How do you solve Maximum Sum of Almost Unique Subarray on LeetCode?

Maximum Sum of Almost Unique Subarray (LeetCode #2841) 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: Using a sliding window technique allows for efficient sum and distinct element tracking.

What is the brute force solution for Maximum Sum of Almost Unique Subarray?

The brute force approach for Maximum Sum of Almost Unique Subarray is Brute Force, with O(n²) time complexity and O(1) space complexity. The brute force approach involves checking every possible subarray of length k and counting the distinct elements. This is straightforward but inefficient for larger arrays. The optimal Optimal Solution approach improves this to O(n).

What algorithm or data structure is used to solve Maximum Sum of Almost Unique Subarray?

Maximum Sum of Almost Unique Subarray uses the following concepts: Array, Hash Table, Sliding Window. The recommended patterns to study are: Hash Map, Array.

What are the interview tips for Maximum Sum of Almost Unique Subarray?

Clearly explain your thought process and the reasoning behind each step. Practice writing clean and efficient code, as clarity is important in interviews. Be prepared to discuss the trade-offs of different approaches and why you chose one over another.

What are common mistakes when solving Maximum Sum of Almost Unique Subarray?

Not properly managing the count of elements when sliding the window. Forgetting to check if the number of distinct elements meets the requirement before updating the maximum sum.

#2841

Maximum Sum of Almost Unique Subarray

Medium

ArrayHash TableSliding WindowHash 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)

Using a sliding window approach allows us to efficiently maintain the sum and count of distinct elements as we move through the array, avoiding the need to repeatedly calculate sums and distinct counts.

⚙️

Algorithm

3 steps

1Step 1: Initialize a sliding window of size k, using two pointers to track the start and end.
2Step 2: Use a HashMap to count occurrences of each element in the current window.
3Step 3: As you slide the window, update the sum and the count of distinct elements. If the count of distinct elements is at least m, compare and possibly update the maximum sum.

solution.py27 lines

1# Full working Python code
2
3def max_sum_almost_unique(nums, m, k):
4    from collections import defaultdict
5    max_sum = 0
6    current_sum = 0
7    count_map = defaultdict(int)
8    n = len(nums)
9
10    for i in range(n):
11        count_map[nums[i]] += 1
12        current_sum += nums[i]
13
14        if i >= k:
15            count_map[nums[i - k]] -= 1
16            current_sum -= nums[i - k]
17            if count_map[nums[i - k]] == 0:
18                del count_map[nums[i - k]]
19
20        if i >= k - 1:
21            if len(count_map) >= m:
22                max_sum = max(max_sum, current_sum)
23
24    return max_sum
25
26# Example usage
27print(max_sum_almost_unique([2, 6, 7, 3, 1, 7], 3, 4))

ℹ

Complexity note: The time complexity is O(n) because we traverse the array once while maintaining the sum and distinct counts dynamically. The space complexity is O(n) due to the HashMap storing counts of elements.

1Using a sliding window technique allows for efficient sum and distinct element tracking.
2HashMaps are useful for counting occurrences and managing distinct elements.

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