LeetCode #3560 — EASY

Find Minimum Log Transportation Cost

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

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The Problem

Problem Statement

You are given integers n, m, and k.

There are two logs of lengths n and m units, which need to be transported in three trucks where each truck can carry one log with length at most k units.

You may cut the logs into smaller pieces, where the cost of cutting a log of length x into logs of length len1 and len2 is cost = len1 * len2 such that len1 + len2 = x.

Return the minimum total cost to distribute the logs onto the trucks. If the logs don't need to be cut, the total cost is 0.

Example 1:

Input: n = 6, m = 5, k = 5

Output: 5

Explanation:

Cut the log with length 6 into logs with length 1 and 5, at a cost equal to 1 * 5 == 5. Now the three logs of length 1, 5, and 5 can fit in one truck each.

Example 2:

Input: n = 4, m = 4, k = 6

Output: 0

Explanation:

The two logs can fit in the trucks already, hence we don't need to cut the logs.

Constraints:

  • 2 <= k <= 105
  • 1 <= n, m <= 2 * k
  • The input is generated such that it is always possible to transport the logs.

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 integers n, m, and k. There are two logs of lengths n and m units, which need to be transported in three trucks where each truck can carry one log with length at most k units. You may cut the logs into smaller pieces, where the cost of cutting a log of length x into logs of length len1 and len2 is cost = len1 * len2 such that len1 + len2 = x. Return the minimum total cost to distribute the logs onto the trucks. If the logs don't need to be cut, the total cost is 0.

Baseline thinking

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

Pattern signal: Math

Example 1

6
5
5

Example 2

4
4
6
Step 02

Core Insight

What unlocks the optimal approach

  • If both logs have a length less than <code>k</code>, cost is zero.
  • Can we transport the logs if both logs have length greater than <code>k</code>.
  • Otherwise, pick the log with greater length and cut it into logs with lengths <code>len1</code> and <code>len2</code> such that <code>len1 + len2</code> equals the original length.
  • To minimize the cost <code>len1 * len2</code>, choose <code>len1</code> and <code>len2</code> as far apart as possible (e.g. <code>1</code> and <code>length−1</code>).
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 #3560: Find Minimum Log Transportation Cost
class Solution {
    public long minCuttingCost(int n, int m, int k) {
        int x = Math.max(n, m);
        return x <= k ? 0 : 1L * k * (x - k);
    }
}
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.