How do you solve Maximum Unique Subarray Sum After Deletion on LeetCode?

Maximum Unique Subarray Sum After Deletion (LeetCode #3487) 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 set helps efficiently track unique elements.

What is the time complexity of Maximum Unique Subarray Sum After Deletion?

The optimal solution for Maximum Unique Subarray Sum After Deletion runs in O(n) time and uses O(n) space. The sliding window approach processes each element once, leading to O(n) complexity.

What is the brute force solution for Maximum Unique Subarray Sum After Deletion?

The brute force approach for Maximum Unique Subarray Sum After Deletion is Brute Force, with O(n²) time complexity and O(1) space complexity. Generate all possible subarrays and check for uniqueness. Calculate the sum for each unique subarray and track the maximum sum found. The optimal Optimal Solution approach improves this to O(n).

What algorithm or data structure is used to solve Maximum Unique Subarray Sum After Deletion?

Maximum Unique Subarray Sum After Deletion uses the following concepts: Array, Hash Table, Greedy. The recommended patterns to study are: Hash Map, Array.

What are the interview tips for Maximum Unique Subarray Sum After Deletion?

Explain your thought process clearly. Discuss edge cases, like all negative numbers. Practice coding on a whiteboard or online editor.

What are common mistakes when solving Maximum Unique Subarray Sum After Deletion?

Not handling negative numbers correctly. Forgetting to reset the left pointer when duplicates are found.

#3487

Maximum Unique Subarray Sum After Deletion

Easy

ArrayHash TableGreedyHash 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 approach with a hash set to track unique elements. Expand the window to include new elements and shrink it when duplicates are found, maximizing the sum.

⚙️

Algorithm

3 steps

1Step 1: Initialize a set to track unique elements and two pointers for the sliding window.
2Step 2: Iterate through the array, adding elements to the set and updating the current sum.
3Step 3: If a duplicate is found, move the left pointer to remove elements until the subarray is unique again.

solution.py14 lines

1def maxUniqueSubarray(nums):
2    seen = set()
3    left = 0
4    max_sum = 0
5    current_sum = 0
6    for right in range(len(nums)):
7        while nums[right] in seen:
8            seen.remove(nums[left])
9            current_sum -= nums[left]
10            left += 1
11        seen.add(nums[right])
12        current_sum += nums[right]
13        max_sum = max(max_sum, current_sum)
14    return max_sum

ℹ

Complexity note: The sliding window approach processes each element once, leading to O(n) complexity.

1Using a set helps efficiently track unique elements.
2Sliding window optimizes the search for unique subarrays.

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