How do you solve Find the Largest Almost Missing Integer on LeetCode?

Find the Largest Almost Missing Integer (LeetCode #3471) 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: Use a sliding window for efficiency.

What is the time complexity of Find the Largest Almost Missing Integer?

The optimal solution for Find the Largest Almost Missing Integer runs in O(n) time and uses O(n) space. The sliding window approach allows us to maintain counts efficiently, leading to linear time complexity.

What is the brute force solution for Find the Largest Almost Missing Integer?

The brute force approach for Find the Largest Almost Missing Integer is Brute Force, with O(n²) time complexity and O(1) space complexity. Check every possible subarray of size k and count occurrences of each integer. This method is straightforward but inefficient. The optimal Optimal Solution approach improves this to O(n).

What algorithm or data structure is used to solve Find the Largest Almost Missing Integer?

Find the Largest Almost Missing Integer uses the following concepts: Array, Hash Table. The recommended patterns to study are: Hash Map, Array.

What are the interview tips for Find the Largest Almost Missing Integer?

Explain your thought process clearly. Consider edge cases early in your solution. Practice coding under time constraints.

What are common mistakes when solving Find the Largest Almost Missing Integer?

Forgetting to update counts correctly while sliding the window. Not handling edge cases for k = 1 or k = n.

#3471

Find the Largest Almost Missing Integer

Easy

ArrayHash TableHash 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)

Use a sliding window to efficiently count occurrences of integers in subarrays of size k without re-evaluating the entire subarray each time.

⚙️

Algorithm

3 steps

1Step 1: Initialize a frequency map to count occurrences of integers in the first k elements.
2Step 2: Slide the window across the array, updating counts by removing the leftmost element and adding the new rightmost element.
3Step 3: Track integers that appear exactly once and return the largest.

solution.py12 lines

1def largest_almost_missing(nums, k):
2    from collections import defaultdict
3    count = defaultdict(int)
4    n = len(nums)
5    for i in range(k): count[nums[i]] += 1
6    result = -1
7    if count[nums[0]] == 1: result = nums[0]
8    for i in range(1, n - k + 1):
9        count[nums[i - 1]] -= 1
10        count[nums[i + k - 1]] += 1
11        if count[nums[i]] == 1: result = max(result, nums[i])
12    return result

ℹ

Complexity note: The sliding window approach allows us to maintain counts efficiently, leading to linear time complexity.

1Use a sliding window for efficiency.
2Track counts dynamically to avoid redundant calculations.

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