Is K-Similar Strings easy or hard?

K-Similar Strings is classified as a Hard problem because it requires modeling the task as a graph search over permutations and applying pruning to keep the state space manageable. Understanding why only certain swaps should be explored is the key insight that makes the problem tractable.

How to solve K-Similar Strings in O(n)?

An O(n) solution is not possible because the problem requires exploring multiple swap combinations to guarantee the minimum transformation sequence. The standard approach uses BFS or DFS with pruning over string states. These algorithms explore permutations but reduce the search space by only fixing mismatched positions.

What is the best approach for K-Similar Strings?

The most common solution uses Breadth-First Search (BFS) on string states. Each state represents a string configuration, and swaps generate neighboring states. By always fixing the first mismatched index and only swapping with characters that match the target, the branching factor stays small. BFS guarantees the minimum number of swaps because it explores transformations level by level.

What data structure is used in K-Similar Strings?

The BFS solution relies on a queue to process string states level by level and a hash set to track visited permutations. These structures prevent repeated exploration and ensure the algorithm finds the shortest swap sequence. The DFS approach instead uses recursion with pruning conditions.

What is the time complexity of K-Similar Strings?

The worst-case time complexity is O(n!) because every permutation of the string could theoretically be explored. In practice, pruning strategies significantly reduce the search space. Most optimized implementations behave closer to O(n^3)–O(n^4) for typical constraints due to fixing mismatches greedily.

K-Similar Strings Python or Java solution approach?

Both Python and Java implementations typically use BFS with a queue and a visited set. At each step, find the first mismatched index and generate new strings by swapping with characters that match the target string at that position. This keeps the number of generated states manageable.

Is K-Similar Strings asked at Google, Amazon, or Meta?

K-Similar Strings is considered a hard interview problem and appears in advanced algorithm rounds at companies like Google and Meta. It tests knowledge of BFS state search, pruning techniques, and reasoning about permutations. Candidates are often expected to explain the mismatch-based pruning optimization.

Description Solution Video 17:45

854. K-Similar Strings

Hard41.3% acceptance2 companies

Strings s1 and s2 are k-similar (for some non-negative integer k) if we can swap the positions of two letters in s1 exactly k times so that the resulting string equals s2.

Given two anagrams s1 and s2, return the smallest k for which s1 and s2 are k-similar.

Example 1:

Input: s1 = "ab", s2 = "ba"
Output: 1
Explanation: The two string are 1-similar because we can use one swap to change s1 to s2: "ab" --> "ba".

Example 2:

Input: s1 = "abc", s2 = "bca"
Output: 2
Explanation: The two strings are 2-similar because we can use two swaps to change s1 to s2: "abc" --> "bac" --> "bca".

Constraints:

1 <= s1.length <= 20
s2.length == s1.length
s1 and s2 contain only lowercase letters from the set {'a', 'b', 'c', 'd', 'e', 'f'}.
s2 is an anagram of s1.