LeetCode #2382 — HARD

Maximum Segment Sum After Removals

Break down a hard problem into reliable checkpoints, edge-case handling, and complexity trade-offs.

Solve on LeetCode
The Problem

Problem Statement

You are given two 0-indexed integer arrays nums and removeQueries, both of length n. For the ith query, the element in nums at the index removeQueries[i] is removed, splitting nums into different segments.

A segment is a contiguous sequence of positive integers in nums. A segment sum is the sum of every element in a segment.

Return an integer array answer, of length n, where answer[i] is the maximum segment sum after applying the ith removal.

Note: The same index will not be removed more than once.

Example 1:

Input: nums = [1,2,5,6,1], removeQueries = [0,3,2,4,1]
Output: [14,7,2,2,0]
Explanation: Using 0 to indicate a removed element, the answer is as follows:
Query 1: Remove the 0th element, nums becomes [0,2,5,6,1] and the maximum segment sum is 14 for segment [2,5,6,1].
Query 2: Remove the 3rd element, nums becomes [0,2,5,0,1] and the maximum segment sum is 7 for segment [2,5].
Query 3: Remove the 2nd element, nums becomes [0,2,0,0,1] and the maximum segment sum is 2 for segment [2]. 
Query 4: Remove the 4th element, nums becomes [0,2,0,0,0] and the maximum segment sum is 2 for segment [2]. 
Query 5: Remove the 1st element, nums becomes [0,0,0,0,0] and the maximum segment sum is 0, since there are no segments.
Finally, we return [14,7,2,2,0].

Example 2:

Input: nums = [3,2,11,1], removeQueries = [3,2,1,0]
Output: [16,5,3,0]
Explanation: Using 0 to indicate a removed element, the answer is as follows:
Query 1: Remove the 3rd element, nums becomes [3,2,11,0] and the maximum segment sum is 16 for segment [3,2,11].
Query 2: Remove the 2nd element, nums becomes [3,2,0,0] and the maximum segment sum is 5 for segment [3,2].
Query 3: Remove the 1st element, nums becomes [3,0,0,0] and the maximum segment sum is 3 for segment [3].
Query 4: Remove the 0th element, nums becomes [0,0,0,0] and the maximum segment sum is 0, since there are no segments.
Finally, we return [16,5,3,0].

Constraints:

  • n == nums.length == removeQueries.length
  • 1 <= n <= 105
  • 1 <= nums[i] <= 109
  • 0 <= removeQueries[i] < n
  • All the values of removeQueries are unique.
Patterns Used
🔗Union-Find→📐Segment Tree→

Roadmap

  1. Brute Force Baseline
  2. Core Insight
  3. Algorithm Walkthrough
  4. Edge Cases
  5. Full Annotated Code
  6. Interactive Study Demo
  7. Complexity Analysis
Step 01

Brute Force Baseline

Problem summary: You are given two 0-indexed integer arrays nums and removeQueries, both of length n. For the ith query, the element in nums at the index removeQueries[i] is removed, splitting nums into different segments. A segment is a contiguous sequence of positive integers in nums. A segment sum is the sum of every element in a segment. Return an integer array answer, of length n, where answer[i] is the maximum segment sum after applying the ith removal. Note: The same index will not be removed more than once.

Baseline thinking

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

Pattern signal: Array · Union-Find · Segment Tree

Example 1

[1,2,5,6,1]
[0,3,2,4,1]

Example 2

[3,2,11,1]
[3,2,1,0]
Step 02

Core Insight

What unlocks the optimal approach

  • Use a sorted data structure to collect removal points and store the segments.
  • Use a heap or priority queue to store segment sums and their corresponding boundaries.
  • Make sure to remove invalid segments from the heap.
Interview move: turn each hint into an invariant you can check after every iteration/recursion step.
Step 03

Algorithm Walkthrough

Iteration Checklist

  1. Define state (indices, window, stack, map, DP cell, or recursion frame).
  2. Apply one transition step and update the invariant.
  3. Record answer candidate when condition is met.
  4. 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
Largest constraint values
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 #2382: Maximum Segment Sum After Removals
class Solution {
    private int[] p;
    private long[] s;

    public long[] maximumSegmentSum(int[] nums, int[] removeQueries) {
        int n = nums.length;
        p = new int[n];
        s = new long[n];
        for (int i = 0; i < n; ++i) {
            p[i] = i;
        }
        long[] ans = new long[n];
        long mx = 0;
        for (int j = n - 1; j > 0; --j) {
            int i = removeQueries[j];
            s[i] = nums[i];
            if (i > 0 && s[find(i - 1)] > 0) {
                merge(i, i - 1);
            }
            if (i < n - 1 && s[find(i + 1)] > 0) {
                merge(i, i + 1);
            }
            mx = Math.max(mx, s[find(i)]);
            ans[j - 1] = mx;
        }
        return ans;
    }

    private int find(int x) {
        if (p[x] != x) {
            p[x] = find(p[x]);
        }
        return p[x];
    }

    private void merge(int a, int b) {
        int pa = find(a), pb = find(b);
        p[pa] = pb;
        s[pb] += s[pa];
    }
}
Step 06

Interactive Study Demo

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

Press Step or Run All to begin.
Step 07

Complexity Analysis

Time
O(α(n))
Space
O(n)

Approach Breakdown

BRUTE FORCE
O(n²) time
O(n) space

Track components with a list or adjacency matrix. Each union operation may need to update all n elements’ component labels, giving O(n) per union. For n union operations total: O(n²). Find is O(1) with direct lookup, but union dominates.

UNION-FIND
O(α(n)) time
O(n) space

With path compression and union by rank, each find/union operation takes O(α(n)) amortized time, where α is the inverse Ackermann function — effectively constant. Space is O(n) for the parent and rank arrays. For m operations on n elements: O(m × α(n)) total.

Shortcut: Union-Find with path compression + rank → O(α(n)) per operation ≈ O(1). Just say “nearly constant.”
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.

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