LeetCode #3492 — EASY

Maximum Containers on a Ship

Build confidence with an intuition-first walkthrough focused on math fundamentals.

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The Problem

Problem Statement

You are given a positive integer n representing an n x n cargo deck on a ship. Each cell on the deck can hold one container with a weight of exactly w.

However, the total weight of all containers, if loaded onto the deck, must not exceed the ship's maximum weight capacity, maxWeight.

Return the maximum number of containers that can be loaded onto the ship.

Example 1:

Input: n = 2, w = 3, maxWeight = 15

Output: 4

Explanation:

The deck has 4 cells, and each container weighs 3. The total weight of loading all containers is 12, which does not exceed maxWeight.

Example 2:

Input: n = 3, w = 5, maxWeight = 20

Output: 4

Explanation:

The deck has 9 cells, and each container weighs 5. The maximum number of containers that can be loaded without exceeding maxWeight is 4.

Constraints:

  • 1 <= n <= 1000
  • 1 <= w <= 1000
  • 1 <= maxWeight <= 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 are given a positive integer n representing an n x n cargo deck on a ship. Each cell on the deck can hold one container with a weight of exactly w. However, the total weight of all containers, if loaded onto the deck, must not exceed the ship's maximum weight capacity, maxWeight. Return the maximum number of containers that can be loaded onto the ship.

Baseline thinking

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

Pattern signal: Math

Example 1

2
3
15

Example 2

3
5
20
Step 02

Core Insight

What unlocks the optimal approach

  • What are the limits on the number of containers?
  • We can load at most <code>min(n * n, maxWeight / w)</code> containers.
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
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 #3492: Maximum Containers on a Ship
class Solution {
    public int maxContainers(int n, int w, int maxWeight) {
        return Math.min(n * n * w, maxWeight) / w;
    }
}
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(1)
Space
O(1)

Approach Breakdown

ITERATIVE
O(n) time
O(1) space

Simulate the process step by step — multiply n times, check each number up to n, or iterate through all possibilities. Each step is O(1), but doing it n times gives O(n). No extra space needed since we just track running state.

MATH INSIGHT
O(log n) time
O(1) space

Math problems often have a closed-form or O(log n) solution hidden behind an O(n) simulation. Modular arithmetic, fast exponentiation (repeated squaring), GCD (Euclidean algorithm), and number theory properties can dramatically reduce complexity.

Shortcut: Look for mathematical properties that eliminate iteration. Repeated squaring → O(log n). Modular arithmetic avoids overflow.
Coach Notes

Common Mistakes

Review these before coding to avoid predictable interview regressions.

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.