两个求解目标类似的题目,对比记忆!
5.最长回文子串
Python
class Solution:
def longestPalindrome(self, s: str) -> str:
n = len(s)
start = end = 0
dp = [[False] * n for _ in range(n)]
for i in range(0, n):
dp[i][i] = True
for i in range(n-1, -1, -1): # 从n-1到0
for j in range(i+1, n):
dp[i][j] = (j-i == 1 or dp[i+1][j-1]) and (s[i] == s[j])
if dp[i][j] and (j - i > end - start):
start = i
end = j
return s[start: end+1]
Java
法1:二维DP
最基础方法!必须掌握!
O(N^2) + O(N^2)
class Solution {
public String longestPalindrome(String s) {
int n = s.length();
if (n == 1) {
return s;
}
String res = s.substring(0, 1);
boolean[][] dp = new boolean[n][n];
for (int i = 0; i < n; ++i) {
dp[i][i] = true;
}
for (int i = n - 1; i >= 0; --i) {
for (int j = i + 1; j < n; ++j) {
dp[i][j] = (j - i == 1 || dp[i + 1][j - 1]) && (s.charAt(i) == s.charAt(j));
if (dp[i][j] && (j - i + 1 > res.length())) {
res = s.substring(i, j + 1);
}
}
}
return res;
}
}
法2:中心扩展法
O(N^2) + O(1)
class Solution {
public String longestPalindrome(String s) {
if (s.length() < 2) {
return s;
}
String res = "";
for (int i = 0; i < s.length(); ++i) {
String res1 = palindrome(s, i, i);
String res2 = palindrome(s, i, i + 1);
res = res1.length() > res.length() ? res1 : res;
res = res2.length() > res.length() ? res2 : res;
}
return res;
}
public String palindrome(String s, int i, int j) {
while (i >= 0 && j < s.length() && (s.charAt(i) == s.charAt(j))) {
--i;
++j;
}
return s.substring(i + 1, j);
}
}
516.最长回文子序列
Python
class Solution:
def longestPalindromeSubseq(self, s: str) -> int:
n = len(s)
dp = [[0] * n for _ in range(n)]
for i in range(n):
dp[i][i] = 1
for i in range(n-1, -1, -1):
for j in range(i+1, n):
if s[i] == s[j]:
dp[i][j] = (0 if j - i == 0 else dp[i+1][j-1]) + 2
else:
dp[i][j] = max(dp[i+1][j], dp[i][j-1])
return dp[0][n-1]
Java
class Solution {
public int longestPalindromeSubseq(String s) {
int n = s.length();
int[][] dp = new int[n][n];
for (int i = 0; i < n; ++i) {
dp[i][i] = 1;
}
for (int i = n - 1; i >= 0; --i) {
for (int j = i + 1; j < n; ++j) {
if (s.charAt(i) == s.charAt(j)) {
dp[i][j] = (j - i == 1 ? 0 : dp[i + 1][j - 1]) + 2;
} else {
dp[i][j] = Math.max(dp[i + 1][j], dp[i][j - 1]);
}
}
}
return dp[0][n - 1];
}
}