Given an input string s and a pattern p, implement regular expression matching with support for '.' and '*' where:
'.'Matches any single character.'*'Matches zero or more of the preceding element.
Return a boolean indicating whether the matching covers the entire input string (not partial).
Example 1:
Input: s = "aa", p = "a" Output: false Explanation: "a" does not match the entire string "aa".
Example 2:
Input: s = "aa", p = "a*" Output: true Explanation: '*' means zero or more of the preceding element, 'a'. Therefore, by repeating 'a' once, it becomes "aa".
Example 3:
Input: s = "ab", p = ".*" Output: true Explanation: ".*" means "zero or more (*) of any character (.)".
Constraints:
1 <= s.length <= 201 <= p.length <= 20scontains only lowercase English letters.pcontains only lowercase English letters,'.', and'*'.- It is guaranteed for each appearance of the character
'*', there will be a previous valid character to match.
Approach 01: Dynamic Programming (Bottom-Up)
class Solution {
public:
bool isMatch(string s, string p) {
int m = s.length(), n = p.length();
vector> dp(m + 1, vector(n + 1, false));
dp[0][0] = true;
for (int j = 2; j <= n; j++) {
if (p[j-1] == '*') dp[0][j] = dp[0][j-2];
}
for (int i = 1; i <= m; i++) {
for (int j = 1; j <= n; j++) {
if (p[j-1] == '.' || p[j-1] == s[i-1]) {
dp[i][j] = dp[i-1][j-1];
} else if (p[j-1] == '*') {
dp[i][j] = dp[i][j-2];
if (p[j-2] == '.' || p[j-2] == s[i-1]) {
dp[i][j] = dp[i][j] || dp[i-1][j];
}
}
}
}
return dp[m][n];
}
};
Time Complexity:
- O(m * n): We fill the DP table of size
(m+1) * (n+1).
Space Complexity:
- O(m * n): Space used for the DP table.
class Solution:
def isMatch(self, s: str, p: str) -> bool:
m, n = len(s), len(p)
dp = [[False] * (n + 1) for _ in range(m + 1)]
dp[0][0] = True
for j in range(2, n + 1):
if p[j-1] == '*':
dp[0][j] = dp[0][j-2]
for i in range(1, m + 1):
for j in range(1, n + 1):
if p[j-1] == '.' or p[j-1] == s[i-1]:
dp[i][j] = dp[i-1][j-1]
elif p[j-1] == '*':
dp[i][j] = dp[i][j-2]
if p[j-2] == '.' or p[j-2] == s[i-1]:
dp[i][j] |= dp[i-1][j]
return dp[m][n]
Time Complexity:
- O(m * n): We fill the DP table of size
(m+1) * (n+1).
Space Complexity:
- O(m * n): Space used for the DP table.
class Solution {
public boolean isMatch(String s, String p) {
int m = s.length(), n = p.length();
boolean[][] dp = new boolean[m + 1][n + 1];
dp[0][0] = true;
for (int j = 2; j <= n; j++) {
if (p.charAt(j-1) == '*') dp[0][j] = dp[0][j-2];
}
for (int i = 1; i <= m; i++) {
for (int j = 1; j <= n; j++) {
if (p.charAt(j-1) == '.' || p.charAt(j-1) == s.charAt(i-1)) {
dp[i][j] = dp[i-1][j-1];
} else if (p.charAt(j-1) == '*') {
dp[i][j] = dp[i][j-2];
if (p.charAt(j-2) == '.' || p.charAt(j-2) == s.charAt(i-1)) {
dp[i][j] = dp[i][j] || dp[i-1][j];
}
}
}
}
return dp[m][n];
}
}
Time Complexity:
- O(m * n): We fill the DP table of size
(m+1) * (n+1).
Space Complexity:
- O(m * n): Space used for the DP table.