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English Version

题目描述

给定两个字符串 text1 和 text2,返回这两个字符串的最长 公共子序列 的长度。如果不存在 公共子序列 ,返回 0

一个字符串的 子序列 是指这样一个新的字符串:它是由原字符串在不改变字符的相对顺序的情况下删除某些字符(也可以不删除任何字符)后组成的新字符串。

  • 例如,"ace""abcde" 的子序列,但 "aec" 不是 "abcde" 的子序列。

两个字符串的 公共子序列 是这两个字符串所共同拥有的子序列。

 

示例 1:

输入:text1 = "abcde", text2 = "ace" 
输出:3  
解释:最长公共子序列是 "ace" ,它的长度为 3 。

示例 2:

输入:text1 = "abc", text2 = "abc"
输出:3
解释:最长公共子序列是 "abc" ,它的长度为 3 。

示例 3:

输入:text1 = "abc", text2 = "def"
输出:0
解释:两个字符串没有公共子序列,返回 0 。

 

提示:

  • 1 <= text1.length, text2.length <= 1000
  • text1 和 text2 仅由小写英文字符组成。

解法

方法一:动态规划

定义 dp[i][j] 表示 text1[0:i-1]text2[0:j-1] 的最长公共子序列(闭区间)。

递推公式如下:

时间复杂度:$O(mn)$。

Python3

class Solution:
    def longestCommonSubsequence(self, text1: str, text2: str) -> int:
        m, n = len(text1), len(text2)
        dp = [[0] * (n + 1) for _ in range(m + 1)]
        for i in range(1, m + 1):
            for j in range(1, n + 1):
                if text1[i - 1] == text2[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[-1][-1]

Java

class Solution {
    public int longestCommonSubsequence(String text1, String text2) {
        int m = text1.length(), n = text2.length();
        int[][] dp = new int[m + 1][n + 1];
        for (int i = 1; i <= m; ++i) {
            for (int j = 1; j <= n; ++j) {
                if (text1.charAt(i - 1) == text2.charAt(j - 1)) {
                    dp[i][j] = dp[i - 1][j - 1] + 1;
                } else {
                    dp[i][j] = Math.max(dp[i - 1][j], dp[i][j - 1]);
                }
            }
        }
        return dp[m][n];
    }
}

C++

class Solution {
public:
    int longestCommonSubsequence(string text1, string text2) {
        int m = text1.size(), n = text2.size();
        vector<vector<int>> dp(m + 1, vector<int>(n + 1));
        for (int i = 1; i <= m; ++i) {
            for (int j = 1; j <= n; ++j) {
                if (text1[i - 1] == text2[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];
    }
};

Go

func longestCommonSubsequence(text1 string, text2 string) int {
	m, n := len(text1), len(text2)
	dp := make([][]int, m+1)
	for i := 0; i <= m; i++ {
		dp[i] = make([]int, n+1)
	}
	for i := 1; i <= m; i++ {
		for j := 1; j <= n; j++ {
			if text1[i-1] == text2[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]
}

func max(a, b int) int {
	if a > b {
		return a
	}
	return b
}

JavaScript

/**
 * @param {string} text1
 * @param {string} text2
 * @return {number}
 */
var longestCommonSubsequence = function (text1, text2) {
    const m = text1.length;
    const n = text2.length;
    const dp = new Array(m + 1).fill(0).map(() => new Array(n + 1).fill(0));
    for (let i = 1; i <= m; ++i) {
        for (let j = 1; j <= n; ++j) {
            if (text1[i - 1] == text2[j - 1]) {
                dp[i][j] = dp[i - 1][j - 1] + 1;
            } else {
                dp[i][j] = Math.max(dp[i - 1][j], dp[i][j - 1]);
            }
        }
    }
    return dp[m][n];
};

TypeScript

function longestCommonSubsequence(text1: string, text2: string): number {
    const m = text1.length;
    const n = text2.length;
    const dp = Array.from({ length: m + 1 }, () => Array(n + 1).fill(0));
    for (let i = 1; i <= m; i++) {
        for (let j = 1; j <= n; j++) {
            if (text1[i - 1] === text2[j - 1]) {
                dp[i][j] = dp[i - 1][j - 1] + 1;
            } else {
                dp[i][j] = Math.max(dp[i - 1][j], dp[i][j - 1]);
            }
        }
    }
    return dp[m][n];
}

Rust

impl Solution {
    pub fn longest_common_subsequence(text1: String, text2: String) -> i32 {
        let (m, n) = (text1.len(), text2.len());
        let (text1, text2) = (text1.as_bytes(), text2.as_bytes());
        let mut dp = vec![vec![0; n + 1]; m + 1];
        for i in 1..=m {
            for j in 1..=n {
                dp[i][j] = if text1[i - 1] == text2[j - 1] {
                    dp[i - 1][j - 1] + 1
                } else {
                    dp[i - 1][j].max(dp[i][j - 1])
                }
            }
        }
        dp[m][n]
    }
}

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