How do you solve Maximum Erasure Value on LeetCode?

Maximum Erasure Value (LeetCode #1695) 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 allows us to efficiently manage the uniqueness of elements in the current subarray.

What is the brute force solution for Maximum Erasure Value?

The brute force approach for Maximum Erasure Value is Brute Force, with O(n²) time complexity and O(1) space complexity. The brute-force approach involves checking every possible subarray and calculating the sum of its elements if it contains unique elements. This is simple but inefficient, especially for larger arrays. The optimal Optimal Solution approach improves this to O(n).

What algorithm or data structure is used to solve Maximum Erasure Value?

Maximum Erasure Value uses the following concepts: Array, Hash Table, Sliding Window. The recommended patterns to study are: Hash Map, Array, Sliding Window.

What are the interview tips for Maximum Erasure Value?

Always clarify whether the subarray must be contiguous and if duplicates are allowed. Discuss your thought process and approach before jumping into coding; this shows your problem-solving skills. Practice explaining your code as you write it, as this is often part of the interview process.

What are common mistakes when solving Maximum Erasure Value?

Failing to properly manage the left pointer when duplicates are found, leading to incorrect sums. Not using a set to track unique elements, which can complicate the logic.

#1695

Maximum Erasure Value

Medium

ArrayHash TableSliding WindowHash MapArraySliding Window

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)

The optimal solution uses the sliding window technique to efficiently find the maximum score of a subarray with unique elements. This reduces the need to check every possible subarray explicitly.

⚙️

Algorithm

4 steps

1Step 1: Initialize two pointers (left and right) and a set to track unique elements.
2Step 2: Expand the right pointer to include elements in the window until a duplicate is found.
3Step 3: If a duplicate is found, move the left pointer to shrink the window until all elements are unique again.
4Step 4: Keep track of the current sum and update the maximum score whenever the window is valid.

solution.py14 lines

1# Full working Python code
2
3def maximumErasureValue(nums):
4    left, max_score, current_sum = 0, 0, 0
5    unique_elements = set()
6    for right in range(len(nums)):
7        while nums[right] in unique_elements:
8            unique_elements.remove(nums[left])
9            current_sum -= nums[left]
10            left += 1
11        unique_elements.add(nums[right])
12        current_sum += nums[right]
13        max_score = max(max_score, current_sum)
14    return max_score

ℹ

Complexity note: The time complexity is O(n) because each element is processed at most twice (once by the right pointer and once by the left pointer). The space complexity is O(n) due to the set storing unique elements.

1Using a sliding window allows us to efficiently manage the uniqueness of elements in the current subarray.
2Tracking the sum dynamically as we expand and contract the window helps avoid recalculating sums repeatedly.

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