How do you solve Longest Common Prefix on LeetCode?

Longest Common Prefix (LeetCode #14) can be solved using Brute Force or Optimal Solution (Horizontal Scanning). The optimal approach is Optimal Solution (Horizontal Scanning) with O(n * m) time complexity and O(1) space complexity. Key insight: The longest common prefix can be found by comparing characters of the strings in a systematic way, which helps in reducing unnecessary comparisons.

What is the brute force solution for Longest Common Prefix?

The brute force approach for Longest Common Prefix is Brute Force, with O(n²) time complexity and O(1) space complexity. The brute force approach involves comparing each string with every other string to find the longest common prefix. We start with the first string and compare it character by character with the others until we find a mismatch. The optimal Optimal Solution (Horizontal Scanning) approach improves this to O(n * m).

What algorithm or data structure is used to solve Longest Common Prefix?

Longest Common Prefix uses the following concepts: Array, String, Trie. The recommended patterns to study are: Hash Map, Array, Two Pointers.

What are the interview tips for Longest Common Prefix?

When you first see this problem, clarify the constraints and ask if there are any edge cases to consider. Communicate your approach clearly, explaining how you will handle mismatches and what your stopping condition will be. Mention edge cases like an empty array or strings with no common prefix to show you are thinking critically about the problem.

What are common mistakes when solving Longest Common Prefix?

Students often forget to handle the case where the input array is empty, which can lead to index errors. Another common mistake is not checking for the length of the strings, which can lead to out-of-bounds errors.

#14

Longest Common Prefix

Easy

ArrayStringTrieHash MapArrayTwo Pointers

LeetCode ↗

Approaches

Brute ForceOptimal

Complexity Comparison

	Brute Force	Optimal Solution (Horizontal Scanning)★
Time	O(n²)	O(n * m)
Space	O(1)	O(1)

💡

Intuition

Time O(n * m)Space O(1)

The optimal solution uses a horizontal scanning method where we compare the prefix with each string in the array, updating the prefix as we go. This is more efficient than the brute force method because we stop checking as soon as we find a mismatch.

⚙️

Algorithm

3 steps

1Step 1: Initialize the prefix as the first string in the array.
2Step 2: Loop through each string in the array starting from the second string.
3Step 3: For each string, compare it with the current prefix character by character, updating the prefix whenever a mismatch is found.

solution.py15 lines

1# Full working Python code with comments
2
3def longest_common_prefix(strs):
4    if not strs:
5        return ""
6    prefix = strs[0]  # Start with the first string as the prefix
7    for string in strs[1:]:  # Compare with the rest of the strings
8        while string[:len(prefix)] != prefix:  # Check if the current prefix matches the start of the string
9            prefix = prefix[:-1]  # Truncate the last character of the prefix
10            if not prefix:
11                return ""  # If prefix is empty, return ""
12    return prefix
13
14# Example usage
15print(longest_common_prefix(["flower", "flow", "flight"]))  # Output: "fl"

ℹ

Complexity note: The time complexity is O(n * m), where n is the number of strings and m is the length of the shortest string. This is because we may need to check each character of the shortest string against the prefix. The space complexity is O(1) since we are using a constant amount of extra space.

1The longest common prefix can be found by comparing characters of the strings in a systematic way, which helps in reducing unnecessary comparisons.
2Using the first string as a reference for the prefix allows us to quickly identify mismatches with other strings.

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