How do you solve Minimum ASCII Delete Sum for Two Strings on LeetCode?

Minimum ASCII Delete Sum for Two Strings (LeetCode #712) can be solved using Brute Force or Optimal Solution. The optimal approach is Optimal Solution with O(m * n) time complexity and O(m * n) space complexity. Key insight: Dynamic programming allows us to break down the problem into smaller subproblems, making it easier to solve.

What is the brute force solution for Minimum ASCII Delete Sum for Two Strings?

The brute force approach for Minimum ASCII Delete Sum for Two Strings is Brute Force, with O(n²) time complexity and O(1) space complexity. The brute-force approach involves generating all possible ways to delete characters from both strings to make them equal. This means checking every combination of deletions, which can be inefficient but is straightforward to understand. The optimal Optimal Solution approach improves this to O(m * n).

What algorithm or data structure is used to solve Minimum ASCII Delete Sum for Two Strings?

Minimum ASCII Delete Sum for Two Strings uses the following concepts: String, Dynamic Programming. The recommended patterns to study are: Dynamic Programming, String Manipulation.

What are the interview tips for Minimum ASCII Delete Sum for Two Strings?

Always explain your thought process while coding, as it helps the interviewer understand your approach. Consider edge cases, such as empty strings, and how they affect your solution. Practice writing clean and efficient code, as readability is important in interviews.

What are common mistakes when solving Minimum ASCII Delete Sum for Two Strings?

Forgetting to initialize the dp array correctly, which can lead to incorrect results. Not considering the case where characters match, which can lead to unnecessary deletions.

#712

Minimum ASCII Delete Sum for Two Strings

Medium

StringDynamic ProgrammingDynamic ProgrammingString Manipulation

LeetCode ↗

Approaches

Brute ForceOptimal

Complexity Comparison

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

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Intuition

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

The optimal approach uses dynamic programming to build a solution incrementally. We create a table that stores the minimum ASCII delete sum for substrings of s1 and s2, allowing us to avoid redundant calculations.

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Algorithm

3 steps

1Step 1: Initialize a 2D array dp where dp[i][j] represents the minimum ASCII delete sum for s1[0:i] and s2[0:j].
2Step 2: Fill the first row and first column of the dp array based on the ASCII values of characters in s1 and s2.
3Step 3: Iterate through the rest of the dp array, filling it based on whether the characters match or not, using previously computed values.

solution.py15 lines

1# Full working Python code
2def minimumDeleteSum(s1, s2):
3    m, n = len(s1), len(s2)
4    dp = [[0] * (n + 1) for _ in range(m + 1)]
5    for i in range(1, m + 1):
6        dp[i][0] = dp[i - 1][0] + ord(s1[i - 1])
7    for j in range(1, n + 1):
8        dp[0][j] = dp[0][j - 1] + ord(s2[j - 1])
9    for i in range(1, m + 1):
10        for j in range(1, n + 1):
11            if s1[i - 1] == s2[j - 1]:
12                dp[i][j] = dp[i - 1][j - 1]
13            else:
14                dp[i][j] = min(dp[i - 1][j] + ord(s1[i - 1]), dp[i][j - 1] + ord(s2[j - 1]))
15    return dp[m][n]

ℹ

Complexity note: The time complexity is O(m * n) because we fill a 2D table of size m x n, where m and n are the lengths of the two strings. The space complexity is also O(m * n) due to the storage of this table.

1Dynamic programming allows us to break down the problem into smaller subproblems, making it easier to solve.
2Understanding the relationship between the characters of the two strings helps in optimizing the solution.

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