How do you solve Reward Top K Students on LeetCode?

Reward Top K Students (LeetCode #2512) can be solved using Brute Force or Optimal Solution. The optimal approach is Optimal Solution with O(n log n) time complexity and O(n) space complexity. Key insight: Using hash maps allows for efficient score tracking and retrieval.

What is the brute force solution for Reward Top K Students?

The brute force approach for Reward Top K Students is Brute Force, with O(n²) time complexity and O(1) space complexity. In this approach, we will iterate through each student's feedback report, count the positive and negative words, and calculate their scores. This method is straightforward but inefficient as it checks each report multiple times. The optimal Optimal Solution approach improves this to O(n log n).

What are the interview tips for Reward Top K Students?

Always clarify the problem requirements and constraints before jumping into coding. Think about edge cases, such as when k is greater than the number of students. Practice explaining your thought process as you code; it helps in interviews.

What are common mistakes when solving Reward Top K Students?

Not using sets for quick lookups of positive and negative feedback words. Forgetting to handle ties in scores correctly by sorting based on IDs.

#2512

Reward Top K Students

Medium

ArrayHash TableStringSortingHeap (Priority Queue)Hash MapArray

LeetCode ↗

Approaches

Brute ForceOptimal

Complexity Comparison

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

💡

Intuition

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

In this approach, we use a hash map to store scores efficiently while iterating through the reports only once. This reduces the time complexity significantly, allowing us to handle larger inputs.

⚙️

Algorithm

5 steps

1Step 1: Create a hash map to store scores for each student.
2Step 2: Create sets for positive and negative feedback for O(1) lookups.
3Step 3: Iterate through each report, split the words, and update the scores using the hash map.
4Step 4: Sort the students based on their scores and IDs using a custom comparator.
5Step 5: Return the top k students.

solution.py15 lines

1def topKStudents(positive_feedback, negative_feedback, report, student_id, k):
2    score = {}
3    pos_set = set(positive_feedback)
4    neg_set = set(negative_feedback)
5    for i in range(len(report)):
6        words = report[i].split()
7        student = student_id[i]
8        score[student] = score.get(student, 0)
9        for word in words:
10            if word in pos_set:
11                score[student] += 3
12            elif word in neg_set:
13                score[student] -= 1
14    sorted_students = sorted(score.items(), key=lambda x: (-x[1], x[0]))
15    return [student for student, _ in sorted_students[:k]]

ℹ

Complexity note: The time complexity is O(n log n) due to the sorting step after calculating the scores, while the space complexity is O(n) for storing the scores in a hash map.

1Using hash maps allows for efficient score tracking and retrieval.
2Sorting based on multiple criteria (score and ID) is a common pattern in ranking problems.

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