Longest Uncommon Subsequence II

Longest Uncommon Subsequence II - Problem

Given an array of strings strs, return the length of the longest uncommon subsequence between them. If the longest uncommon subsequence does not exist, return -1.

An uncommon subsequence between an array of strings is a string that is a subsequence of one string but not the others.

A subsequence of a string s is a string that can be obtained after deleting any number of characters from s. For example, "abc" is a subsequence of "aebdc" because you can delete the underlined characters in "aebdc" to get "abc". Other subsequences of "aebdc" include "aebdc", "aeb", and "" (empty string).

Input & Output

Example 1 — Mixed Unique and Duplicate

$ Input: strs = ["aba", "cdc", "eae", "eae"]

› Output: 3

💡 Note: "aba" and "cdc" are unique strings (appear only once). "eae" appears twice so it's not uncommon. The longest unique strings have length 3.

Example 2 — All Identical Strings

$ Input: strs = ["aaa", "aaa", "aa"]

› Output: 2

💡 Note: "aaa" appears twice, but "aa" is unique. The longest uncommon subsequence has length 2.

Example 3 — All Strings Are Identical

$ Input: strs = ["abc", "abc", "abc"]

› Output: -1

💡 Note: All strings are identical, so no string is unique. No uncommon subsequence exists.

Constraints

2 ≤ strs.length ≤ 50
1 ≤ strs[i].length ≤ 10
strs[i] consists of lowercase English letters

Visualization

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Asked in

G Google 15 f Facebook 12

The key insight is that any string appearing exactly once in the array is automatically an uncommon subsequence of itself. Best approach uses a hash map to count frequencies in O(n) time. Time: O(n), Space: O(n)

Common Approaches

✓ Bfs

⏱️ Time: N/A Space: N/A

Brute Force - Check All Subsequences

⏱️ Time: O(n × 2^m × n × m) Space: O(2^m)

For each string, generate all its subsequences, then check if each subsequence appears as a subsequence in any other string. Keep track of the longest uncommon subsequence found.

Hash Map - Count Frequencies

⏱️ Time: O(n) Space: O(n)

Count the frequency of each string using a hash map. Then iterate through the strings again to find the longest string that appears exactly once.

Key Insight - Unique Strings

⏱️ Time: O(n²) Space: O(1)

The key insight is that if a string is unique (appears only once in the array), then the string itself is an uncommon subsequence. We only need to check if strings are duplicates and return the length of the longest unique string.

Algorithm Steps — Algorithm Steps

Code -

solution.c — C

Time & Space Complexity

Time Complexity

⏱️

✓ Linear Growth

Space Complexity

⚡ Linearithmic Space

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Medium Frequency

~15 min Avg. Time

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Smart Actions

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