LeetCode #1695 — MEDIUM

Maximum Erasure Value

Move from brute-force thinking to an efficient approach using array strategy.

The Problem

Problem Statement

You are given an array of positive integers nums and want to erase a subarray containing unique elements. The score you get by erasing the subarray is equal to the sum of its elements.

Return the maximum score you can get by erasing exactly one subarray.

An array b is called to be a subarray of a if it forms a contiguous subsequence of a, that is, if it is equal to a[l],a[l+1],...,a[r] for some (l,r).

Example 1:

Input: nums = [4,2,4,5,6]
Output: 17
Explanation: The optimal subarray here is [2,4,5,6].

Example 2:

Input: nums = [5,2,1,2,5,2,1,2,5]
Output: 8
Explanation: The optimal subarray here is [5,2,1] or [1,2,5].

Constraints:

1 <= nums.length <= 10⁵
1 <= nums[i] <= 10⁴

Step 01

Brute Force Baseline

Problem summary: You are given an array of positive integers nums and want to erase a subarray containing unique elements. The score you get by erasing the subarray is equal to the sum of its elements. Return the maximum score you can get by erasing exactly one subarray. An array b is called to be a subarray of a if it forms a contiguous subsequence of a, that is, if it is equal to a[l],a[l+1],...,a[r] for some (l,r).

Baseline thinking

Start with the most direct exhaustive search. That gives a correctness anchor before optimizing.

Pattern signal: Array · Hash Map · Sliding Window

Example 1

[4,2,4,5,6]

Example 2

[5,2,1,2,5,2,1,2,5]

Core Insight

What unlocks the optimal approach

The main point here is for the subarray to contain unique elements for each index. Only the first subarrays starting from that index have unique elements.
This can be solved using the two pointers technique

Interview move: turn each hint into an invariant you can check after every iteration/recursion step.

Step 03

Algorithm Walkthrough

Iteration Checklist

Define state (indices, window, stack, map, DP cell, or recursion frame).
Apply one transition step and update the invariant.
Record answer candidate when condition is met.
Continue until all input is consumed.

Use the first example testcase as your mental trace to verify each transition.

Step 04

Edge Cases

Minimum Input

Single element / shortest valid input

Validate boundary behavior before entering the main loop or recursion.

Duplicates & Repeats

Repeated values / repeated states

Decide whether duplicates should be merged, skipped, or counted explicitly.

Extreme Constraints

Upper-end input sizes

Re-check complexity target against constraints to avoid time-limit issues.

Invalid / Corner Shape

Empty collections, zeros, or disconnected structures

Handle special-case structure before the core algorithm path.

Step 05

Full Annotated Code

Source-backed implementations are provided below for direct study and interview prep.

// Accepted solution for LeetCode #1695: Maximum Erasure Value
class Solution {
    public int maximumUniqueSubarray(int[] nums) {
        int[] d = new int[10001];
        int n = nums.length;
        int[] s = new int[n + 1];
        for (int i = 0; i < n; ++i) {
            s[i + 1] = s[i] + nums[i];
        }
        int ans = 0, j = 0;
        for (int i = 1; i <= n; ++i) {
            int v = nums[i - 1];
            j = Math.max(j, d[v]);
            ans = Math.max(ans, s[i] - s[j]);
            d[v] = i;
        }
        return ans;
    }
}

// Accepted solution for LeetCode #1695: Maximum Erasure Value
func maximumUniqueSubarray(nums []int) (ans int) {
	d := [10001]int{}
	n := len(nums)
	s := make([]int, n+1)
	for i, v := range nums {
		s[i+1] = s[i] + v
	}
	for i, j := 1, 0; i <= n; i++ {
		v := nums[i-1]
		j = max(j, d[v])
		ans = max(ans, s[i]-s[j])
		d[v] = i
	}
	return
}

# Accepted solution for LeetCode #1695: Maximum Erasure Value
class Solution:
    def maximumUniqueSubarray(self, nums: List[int]) -> int:
        d = [0] * (max(nums) + 1)
        s = list(accumulate(nums, initial=0))
        ans = j = 0
        for i, v in enumerate(nums, 1):
            j = max(j, d[v])
            ans = max(ans, s[i] - s[j])
            d[v] = i
        return ans

// Accepted solution for LeetCode #1695: Maximum Erasure Value
impl Solution {
    pub fn maximum_unique_subarray(nums: Vec<i32>) -> i32 {
        let m = *nums.iter().max().unwrap() as usize;
        let mut d = vec![0; m + 1];
        let n = nums.len();

        let mut s = vec![0; n + 1];
        for i in 0..n {
            s[i + 1] = s[i] + nums[i];
        }

        let mut ans = 0;
        let mut j = 0;
        for (i, &v) in nums.iter().enumerate().map(|(i, v)| (i + 1, v)) {
            j = j.max(d[v as usize]);
            ans = ans.max(s[i] - s[j]);
            d[v as usize] = i;
        }

        ans
    }
}

// Accepted solution for LeetCode #1695: Maximum Erasure Value
function maximumUniqueSubarray(nums: number[]): number {
    const m = Math.max(...nums);
    const n = nums.length;
    const s: number[] = Array.from({ length: n + 1 }, () => 0);
    for (let i = 1; i <= n; ++i) {
        s[i] = s[i - 1] + nums[i - 1];
    }
    const d = Array.from({ length: m + 1 }, () => 0);
    let [ans, j] = [0, 0];
    for (let i = 1; i <= n; ++i) {
        j = Math.max(j, d[nums[i - 1]]);
        ans = Math.max(ans, s[i] - s[j]);
        d[nums[i - 1]] = i;
    }
    return ans;
}

Step 06

Interactive Study Demo

Use this to step through a reusable interview workflow for this problem.

Custom checkpoints (one per line)

Press Step or Run All to begin.

Step 07

Complexity Analysis

Time

O(n)

Space

O(n)

Approach Breakdown

BRUTE FORCE

O(n × k) time

O(1) space

For each starting index, scan the next k elements to compute the window aggregate. There are n−k+1 starting positions, each requiring O(k) work, giving O(n × k) total. No extra space since we recompute from scratch each time.

SLIDING WINDOW

O(n) time

O(k) space

The window expands and contracts as we scan left to right. Each element enters the window at most once and leaves at most once, giving 2n total operations = O(n). Space depends on what we track inside the window (a hash map of at most k distinct elements, or O(1) for a fixed-size window).

Shortcut: Each element enters and exits the window once → O(n) amortized, regardless of window size.

Coach Notes

Common Mistakes

Review these before coding to avoid predictable interview regressions.

Off-by-one on range boundaries

Wrong move: Loop endpoints miss first/last candidate.

Usually fails on: Fails on minimal arrays and exact-boundary answers.

Fix: Re-derive loops from inclusive/exclusive ranges before coding.

Mutating counts without cleanup

Wrong move: Zero-count keys stay in map and break distinct/count constraints.

Usually fails on: Window/map size checks are consistently off by one.

Fix: Delete keys when count reaches zero.

Shrinking the window only once

Wrong move: Using `if` instead of `while` leaves the window invalid for multiple iterations.

Usually fails on: Over-limit windows stay invalid and produce wrong lengths/counts.

Fix: Shrink in a `while` loop until the invariant is valid again.