Largest Unique Number - Practice Coding Problems

Largest Unique Number - Problem

You're given an array of integers, and your mission is to find the largest number that appears exactly once. Think of it as finding the biggest "loner" in the crowd!

Here's what makes this interesting: a number might appear multiple times in the array, but you only care about those special numbers that appear exactly once. Among all these unique numbers, you want the largest one.

The Challenge:

If there are numbers that appear exactly once, return the largest of them
If every number appears more than once (no unique numbers), return -1

Example: In [5, 7, 3, 9, 4, 9, 8, 3, 1], the numbers 5, 7, 4, 8, 1 appear exactly once. The largest among these unique numbers is 8.

Input & Output

example_1.py — Python

$ Input: [2, 3, 5, 5]

› Output: 3

💡 Note: The unique numbers are 2 and 3. The largest unique number is 3.

example_2.py — Python

$ Input: [5, 7, 3, 9, 4, 9, 8, 3, 1]

› Output: 8

💡 Note: Numbers 5, 7, 4, 8, 1 appear exactly once. Among these, 8 is the largest.

example_3.py — Python

$ Input: [9, 9, 8, 8]

› Output: -1

💡 Note: All numbers appear more than once, so there are no unique numbers. Return -1.

Constraints

1 ≤ nums.length ≤ 1000
0 ≤ nums[i] ≤ 1000
Follow up: Can you solve this in O(n) time?

Visualization

Tap to expand

Understanding the Visualization

Scan the List

Go through each name and count how many times they appear

Track Solo Guests

Keep track of the highest ID among guests who appear exactly once

Handle Duplicates

When someone appears multiple times, they're no longer a solo guest

Return Result

The highest ID among solo guests is our answer

Key Takeaway

🎯 Key Insight: Use a hash map to count frequencies in O(n) time, while simultaneously tracking the maximum among unique elements. This avoids the need for nested loops and achieves optimal performance.

Asked in

a Amazon 25 G Google 20 ⊞ Microsoft 15 f Meta 10

The optimal solution uses a hash table to count frequencies in O(n) time. We can solve this in one pass by tracking the maximum unique number as we build the frequency map, making it very efficient for large datasets.

Common Approaches

Approach	Time	Space	Notes
✓ Brute Force (Nested Loops)	O(n²)	O(1)	For each number, scan the entire array to count its occurrences
One-Pass Hash Table (Optimal)	O(n)	O(n)	Track frequency and maximum unique element simultaneously in one pass
Two-Pass Hash Table	O(n)	O(n)	Count frequencies first, then find the largest number with frequency 1

Brute Force (Nested Loops) — Algorithm Steps

Initialize result as -1 (assuming no unique number exists)
For each element in the array, count its occurrences
If an element appears exactly once and is larger than current result, update result
Return the result

Visualization

Tap to expand

Step-by-Step Walkthrough

Check Element

Select current element to check

Count Occurrences

Scan entire array counting matches

Update Maximum

If count is 1 and value is larger, update result

Code -

solution.c — C

#include <stdio.h>
#include <stdlib.h>
#include <string.h>

int largestUniqueNumber(int* nums, int n) {
    int result = -1;
    
    for (int i = 0; i < n; i++) {
        int count = 0;
        // Count occurrences of nums[i]
        for (int j = 0; j < n; j++) {
            if (nums[j] == nums[i]) {
                count++;
            }
        }
        
        // If appears exactly once and is larger than current result
        if (count == 1 && nums[i] > result) {
            result = nums[i];
        }
    }
    
    return result;
}

int main() {
    char line[1000];
    fgets(line, sizeof(line), stdin);
    
    // Simple parsing for array format [1,2,3]
    int nums[100];
    int n = 0;
    char* token = strtok(line + 1, ",]");
    while (token != NULL) {
        nums[n++] = atoi(token);
        token = strtok(NULL, ",]");
    }
    
    printf("%d\n", largestUniqueNumber(nums, n));
    return 0;
}

Time & Space Complexity

Time Complexity

⏱️

O(n²)

For each of n elements, we scan through n elements to count occurrences

⚠ Quadratic Growth

Space Complexity

O(1)

Only using a few variables to track count and maximum

✓ Linear Space

28.3K Views

Medium Frequency

~15 min Avg. Time

856 Likes

Ln 1, Col 1

Smart Actions

💡 Explanation

AI Ready

💡 Suggestion Tab to accept Esc to dismiss

// Output will appear here after running code

Code Editor Closed

Click the red button to reopen

Input & Output

Constraints

Visualization

Related Problems

Common Approaches

Brute Force (Nested Loops) — Algorithm Steps

Visualization

Code -

Time & Space Complexity

Select Compiler