How do you solve Reschedule Meetings for Maximum Free Time I on LeetCode?

Reschedule Meetings for Maximum Free Time I (LeetCode #3439) can be solved using Brute Force or Optimal Solution. The optimal approach is Optimal Solution with O(n) time complexity and O(n) space complexity. Key insight: Understanding how to manipulate gaps between meetings is crucial for maximizing free time.

What is the time complexity of Reschedule Meetings for Maximum Free Time I?

The optimal solution for Reschedule Meetings for Maximum Free Time I runs in O(n) time and uses O(n) space. The complexity is O(n) because we only traverse the meetings and gaps a limited number of times, making it efficient for large inputs.

What is the brute force solution for Reschedule Meetings for Maximum Free Time I?

The brute force approach for Reschedule Meetings for Maximum Free Time I is Brute Force, with O(n²) time complexity and O(1) space complexity. The brute force approach involves checking every possible way to reschedule up to k meetings and calculating the resulting free time. This is straightforward but inefficient as it explores all combinations. The optimal Optimal Solution approach improves this to O(n).

What algorithm or data structure is used to solve Reschedule Meetings for Maximum Free Time I?

Reschedule Meetings for Maximum Free Time I uses the following concepts: Array, Greedy, Sliding Window. The recommended patterns to study are: Sliding Window, Greedy Algorithms.

What are the interview tips for Reschedule Meetings for Maximum Free Time I?

Clarify the constraints and requirements before diving into coding. Think about edge cases, such as when there are no meetings or when k equals the number of meetings. Practice explaining your thought process clearly as you work through the problem.

What are common mistakes when solving Reschedule Meetings for Maximum Free Time I?

Failing to account for the last gap after the last meeting. Not maintaining the order of meetings when rescheduling.

#3439

Reschedule Meetings for Maximum Free Time I

Medium

ArrayGreedySliding WindowSliding WindowGreedy Algorithms

LeetCode ↗

Approaches

Brute ForceOptimal

Complexity Comparison

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

💡

Intuition

Time O(n)Space O(n)

The optimal approach uses a sliding window technique to efficiently calculate the maximum free time by focusing on the gaps between meetings. This allows us to maximize free time while only considering the necessary meetings.

⚙️

Algorithm

3 steps

1Step 1: Calculate the gaps between consecutive meetings.
2Step 2: Use a sliding window of size k+1 to find the maximum sum of gaps that can be created by moving k meetings.
3Step 3: Return the maximum free time possible after rescheduling.

solution.py16 lines

1# Full working Python code
2
3def maxFreeTimeOptimal(eventTime, k, startTime, endTime):
4    n = len(startTime)
5    gaps = []
6    for i in range(n - 1):
7        gaps.append(startTime[i + 1] - endTime[i])
8    gaps.append(eventTime - endTime[-1])  # gap after last meeting
9    max_free_time = 0
10    current_sum = sum(gaps[:k + 1])
11    max_free_time = current_sum
12    for i in range(k + 1, len(gaps)):
13        current_sum += gaps[i] - gaps[i - (k + 1)]
14        max_free_time = max(max_free_time, current_sum)
15    return max_free_time
16

ℹ

Complexity note: The complexity is O(n) because we only traverse the meetings and gaps a limited number of times, making it efficient for large inputs.

1Understanding how to manipulate gaps between meetings is crucial for maximizing free time.
2Using a sliding window technique allows for efficient calculation of maximum free time.

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