1143. Longest Common Subsequence
package LeetCode_1143 /** * 1143. Longest Common Subsequence * https://leetcode.com/problems/longest-common-subsequence/description/ * * Given two strings text1 and text2, return the length of their longest common subsequence. 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. (eg, "ace" is a subsequence of "abcde" while "aec" is not). A common subsequence of two strings is a subsequence that is common to both strings. If there is no common subsequence, return 0. Example 1: Input: text1 = "abcde", text2 = "ace" Output: 3 Explanation: The longest common subsequence is "ace" and its length is 3. Constraints: 1 <= text1.length <= 1000 1 <= text2.length <= 1000 The input strings consist of lowercase English characters only. * */ class Solution { fun longestCommonSubsequence(text1: String, text2: String): Int { val m = text1.length val n = text2.length return dp(text1, text2) } /** * solution 1:dfs, Time complexity:O(2^n), Space complexity:O(n*m) * TLE * */ private fun dfs(str1: String, str2: String, m: Int, n: Int): Int { if (m == 0 || n == 0) { return 0 } if (str1[m - 1] == str2[n - 1]) { return 1 + dfs(str1, str2, m - 1, n - 1) } else { return Math.max(dfs(str1, str2, m - 1, n), dfs(str1, str2, m, n - 1)) } } /** * solution 2:dynamic programing (dp) * Time complexity:O(m*n), Space complexity:O(m*n) * */ private fun dp(text1: String, text2: String): Int { val m = text1.length val n = text2.length val dp = Array(m + 1) { IntArray(n + 1) } //fill first row for (col in 0 until n) { //because we set the element in first row and first cols is "" //so with "", the common length is 0, the same as below dp[row][0] dp[0][col] = 0 } //fill first col for (row in 0 until m) { //because we set the element in first row and first cols is "" dp[row][0] = 0 } for (i in 1..m) { for (j in 1..n) { if (text1[i - 1] == text2[j - 1]) { dp[i][j] = 1 + dp[i - 1][j - 1] } else { dp[i][j] = Math.max(dp[i - 1][j], dp[i][j - 1]) } } } return dp[m][n] } }