, L, R, K);
L = 2;
R = 4;
K = 3;
update(res, L, R, K);
display(arr, res, N);
}
}
|
Javascript
<script>
function update(res, L, R, K)
{
L -= 1;
R -= 1;
res[L] ^= K;
res[R + 1] ^= K;
}
function display(arr, res, n)
{
let i;
for (i = 1; i < n; i++)
{
res[i] = res[i] ^ res[i - 1];
}
for (i = 0; i < n; i++)
{
document.write((arr[i] ^ res[i]) + " " );
}
document.write( "<br/>" );
}
let arr = [ 2, 4, 6, 8, 10 ];
let N = arr.length;
let res = Array.from({length: N}, (_, i) => 0);
let L = 1, R = 3, K = 2;
update(res, L, R, K);
L = 2;
R = 4;
K = 3;
update(res, L, R, K);
display(arr, res, N);
</script>
|
Time Complexity:
- The time complexity for the update is O(1).
- The time complexity for displaying the array is O(N).
Auxiliary Space: O(N)
Note: This approach works very well when the update queries are very high compared to the display queries.
Last Updated :
11 Nov, 2021
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