How do you solve Find the Original Typed String II on LeetCode?

Find the Original Typed String II (LeetCode #3333) can be solved using Brute Force or Optimal Solution. The optimal approach is Optimal Solution with O(n * m) time complexity and O(n) space complexity. Key insight: Understanding how to count combinations is crucial for solving problems involving repeated characters.

What is the brute force solution for Find the Original Typed String II?

The brute force approach for Find the Original Typed String II is Brute Force, with O(n²) time complexity and O(1) space complexity. The brute force approach involves generating all possible original strings by considering every possible way to reduce the repeated characters in the given string. This method is straightforward but inefficient for larger inputs. The optimal Optimal Solution approach improves this to O(n * m).

What algorithm or data structure is used to solve Find the Original Typed String II?

Find the Original Typed String II uses the following concepts: String, Dynamic Programming, Prefix Sum. The recommended patterns to study are: Dynamic Programming, Combinatorial Counting.

What are the interview tips for Find the Original Typed String II?

Always clarify the problem constraints and edge cases before diving into coding. Think about how to optimize your solution from the start; consider dynamic programming for counting problems. Practice explaining your thought process clearly, as communication is key in interviews.

What are common mistakes when solving Find the Original Typed String II?

Failing to consider edge cases where k is larger than the length of the string. Not using modular arithmetic correctly, leading to overflow errors.

#3333

Find the Original Typed String II

Hard

StringDynamic ProgrammingPrefix SumDynamic ProgrammingCombinatorial Counting

LeetCode ↗

Approaches

Brute ForceOptimal

Complexity Comparison

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

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Intuition

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

The optimal approach uses dynamic programming to count the number of valid original strings without generating them explicitly. We focus on counting combinations of characters that can be formed while ensuring the length constraint is satisfied.

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Algorithm

3 steps

1Step 1: Count the frequency of each character in the input string.
2Step 2: Use dynamic programming to calculate the number of ways to form strings of lengths from 0 to the total length of the string.
3Step 3: Subtract the count of strings of length less than k from the total count to get the valid original strings.

solution.py13 lines

1def count_original_strings(word, k):
2    from collections import Counter
3    MOD = 10**9 + 7
4    freq = Counter(word)
5    total_length = sum(freq.values())
6    dp = [0] * (total_length + 1)
7    dp[0] = 1
8    for count in freq.values():
9        for i in range(total_length, 0, -1):
10            for j in range(1, count + 1):
11                if i - j >= 0:
12                    dp[i] = (dp[i] + dp[i - j]) % MOD
13    return sum(dp[k:]) % MOD

ℹ

Complexity note: The complexity is O(n * m) where n is the length of the string and m is the number of unique characters, as we iterate through the counts of each character to build the dynamic programming table.

1Understanding how to count combinations is crucial for solving problems involving repeated characters.
2Dynamic programming can significantly reduce the complexity of counting problems by avoiding explicit generation of all combinations.

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