How do you solve String Transformation on LeetCode?

String Transformation (LeetCode #2851) 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: The string t can only be formed from s if it is a rotation of s.

What is the time complexity of String Transformation?

The optimal solution for String Transformation runs in O(n) time and uses O(n) space. The time complexity is O(n) due to the KMP algorithm, which efficiently finds matches in linear time.

What is the brute force solution for String Transformation?

The brute force approach for String Transformation is Brute Force, with O(n²) time complexity and O(1) space complexity. We can generate all possible rotations of string s and check if any of them match string t after k operations. This approach is straightforward but inefficient. The optimal Optimal Solution approach improves this to O(n).

What algorithm or data structure is used to solve String Transformation?

String Transformation uses the following concepts: Math, String, Dynamic Programming, String Matching. The recommended patterns to study are: KMP algorithm for string matching, String rotation and manipulation.

What are the interview tips for String Transformation?

Always clarify the constraints and edge cases before diving into coding. Discuss your thought process and reasoning behind each step of your approach. Practice explaining your code as you write it, as if you are teaching someone else.

What are common mistakes when solving String Transformation?

Not considering the modulo operation when returning the count. Failing to check if k is a multiple of the string length.

#2851

String Transformation

Hard

MathStringDynamic ProgrammingString MatchingKMP algorithm for string matchingString rotation and manipulation

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)

We can use the KMP algorithm or Z algorithm to find all valid rotations of s that match t. This allows us to efficiently count the matches and determine how many can be achieved in k operations.

⚙️

Algorithm

3 steps

1Step 1: Use the KMP algorithm to find all starting indices where s matches t.
2Step 2: Count how many of these indices can be reached in exactly k operations.
3Step 3: Return the count modulo 10^9 + 7.

solution.py33 lines

1def kmp_table(pattern):
2    m = len(pattern)
3    lps = [0] * m
4    length = 0
5    i = 1
6    while i < m:
7        if pattern[i] == pattern[length]:
8            length += 1
9            lps[i] = length
10            i += 1
11        else:
12            if length != 0:
13                length = lps[length - 1]
14            else:
15                lps[i] = 0
16                i += 1
17    return lps
18
19def countTransformations(s, t, k):
20    if len(s) != len(t): return 0
21    s = s + s
22    lps = kmp_table(t)
23    count = 0
24    j = 0
25    for i in range(len(s)):
26        while j > 0 and s[i] != t[j]:
27            j = lps[j - 1]
28        if s[i] == t[j]:
29            j += 1
30        if j == len(t):
31            count += 1
32            j = lps[j - 1]
33    return count if k % len(s) == 0 else 0

ℹ

Complexity note: The time complexity is O(n) due to the KMP algorithm, which efficiently finds matches in linear time.

1The string t can only be formed from s if it is a rotation of s.
2The number of valid transformations is determined by the number of valid rotations that match t.

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