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HomeLeetCode ProblemsMaximize the Minimum Powered City
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How to Solve Maximize the Minimum Powered City Problem

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Hard#2528
LeetCode Problem

Maximize the Minimum Powered City

You are given a 0-indexed integer array stations of length n, where stations[i] represents the number of power stations in the ith city. Each power station can provide power to every city in a fixed range. In other words, if the range is denoted by r, then a power station at city i can provide power to all cities j such that |i - j| <= r and 0 <= i, j <= n - 1. The power of a city is the total number of power stations it is being provided power from. The government has sanctioned building k more power stations, each of which can be built in any city, and have the same range as the pre-existing ones. Given the two integers r and k, return the maximum possible minimum power of a city, if the additional power stations are built optimally. Note that you can build the k power stations in multiple cities.

ArrayBinary SearchGreedyQueueSliding WindowPrefix Sum

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Problem Breakdown

Understanding the Maximize the Minimum Powered City Problem

Let's break down this LeetCode problem and understand what makes it challenging in interview settings.

Problem Statement

You are given a 0-indexed integer array stations of length n, where stations[i] represents the number of power stations in the ith city. Each power station can provide power to every city in a fixed range. In other words, if the range is denoted by r, then a power station at city i can provide power to all cities j such that |i - j| <= r and 0 <= i, j <= n - 1. The power of a city is the total number of power stations it is being provided power from. The government has sanctioned building k more power stations, each of which can be built in any city, and have the same range as the pre-existing ones. Given the two integers r and k, return the maximum possible minimum power of a city, if the additional power stations are built optimally. Note that you can build the k power stations in multiple cities.

HardProblem #2528
LeetCode

Maximize the Minimum Powered City

Related Topics

ArrayBinary SearchGreedyQueueSliding WindowPrefix Sum

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Examples

# Example 1

Input
stations = [1,2,4,5,0], r = 1, k = 2
Output
5

# Example 2

Input
stations = [4,4,4,4], r = 0, k = 3
Output
4

Constraints

n == stations.length
1 <= n <= 105
0 <= stations[i] <= 105
0 <= r <= n - 1
0 <= k <= 109

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Solve Maximize the Minimum Powered City — You are given a 0-indexed integer array stations of length n, where stations[i] ...

Here's the optimal approach using Array:

def solve(input):
# Optimal O(n) solution
return result

Time: O(n)  |  Space: O(n)

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Frequently Asked Questions

Common questions about solving Maximize the Minimum Powered City and using PhantomCodeAI during coding interviews.

How does PhantomCodeAI help with LeetCode problems during a coding interview?
PhantomCodeAI reads the problem from a screenshot or listens to it via audio, then generates a complete solution with a step-by-step approach and time/space complexity, so you can focus on explaining your reasoning rather than getting stuck on implementation.
Which programming languages does PhantomCodeAI support?
11 languages including Python, Java, C++, JavaScript, TypeScript, Go, Rust, Ruby, Swift, Kotlin, and C#. Set a preferred language to get idiomatic solutions.
Does PhantomCodeAI work with HackerRank, CodeSignal, and CoderPad?
Yes — it works alongside LeetCode, HackerRank, CodeSignal, CoderPad, and HackerEarth, plus any browser-based coding environment.
Can PhantomCodeAI handle follow-up questions and modifications?
Yes. Capture the modified problem by screenshot or audio and get an updated solution within seconds, including new edge cases and optimizations.
Is PhantomCodeAI detectable during live interviews?
PhantomCodeAI runs as a native desktop overlay that does not appear in screen shares, recordings, or proctoring software, and works with Zoom, Google Meet, Microsoft Teams, and major coding platforms.
Should I still practice these problems on my own first?
Yes. The most durable preparation is solving problems unaided first, then using AI afterward to review your approach, complexity, and missed edge cases — use it to debrief and learn, not to skip the reps.

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