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Posts tagged as “hard”

花花酱 LeetCode 2267. Check if There Is a Valid Parentheses String Path

A parentheses string is a non-empty string consisting only of '(' and ')'. It is valid if any of the following conditions is true:

  • It is ().
  • It can be written as AB (A concatenated with B), where A and B are valid parentheses strings.
  • It can be written as (A), where A is a valid parentheses string.

You are given an m x n matrix of parentheses grid. A valid parentheses string path in the grid is a path satisfying all of the following conditions:

  • The path starts from the upper left cell (0, 0).
  • The path ends at the bottom-right cell (m - 1, n - 1).
  • The path only ever moves down or right.
  • The resulting parentheses string formed by the path is valid.

Return true if there exists a valid parentheses string path in the grid. Otherwise, return false.

Example 1:

Input: grid = [["(","(","("],[")","(",")"],["(","(",")"],["(","(",")"]]
Output: true
Explanation: The above diagram shows two possible paths that form valid parentheses strings.
The first path shown results in the valid parentheses string "()(())".
The second path shown results in the valid parentheses string "((()))".
Note that there may be other valid parentheses string paths.

Example 2:

Input: grid = [[")",")"],["(","("]]
Output: false
Explanation: The two possible paths form the parentheses strings "))(" and ")((". Since neither of them are valid parentheses strings, we return false.


  • m == grid.length
  • n == grid[i].length
  • 1 <= m, n <= 100
  • grid[i][j] is either '(' or ')'.

Solution: DP

Let dp(i, j, b) denote whether there is a path from (i,j) to (m-1, n-1) given b open parentheses.
if we are at (m – 1, n – 1) and b == 0 then we found a valid path.
dp(i, j, b) = dp(i + 1, j, b’) or dp(i, j + 1, b’) where b’ = b + 1 if grid[i][j] == ‘(‘ else -1

Time complexity: O(m*n*(m + n))
Space complexity: O(m*n*(m + n))


花花酱 LeetCode 2251. Number of Flowers in Full Bloom

You are given a 0-indexed 2D integer array flowers, where flowers[i] = [starti, endi] means the ith flower will be in full bloom from starti to endi (inclusive). You are also given a 0-indexed integer array persons of size n, where persons[i] is the time that the ith person will arrive to see the flowers.

Return an integer array answer of size n, where answer[i] is the number of flowers that are in full bloom when the ith person arrives.

Example 1:

Input: flowers = [[1,6],[3,7],[9,12],[4,13]], persons = [2,3,7,11]
Output: [1,2,2,2]
Explanation: The figure above shows the times when the flowers are in full bloom and when the people arrive.
For each person, we return the number of flowers in full bloom during their arrival.

Example 2:

Input: flowers = [[1,10],[3,3]], persons = [3,3,2]
Output: [2,2,1]
Explanation: The figure above shows the times when the flowers are in full bloom and when the people arrive.
For each person, we return the number of flowers in full bloom during their arrival.


  • 1 <= flowers.length <= 5 * 104
  • flowers[i].length == 2
  • 1 <= starti <= endi <= 109
  • 1 <= persons.length <= 5 * 104
  • 1 <= persons[i] <= 109

Solution: Prefix Sum + Binary Search

Use a treemap to store the counts (ordered by time t), when a flower begins to bloom at start, we increase m[start], when it dies at end, we decrease m[end+1]. prefix_sum[t] indicates the # of blooming flowers at time t.

For each people, use binary search to find the latest # of flowers before his arrival.

Time complexity: O(nlogn + mlogn)
Space complexity: O(n)


花花酱 LeetCode 2242. Maximum Score of a Node Sequence

There is an undirected graph with n nodes, numbered from 0 to n - 1.

You are given a 0-indexed integer array scores of length n where scores[i] denotes the score of node i. You are also given a 2D integer array edges where edges[i] = [ai, bi] denotes that there exists an undirected edge connecting nodes ai and bi.

A node sequence is valid if it meets the following conditions:

  • There is an edge connecting every pair of adjacent nodes in the sequence.
  • No node appears more than once in the sequence.

The score of a node sequence is defined as the sum of the scores of the nodes in the sequence.

Return the maximum score of a valid node sequence with a length of 4If no such sequence exists, return -1.

Example 1:

Input: scores = [5,2,9,8,4], edges = [[0,1],[1,2],[2,3],[0,2],[1,3],[2,4]]
Output: 24
Explanation: The figure above shows the graph and the chosen node sequence [0,1,2,3].
The score of the node sequence is 5 + 2 + 9 + 8 = 24.
It can be shown that no other node sequence has a score of more than 24.
Note that the sequences [3,1,2,0] and [1,0,2,3] are also valid and have a score of 24.
The sequence [0,3,2,4] is not valid since no edge connects nodes 0 and 3.

Example 2:

Input: scores = [9,20,6,4,11,12], edges = [[0,3],[5,3],[2,4],[1,3]]
Output: -1
Explanation: The figure above shows the graph.
There are no valid node sequences of length 4, so we return -1.


  • n == scores.length
  • 4 <= n <= 5 * 104
  • 1 <= scores[i] <= 108
  • 0 <= edges.length <= 5 * 104
  • edges[i].length == 2
  • 0 <= ai, bi <= n - 1
  • ai != bi
  • There are no duplicate edges.

