1163. Last Substring in Lexicographical Order
Description
Given a string s, return the last substring of s in lexicographical order.
Example 1:
Input: s = "abab" Output: "bab" Explanation: The substrings are ["a", "ab", "aba", "abab", "b", "ba", "bab"]. The lexicographically maximum substring is "bab".
Example 2:
Input: s = "leetcode" Output: "tcode"
Constraints:
1 <= s.length <= 4 * 105scontains only lowercase English letters.
Solutions
Solution 1: Two pointers
We notice that if a substring starts from position $i$, then the largest substring with the largest dictionary order must be $s[i,..n-1]$, which is the longest suffix starting from position $i$. Therefore, we only need to find the largest suffix substring.
We use two pointers $i$ and $j$, where pointer $i$ points to the starting position of the current largest substring with the largest dictionary order, and pointer $j$ points to the starting position of the current substring being considered. In addition, we use a variable $k$ to record the current position being compared. Initially, $i = 0$, $j=1$, $k=0$.
Each time, we compare $s[i+k]$ and $s[j+k]$:
If $s[i + k] = s[j + k]$, it means that $s[i,..i+k]$ and $s[j,..j+k]$ are the same, and we add $k$ by $1$ and continue to compare $s[i+k]$ and $s[j+k]$;
If $s[i + k] \lt s[j + k]$, it means that the dictionary order of $s[j,..j+k]$ is larger. At this time, we update $i = i + k + 1$, and reset $k$ to $0$. If $i \geq j$ at this time, we update pointer $j$ to $i + 1$, that is, $j = i + 1$. Here we skip all suffix substrings with $s[i,..,i+k]$ as the starting position, because their dictionary orders are smaller than the suffix substrings with $s[j,..,j+k]$ as the starting position.
Similarly, if $s[i + k] \gt s[j + k]$, it means that the dictionary order of $s[i,..,i+k]$ is larger. At this time, we update $j = j + k + 1$ and reset $k$ to $0$. Here we skip all suffix substrings with $s[j,..,j+k]$ as the starting position, because their dictionary orders are smaller than the suffix substrings with $s[i,..,i+k]$ as the starting position.
Finally, we return the suffix substring starting from $i$, that is, $s[i,..,n-1]$.
The time complexity is $O(n)$, where $n$ is the length of string $s$. The space complexity is $O(1)$.
Python3
class Solution:
def lastSubstring(self, s: str) -> str:
i, j, k = 0, 1, 0
while j + k < len(s):
if s[i + k] == s[j + k]:
k += 1
elif s[i + k] < s[j + k]:
i += k + 1
k = 0
if i >= j:
j = i + 1
else:
j += k + 1
k = 0
return s[i:]
Java
class Solution {
public String lastSubstring(String s) {
int n = s.length();
int i = 0;
for (int j = 1, k = 0; j + k < n;) {
int d = s.charAt(i + k) - s.charAt(j + k);
if (d == 0) {
++k;
} else if (d < 0) {
i += k + 1;
k = 0;
if (i >= j) {
j = i + 1;
}
} else {
j += k + 1;
k = 0;
}
}
return s.substring(i);
}
}
C++
class Solution {
public:
string lastSubstring(string s) {
int n = s.size();
int i = 0;
for (int j = 1, k = 0; j + k < n;) {
if (s[i + k] == s[j + k]) {
++k;
} else if (s[i + k] < s[j + k]) {
i += k + 1;
k = 0;
if (i >= j) {
j = i + 1;
}
} else {
j += k + 1;
k = 0;
}
}
return s.substr(i);
}
};
Go
func lastSubstring(s string) string {
i, n := 0, len(s)
for j, k := 1, 0; j+k < n; {
if s[i+k] == s[j+k] {
k++
} else if s[i+k] < s[j+k] {
i += k + 1
k = 0
if i >= j {
j = i + 1
}
} else {
j += k + 1
k = 0
}
}
return s[i:]
}
TypeScript
function lastSubstring(s: string): string {
const n = s.length;
let i = 0;
for (let j = 1, k = 0; j + k < n; ) {
if (s[i + k] === s[j + k]) {
++k;
} else if (s[i + k] < s[j + k]) {
i += k + 1;
k = 0;
if (i >= j) {
j = i + 1;
}
} else {
j += k + 1;
k = 0;
}
}
return s.slice(i);
}