LeetCode #712 — MEDIUM

Minimum ASCII Delete Sum for Two Strings

Move from brute-force thinking to an efficient approach using dynamic programming strategy.

The Problem

Problem Statement

Given two strings s1 and s2, return the lowest ASCII sum of deleted characters to make two strings equal.

Example 1:

Input: s1 = "sea", s2 = "eat"
Output: 231
Explanation: Deleting "s" from "sea" adds the ASCII value of "s" (115) to the sum.
Deleting "t" from "eat" adds 116 to the sum.
At the end, both strings are equal, and 115 + 116 = 231 is the minimum sum possible to achieve this.

Example 2:

Input: s1 = "delete", s2 = "leet"
Output: 403
Explanation: Deleting "dee" from "delete" to turn the string into "let",
adds 100[d] + 101[e] + 101[e] to the sum.
Deleting "e" from "leet" adds 101[e] to the sum.
At the end, both strings are equal to "let", and the answer is 100+101+101+101 = 403.
If instead we turned both strings into "lee" or "eet", we would get answers of 433 or 417, which are higher.

Constraints:

1 <= s1.length, s2.length <= 1000
s1 and s2 consist of lowercase English letters.

Step 01

Brute Force Baseline

Problem summary: Given two strings s1 and s2, return the lowest ASCII sum of deleted characters to make two strings equal.

Baseline thinking

Start with the most direct exhaustive search. That gives a correctness anchor before optimizing.

Pattern signal: Dynamic Programming

Example 1

"sea"
"eat"

Example 2

"delete"
"leet"

Core Insight

What unlocks the optimal approach

Let dp(i, j) be the answer for inputs s1[i:] and s2[j:].

Interview move: turn each hint into an invariant you can check after every iteration/recursion step.

Step 03

Algorithm Walkthrough

Iteration Checklist

Define state (indices, window, stack, map, DP cell, or recursion frame).
Apply one transition step and update the invariant.
Record answer candidate when condition is met.
Continue until all input is consumed.

Use the first example testcase as your mental trace to verify each transition.

Step 04

Edge Cases

Minimum Input

Single element / shortest valid input

Validate boundary behavior before entering the main loop or recursion.

Duplicates & Repeats

Repeated values / repeated states

Decide whether duplicates should be merged, skipped, or counted explicitly.

Extreme Constraints

Upper-end input sizes

Re-check complexity target against constraints to avoid time-limit issues.

Invalid / Corner Shape

Empty collections, zeros, or disconnected structures

Handle special-case structure before the core algorithm path.

Step 05

Full Annotated Code

Source-backed implementations are provided below for direct study and interview prep.

// Accepted solution for LeetCode #712: Minimum ASCII Delete Sum for Two Strings
class Solution {
    public int minimumDeleteSum(String s1, String s2) {
        int m = s1.length(), n = s2.length();
        int[][] f = new int[m + 1][n + 1];
        for (int i = 1; i <= m; ++i) {
            f[i][0] = f[i - 1][0] + s1.charAt(i - 1);
        }
        for (int j = 1; j <= n; ++j) {
            f[0][j] = f[0][j - 1] + s2.charAt(j - 1);
        }
        for (int i = 1; i <= m; ++i) {
            for (int j = 1; j <= n; ++j) {
                if (s1.charAt(i - 1) == s2.charAt(j - 1)) {
                    f[i][j] = f[i - 1][j - 1];
                } else {
                    f[i][j]
                        = Math.min(f[i - 1][j] + s1.charAt(i - 1), f[i][j - 1] + s2.charAt(j - 1));
                }
            }
        }
        return f[m][n];
    }
}

// Accepted solution for LeetCode #712: Minimum ASCII Delete Sum for Two Strings
func minimumDeleteSum(s1 string, s2 string) int {
	m, n := len(s1), len(s2)
	f := make([][]int, m+1)
	for i := range f {
		f[i] = make([]int, n+1)
	}
	for i, c := range s1 {
		f[i+1][0] = f[i][0] + int(c)
	}
	for j, c := range s2 {
		f[0][j+1] = f[0][j] + int(c)
	}
	for i := 1; i <= m; i++ {
		for j := 1; j <= n; j++ {
			if s1[i-1] == s2[j-1] {
				f[i][j] = f[i-1][j-1]
			} else {
				f[i][j] = min(f[i-1][j]+int(s1[i-1]), f[i][j-1]+int(s2[j-1]))
			}
		}
	}
	return f[m][n]
}

