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Posts tagged as “string”

花花酱 LeetCode 1190. Reverse Substrings Between Each Pair of Parentheses

Given a string s that consists of lower case English letters and brackets. 

Reverse the strings in each pair of matching parentheses, starting from the innermost one.

Your result should not contain any bracket.

Example 1:

Input: s = "(abcd)"
Output: "dcba"

Example 2:

Input: s = "(u(love)i)"
Output: "iloveu"

Example 3:

Input: s = "(ed(et(oc))el)"
Output: "leetcode"

Example 4:

Input: s = "a(bcdefghijkl(mno)p)q"
Output: "apmnolkjihgfedcbq"


  • 0 <= s.length <= 2000
  • s only contains lower case English characters and parentheses.
  • It’s guaranteed that all parentheses are balanced.

Solution: Stack

Use a stack of strings to track all the active strings.
Iterate over the input string:
1. Whenever there is a ‘(‘, push an empty string to the stack.
2. Whenever this is a ‘)’, pop the top string and append the reverse of it to the new stack top.
3. Otherwise, append the letter to the string on top the of stack.

Once done, the (only) string on the top of the stack is the answer.

Time complexity: O(n^2)
Space complexity: O(n)


花花酱 LeetCode 1189. Maximum Number of Balloons

Given a string text, you want to use the characters of text to form as many instances of the word “balloon” as possible.

You can use each character in text at most once. Return the maximum number of instances that can be formed.

Example 1:

Input: text = "nlaebolko"
Output: 1

Example 2:

Input: text = "loonbalxballpoon"
Output: 2

Example 3:

Input: text = "leetcode"
Output: 0


  • 1 <= text.length <= 10^4
  • text consists of lower case English letters only.

Solution: HashTable

Use a hashtable to count the occurrence of each letter and find the bottleneck one.

Time complexity: O(n)
Space complexity: O(1)


花花酱 LeetCode 1178. Number of Valid Words for Each Puzzle

With respect to a given puzzle string, a word is valid if both the following conditions are satisfied:

  • word contains the first letter of puzzle.
  • For each letter in word, that letter is in puzzle.
    For example, if the puzzle is “abcdefg”, then valid words are “faced”, “cabbage”, and “baggage”; while invalid words are “beefed” (doesn’t include “a”) and “based” (includes “s” which isn’t in the puzzle).

Return an array answer, where answer[i] is the number of words in the given word list words that are valid with respect to the puzzle puzzles[i].

Example :

words = ["aaaa","asas","able","ability","actt","actor","access"], 
puzzles = ["aboveyz","abrodyz","abslute","absoryz","actresz","gaswxyz"]
Output: [1,1,3,2,4,0]
1 valid word for "aboveyz" : "aaaa" 
1 valid word for "abrodyz" : "aaaa"
3 valid words for "abslute" : "aaaa", "asas", "able"
2 valid words for "absoryz" : "aaaa", "asas"
4 valid words for "actresz" : "aaaa", "asas", "actt", "access"
There're no valid words for "gaswxyz" cause none of the words in the list contains letter 'g'.


  • 1 <= words.length <= 10^5
  • 4 <= words[i].length <= 50
  • 1 <= puzzles.length <= 10^4
  • puzzles[i].length == 7
  • words[i][j]puzzles[i][j] are English lowercase letters.
  • Each puzzles[i] doesn’t contain repeated characters.

Solution: Subsets

Compress each word to a bit map, and compute the frequency of each bit map.
Since there are at most |words| bitmaps while its value ranging from 0 to 2^26, thus it’s better to use a hashtable instead of an array.

Use the same way to compress a puzzle into a bit map.
Try all subsets (at most 128) of the puzzle (the bit of the first character is be must), and check how many words match each subset.

words = [“aaaa”,”asas”,”able”,”ability”,”actt”,”actor”,”access”],
puzzle = “abslute”
bitmap(“aaaa”) = {0}
bitmap(“asas”) = {0, 18}
bitmap(“able”) = {0,1,4,11}
bitmap(“actt”) = {0, 2, 19}
bitmap(“actor”) = {0, 2, 14, 17, 19}
bitmap(“access”) = {0, 2, 4, 18}

bitmap(“abslute”) = {0, 1, 4, 11, 18, 19, 20}

Time complexity: O(sum(len(w_i)) + |puzzles|)
Space complexity: O(|words|)


花花酱 LeetCode 1143. Longest Common Subsequence

Given two strings text1 and text2, return the length of their longest common subsequence.

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.

Example 2:

Input: text1 = "abc", text2 = "abc"
Output: 3
Explanation: The longest common subsequence is "abc" and its length is 3.

Example 3:

Input: text1 = "abc", text2 = "def"
Output: 0
Explanation: There is no such common subsequence, so the result is 0.


  • 1 <= text1.length <= 1000
  • 1 <= text2.length <= 1000
  • The input strings consist of lowercase English characters only.

Solution: DP

Use dp[i][j] to represent the length of longest common sub-sequence of text1[0:i] and text2[0:j]
dp[i][j] = dp[i – 1][j – 1] + 1 if text1[i – 1] == text2[j – 1] else max(dp[i][j – 1], dp[i – 1][j])

Time complexity: O(mn)
Space complexity: O(mn) -> O(n)




花花酱 LeetCode 1160. Find Words That Can Be Formed by Characters

You are given an array of strings words and a string chars.

A string is good if it can be formed by characters from chars (each character can only be used once).

Return the sum of lengths of all good strings in words.

Example 1:

Input: words = ["cat","bt","hat","tree"], chars = "atach"
Output: 6
The strings that can be formed are "cat" and "hat" so the answer is 3 + 3 = 6.

Example 2:

Input: words = ["hello","world","leetcode"], chars = "welldonehoneyr"
Output: 10
The strings that can be formed are "hello" and "world" so the answer is 5 + 5 = 10.


  1. 1 <= words.length <= 1000
  2. 1 <= words[i].length, chars.length <= 100
  3. All strings contain lowercase English letters only.

Solution: Hashtable

Use a hashtable to store each letter’s frequency of the string and compare that with each word.

Time complexity: O(n + sum(len(word))
Space complexity: O(1)