How do you solve Maximum Number of Events That Can Be Attended on LeetCode?

Maximum Number of Events That Can Be Attended (LeetCode #1353) can be solved using Brute Force or Optimal Solution. The optimal approach is Optimal Solution with O(n log n) time complexity and O(1) space complexity. Key insight: Sorting events by end day allows us to make optimal choices for attending events.

What is the time complexity of Maximum Number of Events That Can Be Attended?

The optimal solution for Maximum Number of Events That Can Be Attended runs in O(n log n) time and uses O(1) space. The sorting step takes O(n log n) time, and the single pass through the events takes O(n) time, making this approach efficient overall.

What is the brute force solution for Maximum Number of Events That Can Be Attended?

The brute force approach for Maximum Number of Events That Can Be Attended is Brute Force, with O(n²) time complexity and O(1) space complexity. The brute force approach tries to attend every possible combination of events to find the maximum number of events that can be attended. This method is straightforward but inefficient, as it checks all possible days for each event. The optimal Optimal Solution approach improves this to O(n log n).

What algorithm or data structure is used to solve Maximum Number of Events That Can Be Attended?

Maximum Number of Events That Can Be Attended uses the following concepts: Array, Greedy, Sorting, Heap (Priority Queue). The recommended patterns to study are: Greedy, Sorting, Array.

What are the interview tips for Maximum Number of Events That Can Be Attended?

Always consider sorting when dealing with intervals or ranges. Think about greedy strategies when you need to maximize or minimize something. Explain your thought process clearly; it shows your understanding of the problem.

What are common mistakes when solving Maximum Number of Events That Can Be Attended?

Not sorting the events before processing, which leads to incorrect results. Failing to account for overlapping events properly.

#1353

Maximum Number of Events That Can Be Attended

Medium

ArrayGreedySortingHeap (Priority Queue)GreedySortingArray

LeetCode ↗

Approaches

Brute ForceOptimal

Complexity Comparison

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

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Intuition

Time O(n log n)Space O(1)

The optimal approach sorts the events by their end days and attends them in order, ensuring that we can maximize the number of events attended without overlap. This is efficient because it allows us to make greedy choices based on the earliest finishing event.

⚙️

Algorithm

3 steps

1Step 1: Sort the events by their end day.
2Step 2: Initialize a counter for attended events and a variable to track the last attended day.
3Step 3: Iterate through the sorted events and for each event, check if the last attended day is less than the event's start day. If yes, attend the event on its start day or the last attended day + 1, and update the last attended day.

solution.py14 lines

1# Full working Python code
2
3def maxEvents(events):
4    events.sort(key=lambda x: x[1])
5    count = 0
6    last_day = 0
7    for start, end in events:
8        if last_day < start:
9            last_day = start
10            count += 1
11        elif last_day < end:
12            last_day += 1
13            count += 1
14    return count

ℹ

Complexity note: The sorting step takes O(n log n) time, and the single pass through the events takes O(n) time, making this approach efficient overall.

1Sorting events by end day allows us to make optimal choices for attending events.
2Greedy algorithms work well when making local optimal choices leads to a global optimum.

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