LeetCode #1840 — HARD

Maximum Building Height

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

Solve on LeetCode
The Problem

Problem Statement

You want to build n new buildings in a city. The new buildings will be built in a line and are labeled from 1 to n.

However, there are city restrictions on the heights of the new buildings:

  • The height of each building must be a non-negative integer.
  • The height of the first building must be 0.
  • The height difference between any two adjacent buildings cannot exceed 1.

Additionally, there are city restrictions on the maximum height of specific buildings. These restrictions are given as a 2D integer array restrictions where restrictions[i] = [idi, maxHeighti] indicates that building idi must have a height less than or equal to maxHeighti.

It is guaranteed that each building will appear at most once in restrictions, and building 1 will not be in restrictions.

Return the maximum possible height of the tallest building.

Example 1:

Input: n = 5, restrictions = [[2,1],[4,1]]
Output: 2
Explanation: The green area in the image indicates the maximum allowed height for each building.
We can build the buildings with heights [0,1,2,1,2], and the tallest building has a height of 2.

Example 2:

Input: n = 6, restrictions = []
Output: 5
Explanation: The green area in the image indicates the maximum allowed height for each building.
We can build the buildings with heights [0,1,2,3,4,5], and the tallest building has a height of 5.

Example 3:

Input: n = 10, restrictions = [[5,3],[2,5],[7,4],[10,3]]
Output: 5
Explanation: The green area in the image indicates the maximum allowed height for each building.
We can build the buildings with heights [0,1,2,3,3,4,4,5,4,3], and the tallest building has a height of 5.

Constraints:

  • 2 <= n <= 109
  • 0 <= restrictions.length <= min(n - 1, 105)
  • 2 <= idi <= n
  • idi is unique.
  • 0 <= maxHeighti <= 109

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 want to build n new buildings in a city. The new buildings will be built in a line and are labeled from 1 to n. However, there are city restrictions on the heights of the new buildings: The height of each building must be a non-negative integer. The height of the first building must be 0. The height difference between any two adjacent buildings cannot exceed 1. Additionally, there are city restrictions on the maximum height of specific buildings. These restrictions are given as a 2D integer array restrictions where restrictions[i] = [idi, maxHeighti] indicates that building idi must have a height less than or equal to maxHeighti. It is guaranteed that each building will appear at most once in restrictions, and building 1 will not be in restrictions. Return the maximum possible height of the tallest building.

Baseline thinking

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

Pattern signal: Array · Math

Example 1

5
[[2,1],[4,1]]

Example 2

6
[]

Example 3

10
[[5,3],[2,5],[7,4],[10,3]]

Related Problems

  • Find Maximum Value in a Constrained Sequence (find-maximum-value-in-a-constrained-sequence)
Step 02

Core Insight

What unlocks the optimal approach

  • Is it possible to find the max height if given the height range of a particular building?
  • You can find the height range of a restricted building by doing 2 passes from the left and right.
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 #1840: Maximum Building Height
class Solution {
    public int maxBuilding(int n, int[][] restrictions) {
        List<int[]> r = new ArrayList<>();
        r.addAll(Arrays.asList(restrictions));
        r.add(new int[] {1, 0});
        Collections.sort(r, (a, b) -> a[0] - b[0]);
        if (r.get(r.size() - 1)[0] != n) {
            r.add(new int[] {n, n - 1});
        }
        int m = r.size();
        for (int i = 1; i < m; ++i) {
            int[] a = r.get(i - 1), b = r.get(i);
            b[1] = Math.min(b[1], a[1] + b[0] - a[0]);
        }
        for (int i = m - 2; i > 0; --i) {
            int[] a = r.get(i), b = r.get(i + 1);
            a[1] = Math.min(a[1], b[1] + b[0] - a[0]);
        }
        int ans = 0;
        for (int i = 0; i < m - 1; ++i) {
            int[] a = r.get(i), b = r.get(i + 1);
            int t = (a[1] + b[1] + b[0] - a[0]) / 2;
            ans = Math.max(ans, t);
        }
        return ans;
    }
}
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(m × log m)
Space
O(m)

Approach Breakdown

BRUTE FORCE
O(n²) time
O(1) space

Two nested loops check every pair or subarray. The outer loop fixes a starting point, the inner loop extends or searches. For n elements this gives up to n²/2 operations. No extra space, but the quadratic time is prohibitive for large inputs.

OPTIMIZED
O(n) time
O(1) space

Most array problems have an O(n²) brute force (nested loops) and an O(n) optimal (single pass with clever state tracking). The key is identifying what information to maintain as you scan: a running max, a prefix sum, a hash map of seen values, or two pointers.

Shortcut: If you are using nested loops on an array, there is almost always an O(n) solution. Look for the right auxiliary state.
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.

Overflow in intermediate arithmetic

Wrong move: Temporary multiplications exceed integer bounds.

Usually fails on: Large inputs wrap around unexpectedly.

Fix: Use wider types, modular arithmetic, or rearranged operations.