How do you solve Longest Common Prefix of K Strings After Removal on LeetCode?

Longest Common Prefix of K Strings After Removal (LeetCode #3485) 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: Using a Trie optimizes prefix counting.

What is the time complexity of Longest Common Prefix of K Strings After Removal?

The optimal solution for Longest Common Prefix of K Strings After Removal runs in O(n) time and uses O(n) space. Building the Trie takes O(n) time, and checking counts for each string is also O(n).

What is the brute force solution for Longest Common Prefix of K Strings After Removal?

The brute force approach for Longest Common Prefix of K Strings After Removal is Brute Force, with O(n²) time complexity and O(1) space complexity. We check all combinations of k strings after removing one string at a time. This is straightforward but inefficient as it requires checking many combinations. The optimal Optimal Solution approach improves this to O(n).

What algorithm or data structure is used to solve Longest Common Prefix of K Strings After Removal?

Longest Common Prefix of K Strings After Removal uses the following concepts: Array, String, Trie. The recommended patterns to study are: Hash Map, Array.

What are the interview tips for Longest Common Prefix of K Strings After Removal?

Explain your thought process clearly. Discuss edge cases upfront. Practice Trie-related problems.

What are common mistakes when solving Longest Common Prefix of K Strings After Removal?

Not handling cases where remaining strings are fewer than k. Misunderstanding how to calculate LCP efficiently.

#3485

Longest Common Prefix of K Strings After Removal

Hard

ArrayStringTrieHash MapArray

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)

Using a Trie allows us to efficiently store and count prefixes, making it easy to find the longest common prefix for any k strings after removing one.

⚙️

Algorithm

3 steps

1Step 1: Build a Trie from all strings, maintaining counts of how many strings share each prefix.
2Step 2: For each string removed, check the counts in the Trie for the remaining strings.
3Step 3: Determine the maximum length of common prefixes for any k strings using the counts.

solution.py35 lines

1class TrieNode:
2    def __init__(self):
3        self.children = {}
4        self.count = 0
5
6def insert(root, word):
7    node = root
8    for char in word:
9        if char not in node.children:
10            node.children[char] = TrieNode()
11        node = node.children[char]
12        node.count += 1
13
14def longestCommonPrefix(words, k):
15    root = TrieNode()
16    for word in words:
17        insert(root, word)
18    answer = []
19    for i in range(len(words)):
20        if len(words) - 1 < k:
21            answer.append(0)
22            continue
23        max_lcp = 0
24        node = root
25        for char in words[i]:
26            if char in node.children:
27                node = node.children[char]
28                if node.count >= k:
29                    max_lcp += 1
30                else:
31                    break
32            else:
33                break
34        answer.append(max_lcp)
35    return answer

ℹ

Complexity note: Building the Trie takes O(n) time, and checking counts for each string is also O(n).

1Using a Trie optimizes prefix counting.
2Removing one string affects the counts but not the structure.

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