Solution: Greedy / Top3 neighbors

Since |E| is already 5*104, we can’t enumerate all possible sequences. We must do in O(|E|) or O(|E|log|E|).

Enumerate all the edges, we have a pair of node a, b. To get the optimal answer, we just need to find the largest neighbor of a and b, which we call c, d respectively. Just need to make sure a, b, c, d are unique. i.e. c != d, c != b and d != a. Since the a’s largest neighbor can be either b or d. We can’t just store the largest neighbor, but top 3 instead for each node to avoid duplications.

Time complexity: O(|E|*9)
Space complexity: O(|V|*3)


花花酱 LeetCode 2227. Encrypt and Decrypt Strings

You are given a character array keys containing unique characters and a string array values containing strings of length 2. You are also given another string array dictionary that contains all permitted original strings after decryption. You should implement a data structure that can encrypt or decrypt a 0-indexed string.

A string is encrypted with the following process:

  1. For each character c in the string, we find the index i satisfying keys[i] == c in keys.
  2. Replace c with values[i] in the string.

A string is decrypted with the following process:

  1. For each substring s of length 2 occurring at an even index in the string, we find an i such that values[i] == s. If there are multiple valid i, we choose any one of them. This means a string could have multiple possible strings it can decrypt to.
  2. Replace s with keys[i] in the string.

Implement the Encrypter class:

  • Encrypter(char[] keys, String[] values, String[] dictionary) Initializes the Encrypter class with keys, values, and dictionary.
  • String encrypt(String word1) Encrypts word1 with the encryption process described above and returns the encrypted string.
  • int decrypt(String word2) Returns the number of possible strings word2 could decrypt to that also appear in dictionary.

Example 1:

["Encrypter", "encrypt", "decrypt"]
[[['a', 'b', 'c', 'd'], ["ei", "zf", "ei", "am"], ["abcd", "acbd", "adbc", "badc", "dacb", "cadb", "cbda", "abad"]], ["abcd"], ["eizfeiam"]]

[null, “eizfeiam”, 2]

Explanation Encrypter encrypter = new Encrypter([[‘a’, ‘b’, ‘c’, ‘d’], [“ei”, “zf”, “ei”, “am”], [“abcd”, “acbd”, “adbc”, “badc”, “dacb”, “cadb”, “cbda”, “abad”]); encrypter.encrypt(“abcd”); // return “eizfeiam”.   // ‘a’ maps to “ei”, ‘b’ maps to “zf”, ‘c’ maps to “ei”, and ‘d’ maps to “am”. encrypter.decrypt(“eizfeiam”); // return 2. // “ei” can map to ‘a’ or ‘c’, “zf” maps to ‘b’, and “am” maps to ‘d’. // Thus, the possible strings after decryption are “abad”, “cbad”, “abcd”, and “cbcd”. // 2 of those strings, “abad” and “abcd”, appear in dictionary, so the answer is 2.


  • 1 <= keys.length == values.length <= 26
  • values[i].length == 2
  • 1 <= dictionary.length <= 100
  • 1 <= dictionary[i].length <= 100
  • All keys[i] and dictionary[i] are unique.
  • 1 <= word1.length <= 2000
  • 1 <= word2.length <= 200
  • All word1[i] appear in keys.
  • word2.length is even.
  • keysvalues[i]dictionary[i]word1, and word2 only contain lowercase English letters.
  • At most 200 calls will be made to encrypt and decrypt in total.


For encryption, follow the instruction. Time complexity: O(len(word)) = O(2000)
For decryption, try all words in the dictionary and encrypt them and compare the encrypted string with the word to decrypt. Time complexity: O(sum(len(word_in_dict))) = O(100*100)

Worst case: 200 calls to decryption, T = 200 * O(100 * 100) = O(2*106)



Pre-compute answer for all the words in dictionary.

decrypt: Time complexity: O(1)


花花酱 LeetCode 2223. Sum of Scores of Built Strings

You are building a string s of length n one character at a time, prepending each new character to the front of the string. The strings are labeled from 1 to n, where the string with length i is labeled si.

  • For example, for s = "abaca"s1 == "a"s2 == "ca"s3 == "aca", etc.

The score of si is the length of the longest common prefix between si and sn (Note that s == sn).

Given the final string s, return the sum of the score of every si.

Example 1:

Input: s = "babab"
Output: 9
For s1 == "b", the longest common prefix is "b" which has a score of 1.
For s2 == "ab", there is no common prefix so the score is 0.
For s3 == "bab", the longest common prefix is "bab" which has a score of 3.
For s4 == "abab", there is no common prefix so the score is 0.
For s5 == "babab", the longest common prefix is "babab" which has a score of 5.
The sum of the scores is 1 + 0 + 3 + 0 + 5 = 9, so we return 9.

Example 2:

Input: s = "azbazbzaz"
Output: 14
For s2 == "az", the longest common prefix is "az" which has a score of 2.
For s6 == "azbzaz", the longest common prefix is "azb" which has a score of 3.
For s9 == "azbazbzaz", the longest common prefix is "azbazbzaz" which has a score of 9.
For all other si, the score is 0.
The sum of the scores is 2 + 3 + 9 = 14, so we return 14.


  • 1 <= s.length <= 105
  • s consists of lowercase English letters.

Solution: Z-Function

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