How do you solve Minimum Number of People to Teach on LeetCode?

Minimum Number of People to Teach (LeetCode #1733) can be solved using Brute Force or Optimal Solution. The optimal approach is Optimal Solution with O(n + m) time complexity and O(m) space complexity. Key insight: Understanding the relationships between users is crucial.

What is the brute force solution for Minimum Number of People to Teach?

The brute force approach for Minimum Number of People to Teach is Brute Force, with O(n²) time complexity and O(1) space complexity. The brute force approach involves checking each language one by one to see how many users need to be taught that language to ensure all friends can communicate. This method is straightforward but can be inefficient. The optimal Optimal Solution approach improves this to O(n + m).

What algorithm or data structure is used to solve Minimum Number of People to Teach?

Minimum Number of People to Teach uses the following concepts: Array, Hash Table, Greedy. The recommended patterns to study are: Hash Map, Array.

What are the interview tips for Minimum Number of People to Teach?

Clarify the problem requirements and constraints before diving into coding. Think about edge cases, such as users knowing all languages or none. Discuss your thought process and approach with the interviewer.

What are common mistakes when solving Minimum Number of People to Teach?

Not considering all friendships when counting users to teach. Failing to account for users who already know the language.

#1733

Minimum Number of People to Teach

Medium

ArrayHash TableGreedyHash MapArray

LeetCode ↗

Approaches

Brute ForceOptimal

Complexity Comparison

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

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Intuition

Time O(n + m)Space O(m)

The optimal solution leverages a more systematic approach using a mapping of languages to users, allowing us to efficiently determine which users need to be taught based on their friendships.

⚙️

Algorithm

4 steps

1Step 1: Create a mapping of each language to the users who know it.
2Step 2: For each friendship, track which languages are known by the users involved.
3Step 3: For each language, count how many users from the friendship pairs need to be taught that language.
4Step 4: Return the minimum count of users needed to be taught across all languages.

solution.py15 lines

1def minTeach(n, languages, friendships):
2    from collections import defaultdict
3    lang_to_users = defaultdict(set)
4    for i, lang_set in enumerate(languages):
5        for lang in lang_set:
6            lang_to_users[lang].add(i + 1)
7    min_teach = float('inf')
8    for lang in range(1, n + 1):
9        to_teach = set()
10        for u, v in friendships:
11            if lang not in languages[u - 1] and lang not in languages[v - 1]:
12                to_teach.add(u)
13                to_teach.add(v)
14        min_teach = min(min_teach, len(to_teach))
15    return min_teach

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Complexity note: The time complexity is O(n + m) because we process each language and friendship once. The space complexity is O(m) for storing the mapping of languages to users.

1Understanding the relationships between users is crucial.
2Mapping languages to users helps in efficiently determining who needs to be taught.

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