Search Single Element in Sorted Array
Problem and examples
You are given a sorted array where every element appears exactly twice, except one element that appears only once. Your task is to find and return the single non-repeating element.
Note: The array is sorted in non-decreasing order. There will always be exactly one such unique element in the array.
If the array is empty, return null or an appropriate message indicating that the array is invalid.
| Input Array | Output | Description |
|---|---|---|
| [1, 1, 2, 2, 3, 4, 4] | 3 | 3 is the only number that appears once; others appear in pairs |
| [0, 1, 1, 2, 2, 3, 3] | 0 | 0 is the single element at the beginning |
| [1, 1, 2, 2, 3, 3, 4] | 4 | 4 is the single element at the end |
| [5] | 5 | Only one element in the array, it is the single element |
| [] | null | Array is empty, no element to return |
| [7, 7, 9, 9, 11, 11, 13, 15, 15] | 13 | 13 is the only number that doesn't repeat |
Explore Find Single Element in Sorted Array
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Understanding the Problem
We are given a sorted array where every element appears exactly twice, except for one element that appears only once. Our goal is to find that single, unique element.
Because the array is sorted and all duplicate elements are grouped together, we can make use of this structure to efficiently locate the single element using binary search, achieving O(log n) time complexity instead of scanning the entire array.
Step-by-Step Solution with Example
step 1: Visualize with an example
Let’s consider the array: [1, 1, 2, 2, 3, 4, 4, 5, 5]. Here, every element appears twice except for 3.
step 2: Understand pairing pattern
Normally, in a perfectly paired array like [a, a, b, b, c, c], the first of each pair is found at an even index, and its duplicate at the next (odd) index.
But once a single unique element is introduced, this pattern breaks at that point. After the unique element, all pairs shift: now the first of a pair appears at an odd index.
step 3: Use binary search
We apply binary search to find where this shift in pattern occurs. At each step:
- We compute the
midindex. - If
midis even andnums[mid] == nums[mid+1], it means the unique element is on the right side. - If
midis odd andnums[mid] == nums[mid-1], again, the unique is on the right. - Otherwise, the unique is on the left side (including mid).
step 4: Apply on the example
Let’s walk through the binary search on [1, 1, 2, 2, 3, 4, 4, 5, 5]:
- Start:
low = 0,high = 8 - Mid = (0+8)//2 = 4 →
nums[4] = 3 - Check neighbors:
nums[3] = 2andnums[5] = 4→ both are not equal to 3 - Hence, 3 is the unique element — we’ve found our answer!
step 5: Final implementation logic
def singleNonDuplicate(nums):
low, high = 0, len(nums) - 1
while low < high:
mid = (low + high) // 2
if mid % 2 == 1:
mid -= 1 # make mid even
if nums[mid] == nums[mid + 1]:
low = mid + 2
else:
high = mid
return nums[low]
Edge Cases
- Single element array: e.g.
[7]→ return 7 directly. - Single element at the start: e.g.
[2, 3, 3, 4, 4]→ 2 is unique, since it’s not equal to the next. - Single element at the end: e.g.
[1, 1, 2]→ 2 is unique, as it doesn’t match the previous. - Empty array: Invalid input, return
nullor raise an exception.
Finally
This solution is efficient and leverages the sorted structure and pairing pattern of the array. Understanding the even-odd index behavior before and after the unique element is key to narrowing the search.
Always validate the input and handle edge cases before proceeding with the binary search. If the array was unsorted or had more than one unique element, this approach wouldn’t work — a different method like XOR-based scanning would be needed.
- Initialize
start = 0andend = n - 2. - While
start ≤ end: - → Compute
mid = start + (end - start) / 2. - → Check if
midis even: - → If
arr[mid] == arr[mid + 1], the unique element is on the right →start = mid + 2. - → Else, the unique element is on the left →
end = mid. - → If
midis odd: - → If
arr[mid] == arr[mid - 1], the unique element is on the right →start = mid + 1. - → Else, the unique element is on the left →
end = mid - 1. - After the loop, the
startindex points to the single element.
