What is the time complexity of Number of Strings Which Can Be Rearranged to Contain Substring?

The optimal solution for Number of Strings Which Can Be Rearranged to Contain Substring runs in O(n) time and uses O(1) space. The time complexity is O(n) due to the power calculation, which is efficient compared to generating all strings.

What is the brute force solution for Number of Strings Which Can Be Rearranged to Contain Substring?

The brute force approach for Number of Strings Which Can Be Rearranged to Contain Substring is Brute Force, with O(n²) time complexity and O(1) space complexity. In the brute force approach, we generate all possible strings of length n using lowercase letters and check if any can be rearranged to contain 'leet' as a substring. This is straightforward but inefficient for larger n. The optimal Optimal Solution approach improves this to O(n).

What algorithm or data structure is used to solve Number of Strings Which Can Be Rearranged to Contain Substring?

Number of Strings Which Can Be Rearranged to Contain Substring uses the following concepts: Math, Dynamic Programming, Combinatorics. The recommended patterns to study are: Combinatorial Counting, Dynamic Programming.

What are the interview tips for Number of Strings Which Can Be Rearranged to Contain Substring?

Always start with a brute force solution to understand the problem better. Look for patterns in the problem that can lead to combinatorial solutions. Practice calculating combinations and permutations, as they are common in string-related problems.

What are common mistakes when solving Number of Strings Which Can Be Rearranged to Contain Substring?

Students often try to brute force the solution without considering combinatorial methods. Failing to handle large numbers and modulo operations correctly.

#2930

Number of Strings Which Can Be Rearranged to Contain Substring

Medium

MathDynamic ProgrammingCombinatoricsCombinatorial CountingDynamic Programming

LeetCode ↗

Approaches

Brute ForceOptimal

Complexity Comparison

	Brute Force	Optimal Solution★
Time	O(n²)	O(n)
Space	O(1)	O(1)

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Intuition

Time O(n)Space O(1)

The optimal approach calculates the number of good strings directly using combinatorial counting. We focus on ensuring the string contains the required characters and then distribute the remaining characters freely.

⚙️

Algorithm

3 steps

1Step 1: Ensure the string contains at least one 'l', one 't', and two 'e's.
2Step 2: Calculate the number of remaining characters after placing 'leet'.
3Step 3: Use combinatorial formulas to calculate the arrangements of the remaining characters.

solution.py11 lines

1# Full working Python code
2MOD = 10**9 + 7
3
4def count_good_strings(n):
5    if n < 4:
6        return 0
7    # Calculate combinations
8    total = pow(26, n - 4, MOD)
9    arrangements = (n - 3) * (n - 2) * (n - 1) // 6
10    return (total * arrangements) % MOD
11

ℹ

Complexity note: The time complexity is O(n) due to the power calculation, which is efficient compared to generating all strings.

1A good string must contain at least one 'l', one 't', and two 'e's.
2Combinatorial counting allows us to efficiently calculate arrangements instead of generating all strings.

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