# Accepted solution for LeetCode #712: Minimum ASCII Delete Sum for Two Strings
class Solution:
    def minimumDeleteSum(self, s1: str, s2: str) -> int:
        m, n = len(s1), len(s2)
        f = [[0] * (n + 1) for _ in range(m + 1)]
        for i in range(1, m + 1):
            f[i][0] = f[i - 1][0] + ord(s1[i - 1])
        for j in range(1, n + 1):
            f[0][j] = f[0][j - 1] + ord(s2[j - 1])
        for i in range(1, m + 1):
            for j in range(1, n + 1):
                if s1[i - 1] == s2[j - 1]:
                    f[i][j] = f[i - 1][j - 1]
                else:
                    f[i][j] = min(
                        f[i - 1][j] + ord(s1[i - 1]), f[i][j - 1] + ord(s2[j - 1])
                    )
        return f[m][n]

// Accepted solution for LeetCode #712: Minimum ASCII Delete Sum for Two Strings
impl Solution {
    pub fn minimum_delete_sum(s1: String, s2: String) -> i32 {
        let m: usize = s1.len();
        let n: usize = s2.len();
        let b1 = s1.as_bytes();
        let b2 = s2.as_bytes();

        let mut f: Vec<Vec<i32>> = vec![vec![0; n + 1]; m + 1];

        for i in 1..=m {
            f[i][0] = f[i - 1][0] + b1[i - 1] as i32;
        }
        for j in 1..=n {
            f[0][j] = f[0][j - 1] + b2[j - 1] as i32;
        }

        for i in 1..=m {
            for j in 1..=n {
                if b1[i - 1] == b2[j - 1] {
                    f[i][j] = f[i - 1][j - 1];
                } else {
                    f[i][j] = std::cmp::min(
                        f[i - 1][j] + b1[i - 1] as i32,
                        f[i][j - 1] + b2[j - 1] as i32,
                    );
                }
            }
        }

        f[m][n]
    }
}

// Accepted solution for LeetCode #712: Minimum ASCII Delete Sum for Two Strings
function minimumDeleteSum(s1: string, s2: string): number {
    const m = s1.length;
    const n = s2.length;
    const f = Array.from({ length: m + 1 }, () => Array(n + 1).fill(0));
    for (let i = 1; i <= m; ++i) {
        f[i][0] = f[i - 1][0] + s1[i - 1].charCodeAt(0);
    }
    for (let j = 1; j <= n; ++j) {
        f[0][j] = f[0][j - 1] + s2[j - 1].charCodeAt(0);
    }
    for (let i = 1; i <= m; ++i) {
        for (let j = 1; j <= n; ++j) {
            if (s1[i - 1] === s2[j - 1]) {
                f[i][j] = f[i - 1][j - 1];
            } else {
                f[i][j] = Math.min(
                    f[i - 1][j] + s1[i - 1].charCodeAt(0),
                    f[i][j - 1] + s2[j - 1].charCodeAt(0),
                );
            }
        }
    }
    return f[m][n];
}

Step 06

Interactive Study Demo

Use this to step through a reusable interview workflow for this problem.

Custom checkpoints (one per line)

Press Step or Run All to begin.

Step 07

Complexity Analysis

Time

O(m × n)

Space

O(m × n)

Approach Breakdown

RECURSIVE

O(2ⁿ) time

O(n) space

Pure recursion explores every possible choice at each step. With two choices per state (take or skip), the decision tree has 2ⁿ leaves. The recursion stack uses O(n) space. Many subproblems are recomputed exponentially many times.

DYNAMIC PROGRAMMING

O(n × m) time

O(n × m) space

Each cell in the DP table is computed exactly once from previously solved subproblems. The table dimensions determine both time and space. Look for the state variables — each unique combination of state values is one cell. Often a rolling array can reduce space by one dimension.

Shortcut: Count your DP state dimensions → that’s your time. Can you drop one? That’s your space optimization.

Coach Notes

Common Mistakes

Review these before coding to avoid predictable interview regressions.

State misses one required dimension

Wrong move: An incomplete state merges distinct subproblems and caches incorrect answers.

Usually fails on: Correctness breaks on cases that differ only in hidden state.

Fix: Define state so each unique subproblem maps to one DP cell.