How do you solve Maximize Distance to Closest Person on LeetCode?

Maximize Distance to Closest Person (LeetCode #849) can be solved using Brute Force or Optimal Solution. The optimal approach is Optimal Solution with O(n) time complexity and O(1) space complexity. Key insight: The distance to the closest person can be maximized by considering both leading and trailing empty seats.

What is the time complexity of Maximize Distance to Closest Person?

The optimal solution for Maximize Distance to Closest Person runs in O(n) time and uses O(1) space. This complexity is linear because we only make a single pass through the array, checking each seat once.

What is the brute force solution for Maximize Distance to Closest Person?

The brute force approach for Maximize Distance to Closest Person is Brute Force, with O(n²) time complexity and O(1) space complexity. In the brute force approach, we check every empty seat and calculate the distance to the nearest person. This is straightforward but inefficient, as it involves checking distances multiple times. The optimal Optimal Solution approach improves this to O(n).

What algorithm or data structure is used to solve Maximize Distance to Closest Person?

Maximize Distance to Closest Person uses the following concepts: Array. The recommended patterns to study are: Array, Two Pointers.

What are the interview tips for Maximize Distance to Closest Person?

Always think about edge cases and how they might affect your solution. Discuss your thought process out loud to help the interviewer follow your reasoning. If you get stuck, try to simplify the problem or break it down into smaller parts.

What are common mistakes when solving Maximize Distance to Closest Person?

Not considering edge cases like leading or trailing empty seats. Using nested loops unnecessarily, leading to inefficient solutions.

#849

Maximize Distance to Closest Person

Medium

ArrayArrayTwo Pointers

LeetCode ↗

Approaches

Brute ForceOptimal

Complexity Comparison

	Brute Force	Optimal Solution★
Time	O(n²)	O(n)
Space	O(1)	O(1)

💡

Intuition

Time O(n)Space O(1)

The optimal solution leverages the observation that the maximum distance can be found by considering the edges of the occupied seats and the gaps between them. We can calculate the maximum distance directly without nested loops.

⚙️

Algorithm

4 steps

1Step 1: Initialize variables for the maximum distance and the previous occupied seat index.
2Step 2: Traverse the seats array to find the first occupied seat and handle leading zeros.
3Step 3: For each subsequent occupied seat, calculate the distance to the previous occupied seat and update the maximum distance accordingly.
4Step 4: Handle trailing zeros after the last occupied seat.

solution.py12 lines

1def maxDistToClosest(seats):
2    max_distance = 0
3    prev = -1
4    for i in range(len(seats)):
5        if seats[i] == 1:
6            if prev == -1:
7                max_distance = i  # Leading zeros
8            else:
9                max_distance = max(max_distance, (i - prev) // 2)
10            prev = i
11    max_distance = max(max_distance, len(seats) - 1 - prev)  # Trailing zeros
12    return max_distance

ℹ

Complexity note: This complexity is linear because we only make a single pass through the array, checking each seat once.

1The distance to the closest person can be maximized by considering both leading and trailing empty seats.
2The optimal solution reduces unnecessary calculations by leveraging the positions of occupied seats.

Solutions and explanations are original Tejav content. Problem titles © LeetCode — use the LeetCode button above for the full problem statement.