LeetCode #3371 — MEDIUM

Identify the Largest Outlier in an Array

Move from brute-force thinking to an efficient approach using array strategy.

The Problem

Problem Statement

You are given an integer array nums. This array contains n elements, where exactly n - 2 elements are special numbers. One of the remaining two elements is the sum of these special numbers, and the other is an outlier.

An outlier is defined as a number that is neither one of the original special numbers nor the element representing the sum of those numbers.

Note that special numbers, the sum element, and the outlier must have distinct indices, but may share the same value.

Return the largest potential outlier in nums.

Example 1:

Input: nums = [2,3,5,10]

Output: 10

Explanation:

The special numbers could be 2 and 3, thus making their sum 5 and the outlier 10.

Example 2:

Input: nums = [-2,-1,-3,-6,4]

Output: 4

Explanation:

The special numbers could be -2, -1, and -3, thus making their sum -6 and the outlier 4.

Example 3:

Input: nums = [1,1,1,1,1,5,5]

Output: 5

Explanation:

The special numbers could be 1, 1, 1, 1, and 1, thus making their sum 5 and the other 5 as the outlier.

Constraints:

3 <= nums.length <= 10⁵
-1000 <= nums[i] <= 1000
The input is generated such that at least one potential outlier exists in nums.

Step 01

Brute Force Baseline

Problem summary: You are given an integer array nums. This array contains n elements, where exactly n - 2 elements are special numbers. One of the remaining two elements is the sum of these special numbers, and the other is an outlier. An outlier is defined as a number that is neither one of the original special numbers nor the element representing the sum of those numbers. Note that special numbers, the sum element, and the outlier must have distinct indices, but may share the same value. Return the largest potential outlier in nums.

Baseline thinking

Start with the most direct exhaustive search. That gives a correctness anchor before optimizing.

Pattern signal: Array · Hash Map

Example 1

[2,3,5,10]

Example 2

[-2,-1,-3,-6,4]

Example 3

[1,1,1,1,1,5,5]

Step 02

Core Insight

What unlocks the optimal approach

What will be the value of array sum if we remove the outlier from it?
Use hashmap to find occurrence of an element quickly.

Interview move: turn each hint into an invariant you can check after every iteration/recursion step.

Step 03

Algorithm Walkthrough

Iteration Checklist

Define state (indices, window, stack, map, DP cell, or recursion frame).
Apply one transition step and update the invariant.
Record answer candidate when condition is met.
Continue until all input is consumed.

Use the first example testcase as your mental trace to verify each transition.

Step 04

Edge Cases

Minimum Input

Single element / shortest valid input

Validate boundary behavior before entering the main loop or recursion.

Duplicates & Repeats

Repeated values / repeated states

Decide whether duplicates should be merged, skipped, or counted explicitly.

Extreme Constraints

Upper-end input sizes

Re-check complexity target against constraints to avoid time-limit issues.

Invalid / Corner Shape

Empty collections, zeros, or disconnected structures

Handle special-case structure before the core algorithm path.

Step 05

Full Annotated Code

Source-backed implementations are provided below for direct study and interview prep.

// Accepted solution for LeetCode #3371: Identify the Largest Outlier in an Array
class Solution {
    public int getLargestOutlier(int[] nums) {
        int s = 0;
        Map<Integer, Integer> cnt = new HashMap<>();
        for (int x : nums) {
            s += x;
            cnt.merge(x, 1, Integer::sum);
        }
        int ans = Integer.MIN_VALUE;
        for (var e : cnt.entrySet()) {
            int x = e.getKey(), v = e.getValue();
            int t = s - x;
            if (t % 2 != 0 || !cnt.containsKey(t / 2)) {
                continue;
            }
            if (x != t / 2 || v > 1) {
                ans = Math.max(ans, x);
            }
        }
        return ans;
    }
}

// Accepted solution for LeetCode #3371: Identify the Largest Outlier in an Array
func getLargestOutlier(nums []int) int {
	s := 0
	cnt := map[int]int{}
	for _, x := range nums {
		s += x
		cnt[x]++
	}
	ans := math.MinInt32
	for x, v := range cnt {
		t := s - x
		if t%2 != 0 || cnt[t/2] == 0 {
			continue
		}
		if x != t/2 || v > 1 {
			ans = max(ans, x)
		}
	}
	return ans
}

# Accepted solution for LeetCode #3371: Identify the Largest Outlier in an Array
class Solution:
    def getLargestOutlier(self, nums: List[int]) -> int:
        s = sum(nums)
        cnt = Counter(nums)
        ans = -inf
        for x, v in cnt.items():
            t = s - x
            if t % 2 or cnt[t // 2] == 0:
                continue
            if x != t // 2 or v > 1:
                ans = max(ans, x)
        return ans

// Accepted solution for LeetCode #3371: Identify the Largest Outlier in an Array
use std::collections::HashMap;

impl Solution {
    pub fn get_largest_outlier(nums: Vec<i32>) -> i32 {
        let mut s = 0;
        let mut cnt = HashMap::new();
        for &x in &nums {
            s += x;
            *cnt.entry(x).or_insert(0) += 1;
        }

        let mut ans = i32::MIN;
        for (&x, &v) in &cnt {
            let t = s - x;
            if t % 2 != 0 {
                continue;
            }
            let y = t / 2;
            if let Some(&count_y) = cnt.get(&y) {
                if x != y || v > 1 {
                    ans = ans.max(x);
                }
            }
        }
        ans
    }
}

// Accepted solution for LeetCode #3371: Identify the Largest Outlier in an Array
function getLargestOutlier(nums: number[]): number {
    let s = 0;
    const cnt: Record<number, number> = {};
    for (const x of nums) {
        s += x;
        cnt[x] = (cnt[x] || 0) + 1;
    }
    let ans = -Infinity;
    for (const [x, v] of Object.entries(cnt)) {
        const t = s - +x;
        if (t % 2 || !cnt.hasOwnProperty((t / 2) | 0)) {
            continue;
        }
        if (+x != ((t / 2) | 0) || v > 1) {
            ans = Math.max(ans, +x);
        }
    }
    return ans;
}

Step 06

Interactive Study Demo

Use this to step through a reusable interview workflow for this problem.

Custom checkpoints (one per line)

Press Step or Run All to begin.

Step 07

Complexity Analysis

Time

O(n)

Space

O(n)

Approach Breakdown

BRUTE FORCE

O(n²) time

O(1) space

Two nested loops check every pair or subarray. The outer loop fixes a starting point, the inner loop extends or searches. For n elements this gives up to n²/2 operations. No extra space, but the quadratic time is prohibitive for large inputs.

OPTIMIZED

O(n) time

O(1) space

Most array problems have an O(n²) brute force (nested loops) and an O(n) optimal (single pass with clever state tracking). The key is identifying what information to maintain as you scan: a running max, a prefix sum, a hash map of seen values, or two pointers.

Shortcut: If you are using nested loops on an array, there is almost always an O(n) solution. Look for the right auxiliary state.

Coach Notes

Common Mistakes

Review these before coding to avoid predictable interview regressions.

Off-by-one on range boundaries

Wrong move: Loop endpoints miss first/last candidate.

Usually fails on: Fails on minimal arrays and exact-boundary answers.

Fix: Re-derive loops from inclusive/exclusive ranges before coding.

Mutating counts without cleanup

Wrong move: Zero-count keys stay in map and break distinct/count constraints.

Usually fails on: Window/map size checks are consistently off by one.

Fix: Delete keys when count reaches zero.