Problem# You are given an integer n and an integer start.
Define an array nums where nums[i] = start + 2 * i (0-indexed ) and n == nums.length.
Return the bitwise XOR of all elements of nums.
Examples# Example 1# 1
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Input: n = 5 , start = 0
Output: 8
Explanation: Array nums is equal to [ 0 , 2 , 4 , 6 , 8 ] where ( 0 ^ 2 ^ 4 ^ 6 ^ 8 ) = 8.
Where "^" corresponds to bitwise XOR operator.
Example 2# 1
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Input: n = 4 , start = 3
Output: 8
Explanation: Array nums is equal to [ 3 , 5 , 7 , 9 ] where ( 3 ^ 5 ^ 7 ^ 9 ) = 8.
Constraints# 1 <= n <= 10000 <= start <= 1000n == nums.lengthSolution# Method 1 – Simple Iterative XOR# Intuition# The problem asks for the XOR of a sequence where each element is start + 2*i. XOR is associative and commutative, so we can compute the result by iterating through the sequence and applying XOR step by step.
Approach# Initialize ans = 0. For i from 0 to n-1:Compute val = start + 2*i. ans ^= val. Return ans. Code#
Cpp
Go
Java
Kotlin
Python
Rust
Typescript
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class Solution {
public :
int xorOperation(int n, int start) {
int ans = 0 ;
for (int i = 0 ; i < n; ++ i) {
ans ^= start + 2 * i;
}
return ans;
}
};
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func xorOperation (n int , start int ) int {
ans := 0
for i := 0 ; i < n ; i ++ {
ans ^= start + 2 * i
}
return ans
}
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class Solution {
public int xorOperation (int n, int start) {
int ans = 0;
for (int i = 0; i < n; i++ ) {
ans ^= start + 2 * i;
}
return ans;
}
}
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class Solution {
fun xorOperation (n: Int, start: Int): Int {
var ans = 0
for (i in 0 until n) {
ans = ans xor (start + 2 * i)
}
return ans
}
}
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class Solution :
def xorOperation (self, n: int, start: int) -> int:
ans = 0
for i in range(n):
ans ^= start + 2 * i
return ans
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impl Solution {
pub fn xor_operation (n: i32 , start: i32 ) -> i32 {
let mut ans = 0 ;
for i in 0 .. n {
ans ^= start + 2 * i;
}
ans
}
}
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class Solution {
xorOperation (n : number , start : number ): number {
let ans = 0 ;
for (let i = 0 ; i < n ; i ++ ) {
ans ^= start + 2 * i ;
}
return ans ;
}
}
Complexity# ⏰ Time complexity: O(n) — We compute n values and XOR them. 🧺 Space complexity: O(1) — Only a constant amount of extra space is used.