How do you solve Identify the Largest Outlier in an Array on LeetCode?

Identify the Largest Outlier in an Array (LeetCode #3371) can be solved using Brute Force or Optimal Solution. The optimal approach is Optimal Solution with O(n) time complexity and O(n) space complexity. Key insight: The sum of the special numbers and the outlier can help identify potential outliers.

What is the brute force solution for Identify the Largest Outlier in an Array?

The brute force approach for Identify the Largest Outlier in an Array is Brute Force, with O(n²) time complexity and O(1) space complexity. In this approach, we will check each element in the array to see if it can be an outlier. We will do this by calculating the sum of all other elements and checking if the current element matches that sum. The optimal Optimal Solution approach improves this to O(n).

What algorithm or data structure is used to solve Identify the Largest Outlier in an Array?

Identify the Largest Outlier in an Array uses the following concepts: Array, Hash Table, Counting, Enumeration. The recommended patterns to study are: Hash Map, Array.

What are the interview tips for Identify the Largest Outlier in an Array?

Always clarify the problem statement and constraints before diving into coding. Think about different approaches and their trade-offs in terms of time and space complexity. Practice explaining your thought process while coding, as communication is key in interviews.

What are common mistakes when solving Identify the Largest Outlier in an Array?

Not considering the edge cases where numbers may repeat. Failing to correctly identify the sum and its relationship to the outlier.

#3371

Identify the Largest Outlier in an Array

Medium

ArrayHash TableCountingEnumerationHash MapArray

LeetCode ↗

Approaches

Brute ForceOptimal

Complexity Comparison

	Brute Force	Optimal Solution★
Time	O(n²)	O(n)
Space	O(1)	O(n)

💡

Intuition

Time O(n)Space O(n)

Instead of checking each element against the sum of others, we can use a HashMap to count occurrences and find the outlier directly by leveraging the properties of sums.

⚙️

Algorithm

4 steps

1Step 1: Create a HashMap to count the occurrences of each number.
2Step 2: Calculate the total sum of the array.
3Step 3: For each unique number in the HashMap, check if it can be the sum of special numbers by subtracting it from the total sum. If the result exists in the HashMap, it means it can be a valid sum.
4Step 4: Track the maximum outlier found during this process.

solution.py10 lines

1def largest_outlier(nums):
2    from collections import Counter
3    count = Counter(nums)
4    total_sum = sum(nums)
5    max_outlier = float('-inf')
6    for num in count:
7        if total_sum - num in count:
8            continue
9        max_outlier = max(max_outlier, num)
10    return max_outlier

ℹ

Complexity note: This complexity is due to the single pass to count occurrences and another pass to check potential outliers, leading to linear time complexity.

1The sum of the special numbers and the outlier can help identify potential outliers.
2Using a HashMap allows for quick lookups and counting occurrences efficiently.

Solutions and explanations are original Tejav content. Problem titles © LeetCode — use the LeetCode button above for the full problem statement.