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23. Unique Number II

The problem can be found at the following link: Question Linkarrow-up-right

Problem Description

Given an array arr[] of size 2*N + 2, where 2*N elements appear in pairs and two elements appear only once, your task is to find those two distinct unique numbers and return them in increasing order.

Examples

Example 1:

Input:

arr[] = [1, 2, 3, 2, 1, 4]

Output:

[3, 4]

Explanation:

3 and 4 occur exactly once in the array. All other elements appear in pairs.

Example 2:

Input:

arr[] = [2, 1, 3, 2]

Output:

[1, 3]

Explanation:

1 and 3 occur only once. 2 appears twice.

Example 3:

Input:

arr[] = [2, 1, 3, 3]

Output:

[1, 2]

Explanation:

1 and 2 occur once. 3 appears twice.

Constraints:

  • $(2 \leq \text{arr.size()} \leq 10^6)$

  • $(1 \leq \text{arr}[i] \leq 5 \times 10^6)$

  • arr.size() is even

My Approach

XOR Partition Method

This is the most efficient and clever approach using bit manipulation.

Algorithm Steps:

  1. XOR all elements → result is XOR of the two unique numbers: x = a ^ b.

  2. Find the rightmost set bit in x.

  3. Partition the array into two groups based on this bit.

  4. XOR each group → you get a and b separately.

  5. Return the numbers in increasing order.

Time and Auxiliary Space Complexity

  • Expected Time Complexity: O(n), as we iterate through the array a constant number of times.

  • Expected Auxiliary Space Complexity: O(1), as we only use a constant number of variables.

Code (C++)

chevron-right⚡ Alternative Approacheshashtag

📊 2️⃣ Hash Map Frequency Count

Algorithm Steps:

  1. Traverse the array and count frequencies using a hash map.

  2. Collect the two numbers that appear exactly once.

📝 Complexity Analysis:

  • Time Complexity: O(n log n) (due to final sorting)

  • Space Complexity: O(n)

✅ Why This Approach?

Simple and works with generalized inputs — even if frequencies are not exactly two.

📊 3️⃣ Sorting and Pair Skipping

Algorithm Steps:

  1. Sort the array.

  2. Compare elements in pairs. Push elements that do not match with their pair.

📝 Complexity Analysis:

  • Time Complexity: O(n log n)

  • Space Complexity: O(1) (excluding result storage)

✅ Why This Approach?

No extra data structures used beyond sorting. Best when space is limited.

🆚 Comparison of Approaches

Approach

⏱️ Time Complexity

🗂️ Space Complexity

Pros

⚠️ Cons

XOR Partition

🟢 O(n)

🟢 O(1)

Fastest, elegant, minimal space

Works only with exactly two unique elements

Hash Map Frequency

🟢 O(n)

🔴 O(n)

Simple, handles arbitrary frequencies

More memory used

Sorting + Pairing

🔴 O(n log n)

🟢 O(1)

No extra space, good for sorted data

Slower due to sorting

Best Choice?

Scenario

Recommended Approach

Exactly 2 unique elements, rest in pairs

🥇 XOR Partition

Frequencies may vary

🥈 Hash Map Frequency

Limited space, sorting is acceptable

🥉 Sorting + Pair Check

🔹 Overall Best: XOR Partition, optimal in both time and space. 🔹 Best for flexible scenarios: Hash Map.

Code (Java)

Code (Python)

Contribution and Support:

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