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动态规划（Dynamic Programming, DP）在字符串数组相关的算法题中应用广泛，尤其是在解决子序列、子串、编辑距离、匹配等问题时。动态规划的核心思想是将问题分解为子问题，并通过存储子问题的解来避免重复计算，从而提高效率。

1143. Longest Common Subsequence

Given two strings text1 and text2, return the length of their longest common subsequence. If there is no common subsequence, return 0.

A subsequence of a string is a new string generated from the original string with some characters (can be none) deleted without changing the relative order of the remaining characters.

For example, "ace" is a subsequence of "abcde".
A common subsequence of two strings is a subsequence that is common to both strings.

Example 1:

Input: text1 = "abcde", text2 = "ace" 
Output: 3  
Explanation: The longest common subsequence is "ace" and its length is 3.

Example 2:

Input: text1 = "abc", text2 = "abc"
Output: 3
Explanation: The longest common subsequence is "abc" and its length is 3.

Example 3:

Input: text1 = "abc", text2 = "def"
Output: 0
Explanation: There is no such common subsequence, so the result is 0.

问题描述

给定两个字符串 s1 和 s2，找到它们的最长公共子序列的长度。子序列是指从原字符串中删除一些字符（可以不连续）后得到的新字符串。

解题思路

• 定义状态：dp[i][j] 表示 s1 的前 i 个字符和 s2 的前 j 个字符的最长公共子序列长度。
• 状态转移：
  • 如果 s1[i-1] == s2[j-1]，则 dp[i][j] = dp[i-1][j-1] + 1。
  • 否则，dp[i][j] = max(dp[i-1][j], dp[i][j-1])。
• 初始化：dp[0][j] = 0 和 dp[i][0] = 0，表示空字符串的 LCS 长度为 0。
• 结果：dp[m][n]，其中 m 和 n 分别是 s1 和 s2 的长度。

C++ 实现

int longestCommonSubsequence(string s1, string s2) {int m = s1.length(), n = s2.length();vector<vector<int>> dp(m + 1, vector<int>(n + 1, 0));for (int i = 1; i <= m; i++) {for (int j = 1; j <= n; j++) {if (s1[i - 1] == s2[j - 1]) {dp[i][j] = dp[i - 1][j - 1] + 1;} else {dp[i][j] = max(dp[i - 1][j], dp[i][j - 1]);}}}return dp[m][n];
}

1092. Shortest Common Supersequence

Given two strings str1 and str2, return the shortest string that has both str1 and str2 as subsequences. If there are multiple valid strings, return any of them.

A string s is a subsequence of string t if deleting some number of characters from t (possibly 0) results in the string s.

Example 1:

Input: str1 = "abac", str2 = "cab"
Output: "cabac"
Explanation: 
str1 = "abac" is a subsequence of "cabac" because we can delete the first "c".
str2 = "cab" is a subsequence of "cabac" because we can delete the last "ac".
The answer provided is the shortest such string that satisfies these properties.

Example 2:

Input: str1 = "aaaaaaaa", str2 = "aaaaaaaa"
Output: "aaaaaaaa"

解题思路

这个问题可以转化为求两个字符串的最长公共子序列（LCS），然后通过 LCS 构造最短的公共超序列。

构造最短公共超序列

• 初始化指针：
  • 使用指针 i 和 j 分别遍历 str1 和 str2。
• 遍历字符串：
  • 如果 str1[i] == str2[j]，则将当前字符添加到结果中，并移动两个指针。
  • 否则，将 str1[i] 或 str2[j] 中较小的字符添加到结果中，并移动对应的指针。
• 处理剩余字符：
  • 如果 str1 或 str2 有剩余字符，将它们全部添加到结果中。

C++ 实现

string shortestCommonSupersequence(string str1, string str2) {int m = str1.length(), n = str2.length();// 动态规划求 LCSvector<vector<int>> dp(m + 1, vector<int>(n + 1, 0));for (int i = 1; i <= m; i++) {for (int j = 1; j <= n; j++) {if (str1[i - 1] == str2[j - 1]) {dp[i][j] = dp[i - 1][j - 1] + 1;} else {dp[i][j] = max(dp[i - 1][j], dp[i][j - 1]);}}}// 构造最短公共超序列string result;int i = m, j = n;while (i > 0 && j > 0) {if (str1[i - 1] == str2[j - 1]) {result.push_back(str1[i - 1]);i--;j--;} else if (dp[i - 1][j] > dp[i][j - 1]) {result.push_back(str1[i - 1]);i--;} else {result.push_back(str2[j - 1]);j--;}}// 处理剩余字符while (i > 0) {result.push_back(str1[i - 1]);i--;}while (j > 0) {result.push_back(str2[j - 1]);j--;}// 反转结果reverse(result.begin(), result.end());return result;
}