What is the optimal time complexity of solving Meeting Rooms II?

The optimized solution achieves a time complexity of O(n) by using sorting, mathematical shortcuts, or hash table lookups.

What is the auxiliary space complexity for Meeting Rooms II?

The space complexity is O(n) since we only use a minimal/constant amount of extra memory for tracking variables (or auxiliary space if dynamic structures are allocated).

What are the typical edge cases we must handle in Meeting Rooms II?

Key edge cases include empty collections, negative or large bounds causing integer overflow, arrays with only one element, or inputs where target criteria are not met.

How can we optimize the brute force approach for Meeting Rooms II?

Brute force methods often inspect all possible combinations. We optimize this by sorting the input list to enable binary search, or tracking processed items in a set to avoid redundant nested loop lookups.

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Meeting Rooms II

Learn how to solve the 'Meeting Rooms II' problem. This detailed resource details brute force and optimized approaches.

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Problem Statement

Easy

Given an array of meeting time intervals intervals where intervals[i] = [starti, endi], find the minimum number of conference rooms required.

Write a function minMeetingRooms(intervals: List[List[int]]) -> int.

Constraints

•0 <= len(intervals) <= 10^4
•intervals[i].length == 2
•0 <= starti < endi <= 10^6

Examples

Example 1

Input

intervals = [[0,30],[5,10],[15,20]]

Output

Explanation

Room 1: [0,30], Room 2: [5,10], [15,20].

Example 2

Input

intervals = [[7,10],[2,4]]

Output

Explanation

Meetings do not overlap.

Need a Hint?

Analyze the input constraints. Try sorting first (O(n log n)) or using a hash map/set to track seen elements in O(n) time.

Edge Cases to Watch

Empty list or null input variables
Single item lists/arrays
Extremely large input bounds causing integer or stack overflow

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