Implementation and efficiency
C implementation
#include <stdio.h>
int singleNonDuplicate(int arr[], int size) {
int start = 0, end = size - 2;
while (start <= end) {
int mid = start + (end - start) / 2;
if ((mid % 2 == 0 && arr[mid] == arr[mid + 1]) ||
(mid % 2 == 1 && arr[mid] == arr[mid - 1])) {
start = mid + 1;
} else {
end = mid - 1;
}
}
return arr[start];
}
int main() {
int arr[] = {1, 1, 2, 2, 3, 4, 4};
int size = sizeof(arr) / sizeof(arr[0]);
printf("Single Element: %d\n", singleNonDuplicate(arr, size));
return 0;
}C++ implementation
#include <iostream>
using namespace std;
int singleNonDuplicate(int arr[], int size) {
int start = 0, end = size - 2;
while (start <= end) {
int mid = start + (end - start) / 2;
if ((mid % 2 == 0 && arr[mid] == arr[mid + 1]) ||
(mid % 2 == 1 && arr[mid] == arr[mid - 1])) {
start = mid + 1;
} else {
end = mid - 1;
}
}
return arr[start];
}
int main() {
int arr[] = {1, 1, 2, 2, 3, 4, 4};
int size = sizeof(arr)/sizeof(arr[0]);
cout << "Single Element: " << singleNonDuplicate(arr, size) << endl;
return 0;
}Python implementation
def single_non_duplicate(arr):
start, end = 0, len(arr) - 2
while start <= end:
mid = start + (end - start) // 2
if (mid % 2 == 0 and arr[mid] == arr[mid + 1]) or (mid % 2 == 1 and arr[mid] == arr[mid - 1]):
start = mid + 1
else:
end = mid - 1
return arr[start]
arr = [1, 1, 2, 2, 3, 4, 4]
print("Single Element:", single_non_duplicate(arr))Java implementation
public class SingleElementFinder {
public static int singleNonDuplicate(int[] arr) {
int start = 0, end = arr.length - 2;
while (start <= end) {
int mid = start + (end - start) / 2;
if ((mid % 2 == 0 && arr[mid] == arr[mid + 1]) ||
(mid % 2 == 1 && arr[mid] == arr[mid - 1])) {
start = mid + 1;
} else {
end = mid - 1;
}
}
return arr[start];
}
public static void main(String[] args) {
int[] arr = {1, 1, 2, 2, 3, 4, 4};
System.out.println("Single Element: " + singleNonDuplicate(arr));
}
}JavaScript implementation
function singleNonDuplicate(arr) {
let start = 0, end = arr.length - 2;
while (start <= end) {
let mid = Math.floor(start + (end - start) / 2);
if ((mid % 2 === 0 && arr[mid] === arr[mid + 1]) ||
(mid % 2 === 1 && arr[mid] === arr[mid - 1])) {
start = mid + 1;
} else {
end = mid - 1;
}
}
return arr[start];
}
const arr = [1, 1, 2, 2, 3, 4, 4];
console.log("Single Element:", singleNonDuplicate(arr));Go implementation
package main
import "fmt"
func singleNonDuplicate(arr []int) int {
start, end := 0, len(arr) - 2
for start <= end {
mid := start + (end - start) / 2
if (mid % 2 == 0 && arr[mid] == arr[mid + 1]) || (mid % 2 == 1 && arr[mid] == arr[mid - 1]) {
start = mid + 1
} else {
end = mid - 1
}
}
return arr[start]
}
func main() {
arr := []int{1, 1, 2, 2, 3, 4, 4}
fmt.Println("Single Element:", singleNonDuplicate(arr))
}Rust implementation
fn single_non_duplicate(arr: &[i32]) -> i32 {
let mut start = 0;
let mut end = arr.len() - 2;
while start <= end {
let mid = start + (end - start) / 2;
if (mid % 2 == 0 && arr[mid] == arr[mid + 1]) || (mid % 2 == 1 && arr[mid] == arr[mid - 1]) {
start = mid + 1;
} else {
end = mid - 1;
}
}
arr[start]
}
fn main() {
let arr = [1, 1, 2, 2, 3, 4, 4];
println!("Single Element: {}", single_non_duplicate(&arr));
}Kotlin implementation
fun singleNonDuplicate(arr: IntArray): Int {
var start = 0
var end = arr.size - 2
while (start <= end) {
val mid = start + (end - start) / 2
if ((mid % 2 == 0 && arr[mid] == arr[mid + 1]) || (mid % 2 == 1 && arr[mid] == arr[mid - 1])) {
start = mid + 1
} else {
end = mid - 1
}
}
return arr[start]
}
fun main() {
val arr = intArrayOf(1, 1, 2, 2, 3, 4, 4)
println("Single Element: ${singleNonDuplicate(arr)}")
}Swift implementation
func singleNonDuplicate(_ arr: [Int]) -> Int {
var start = 0
var end = arr.count - 2
while start <= end {
let mid = start + (end - start) / 2
if (mid % 2 == 0 && arr[mid] == arr[mid + 1]) || (mid % 2 == 1 && arr[mid] == arr[mid - 1]) {
start = mid + 1
} else {
end = mid - 1
}
}
return arr[start]
}
let arr = [1, 1, 2, 2, 3, 4, 4]
print("Single Element: \(singleNonDuplicate(arr))")TypeScript implementation
function singleNonDuplicate(arr: number[]): number {
let start = 0, end = arr.length - 2;
while (start <= end) {
let mid = Math.floor(start + (end - start) / 2);
if ((mid % 2 === 0 && arr[mid] === arr[mid + 1]) ||
(mid % 2 === 1 && arr[mid] === arr[mid - 1])) {
start = mid + 1;
} else {
end = mid - 1;
}
}
return arr[start];
}
const arr: number[] = [1, 1, 2, 2, 3, 4, 4];
console.log("Single Element:", singleNonDuplicate(arr));Time complexity
| Case | Time | Explanation |
|---|---|---|
| best case | O(1) | If the unique element is found at the first mid index. |
| average case | O(log n) | Binary search reduces the search space by half in every step. |
| worst case | O(log n) | Even in the worst case, binary search checks only log(n) elements. |
Space complexity
O(1)
Only a few variables (start, end, mid) are used regardless of input size.