Leetcode #2141: Maximum Running Time of N Computers

In this guide, we solve Leetcode #2141 Maximum Running Time of N Computers in Python and focus on the core idea that makes the solution efficient.

You will see the intuition, the step-by-step method, and a clean Python implementation you can use in interviews.

Leetcode

Problem Statement

You have n computers. You are given the integer n and a 0-indexed integer array batteries where the ith battery can run a computer for batteries[i] minutes.

Quick Facts

Difficulty: Hard
Premium: No
Tags: Greedy, Array, Binary Search, Sorting

Intuition

The problem structure suggests a monotonic decision, which makes binary search a natural fit.

By halving the search space each step, we reach the answer efficiently.

Approach

Search either directly on a sorted array or on the answer space using a check function.

Each check is fast, and the logarithmic search keeps the overall runtime low.

Steps:

Define the search bounds.
Check the mid point condition.
Narrow the bounds until convergence.

Example

Input: n = 2, batteries = [3,3,3]
Output: 4
Explanation: 
Initially, insert battery 0 into the first computer and battery 1 into the second computer.
After two minutes, remove battery 1 from the second computer and insert battery 2 instead. Note that battery 1 can still run for one minute.
At the end of the third minute, battery 0 is drained, and you need to remove it from the first computer and insert battery 1 instead.
By the end of the fourth minute, battery 1 is also drained, and the first computer is no longer running.
We can run the two computers simultaneously for at most 4 minutes, so we return 4.

Python Solution

class Solution:
    def maxRunTime(self, n: int, batteries: List[int]) -> int:
        l, r = 0, sum(batteries)
        while l < r:
            mid = (l + r + 1) >> 1
            if sum(min(x, mid) for x in batteries) >= n * mid:
                l = mid
            else:
                r = mid - 1
        return l

Complexity

The time complexity is $O(n \times \log M)$ , where $M$ is the total power of all batteries, and the space complexity is $O(1)$ . The space complexity is $O(1)$ .

Edge Cases and Pitfalls

Watch for boundary values, empty inputs, and duplicate values where applicable. If the problem involves ordering or constraints, confirm the invariant is preserved at every step.

Summary

This Python solution focuses on the essential structure of the problem and keeps the implementation interview-friendly while meeting the constraints.

Example

Input: n = 2, batteries = [3,3,3]
Output: 4
Explanation: 
Initially, insert battery 0 into the first computer and battery 1 into the second computer.
After two minutes, remove battery 1 from the second computer and insert battery 2 instead. Note that battery 1 can still run for one minute.
At the end of the third minute, battery 0 is drained, and you need to remove it from the first computer and insert battery 1 instead.
By the end of the fourth minute, battery 1 is also drained, and the first computer is no longer running.
We can run the two computers simultaneously for at most 4 minutes, so we return 4.

Python Solution

class Solution:
    def maxRunTime(self, n: int, batteries: List[int]) -> int:
        l, r = 0, sum(batteries)
        while l < r:
            mid = (l + r + 1) >> 1
            if sum(min(x, mid) for x in batteries) >= n * mid:
                l = mid
            else:
                r = mid - 1
        return l

Leetcode #2141: Maximum Running Time of N Computers

Problem Statement

Quick Facts

Intuition

Approach

Example

Python Solution

Complexity

Edge Cases and Pitfalls

Summary

Ace your next coding interview

Leetcode #2141: Maximum Running Time of N Computers

Problem Statement

Quick Facts

Intuition

Approach

Example

Python Solution

Complexity

Edge Cases and Pitfalls

Summary

Ace your next coding interview