Press "Enter" to skip to content

Posts tagged as “subset”

花花酱 LeetCode 1452. People Whose List of Favorite Companies Is Not a Subset of Another List

Given the array favoriteCompanies where favoriteCompanies[i] is the list of favorites companies for the ith person (indexed from 0).

Return the indices of people whose list of favorite companies is not a subset of any other list of favorites companies. You must return the indices in increasing order.

Example 1:

Input: favoriteCompanies = [["leetcode","google","facebook"],["google","microsoft"],["google","facebook"],["google"],["amazon"]]
Output: [0,1,4] 
Person with index=2 has favoriteCompanies[2]=["google","facebook"] which is a subset of favoriteCompanies[0]=["leetcode","google","facebook"] corresponding to the person with index 0. 
Person with index=3 has favoriteCompanies[3]=["google"] which is a subset of favoriteCompanies[0]=["leetcode","google","facebook"] and favoriteCompanies[1]=["google","microsoft"]. 
Other lists of favorite companies are not a subset of another list, therefore, the answer is [0,1,4].

Example 2:

Input: favoriteCompanies = [["leetcode","google","facebook"],["leetcode","amazon"],["facebook","google"]]
Output: [0,1] 
Explanation: In this case favoriteCompanies[2]=["facebook","google"] is a subset of favoriteCompanies[0]=["leetcode","google","facebook"], therefore, the answer is [0,1].

Example 3:

Input: favoriteCompanies = [["leetcode"],["google"],["facebook"],["amazon"]]
Output: [0,1,2,3]


  • 1 <= favoriteCompanies.length <= 100
  • 1 <= favoriteCompanies[i].length <= 500
  • 1 <= favoriteCompanies[i][j].length <= 20
  • All strings in favoriteCompanies[i] are distinct.
  • All lists of favorite companies are distinct, that is, If we sort alphabetically each list then favoriteCompanies[i] != favoriteCompanies[j].
  • All strings consist of lowercase English letters only.

Solution: Hashtable

Time complexity: O(n*n*m)
Space complexity: O(n*m)
where n is the # of people, m is the # of companies


Use int as key to make it faster.


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|)