How do you solve Maximum Sum of Distinct Subarrays With Length K on LeetCode?

Maximum Sum of Distinct Subarrays With Length K (LeetCode #2461) 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 sliding window technique is useful for problems involving contiguous subarrays.

What is the time complexity of Maximum Sum of Distinct Subarrays With Length K?

The optimal solution for Maximum Sum of Distinct Subarrays With Length K runs in O(n) time and uses O(n) space. The time complexity is O(n) because we traverse the array once, and the space complexity is O(n) due to the hash map storing counts of elements.

What is the brute force solution for Maximum Sum of Distinct Subarrays With Length K?

The brute force approach for Maximum Sum of Distinct Subarrays With Length K 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 to see if all elements are distinct and then calculating their sums. 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 Distinct Subarrays With Length K?

Maximum Sum of Distinct Subarrays With Length K 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 Distinct Subarrays With Length K?

Always clarify the problem constraints and edge cases before diving into coding. Think about how you can optimize your solution after writing the brute force version. Practice explaining your thought process clearly, as communication is key in interviews.

What are common mistakes when solving Maximum Sum of Distinct Subarrays With Length K?

Not checking for distinct elements correctly, leading to incorrect sums. Failing to handle edge cases where k is equal to the length of the array.

#2461

Maximum Sum of Distinct Subarrays With Length K

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)

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Intuition

Time O(n)Space O(n)

Using a sliding window approach allows us to efficiently track the current subarray and its distinct elements, updating the sum as we slide the window across the array. This avoids the need to recheck all elements repeatedly.

⚙️

Algorithm

3 steps

1Step 1: Initialize a hash map to count occurrences of elements and a variable for the current sum.
2Step 2: Use a sliding window of size k to traverse the array, adding new elements and removing old ones as the window slides.
3Step 3: If the window contains all distinct elements, update the maximum sum.

solution.py17 lines

1def maxSumDistinctSubarray(nums, k):
2    count = {}
3    max_sum = 0
4    current_sum = 0
5    n = len(nums)
6    for i in range(n):
7        if i >= k:
8            left = nums[i - k]
9            count[left] -= 1
10            if count[left] == 0:
11                del count[left]
12                current_sum -= left
13        count[nums[i]] = count.get(nums[i], 0) + 1
14        current_sum += nums[i]
15        if len(count) == k:
16            max_sum = max(max_sum, current_sum)
17    return max_sum

ℹ

Complexity note: The time complexity is O(n) because we traverse the array once, and the space complexity is O(n) due to the hash map storing counts of elements.

1The sliding window technique is useful for problems involving contiguous subarrays.
2Using a hash map allows for efficient tracking of element counts and distinctness.

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