Posts tagged as “subset”

花花酱 LeetCode 646. Maximum Length of Pair Chain

By zxi on April 9, 2025

这题我选DP，因为不需要证明，直接干就行了。

方法1: DP

首先还是需要对pairs的right进行排序。一方面是为了方便chaining，另一方面是可以去重。

然后定义 dp[i] := 以pairs[i]作为结尾，最长的序列的长度。

状态转移：dp[i] = max(dp[j] + 1) where pairs[i].left > pairs[j].right and 0 < j < i.

解释：对于pairs[i]，找一个最优的pairs[j]，满足pairs[i].left > pairs[j].right，这样我就可以把pairs[i]追加到以pairs[j]结尾的最长序列后面了，长度+1。

边检条件：dp[0:n] = 1，每个pair以自己作为序列的最后元素，长度为1。

时间复杂度：O(n²)
空间复杂度：O(n)

class Solution {

public:

int findLongestChain(vector<vector<int>>& pairs) {

sort(begin(pairs), end(pairs), [](const auto& p1, const auto& p2){

return p1[1] < p2[1];

});

const int n = pairs.size();

vector<int> dp(n, 1);

for (int i = 0; i < n; ++i)

for (int j = 0; j < i; ++j)

if (pairs[i][0] > pairs[j][1])

dp[i] = max(dp[i], dp[j] + 1);

return *max_element(begin(dp), end(dp));

}

};

方法2: 贪心

和DP一样，对数据进行排序。

每当我看到 cur.left > prev.right，那么就直接把cur接在prev后面。我们需要证明这么做是最优的，而不是跳过它选后面的元素。

Case 0: cur已经是最后一个元素了，跳过就不是最优解了。

假设cur后面有next, 因为已经排序过了，我们可以得知 next.right >= cur.right。

Case 1: next.right == cur.right。这时候选cur和选next对后面的决策来说是一样的，但由于Case 0的存在，我必须选cur。

Case 2: next.right > cur.right。不管left的情况怎么样，由于right更小，这时候选择cur一定是优于next。

综上所述，看到有元素可以连接起来就贪心的选它就对了。

时间复杂度：O(nlogn)
空间复杂度：O(1)

class Solution {

public:

int findLongestChain(vector<vector<int>>& pairs) {

sort(begin(pairs), end(pairs), [](const auto& p1, const auto& p2){

return p1[1] < p2[1];

});

int right = INT_MIN;

int ans = 0;

for (const auto& p : pairs)

if (p[0] > right) {

right = p[1];

++ans;

}

return ans;

}

};

花花酱 LeetCode 368. Largest Divisible Subset

By zxi on April 4, 2025

也算是一道比较经典的DP题了，需要找到一个最大的子集，使得其中元素两两的余数为0。

我们假设有一个集合为{a1, a2, a3, …, an}, {a1 < a2 < … < an)

如果 a2 % a1 = 0, a3 % a2 == 0, 那么a3 % a1 一定也等于0。也就是说a2是a1的倍数，a3是a2的倍数，那么a3一定也是a1的倍数。所以我们并不需要测试任意两个元素之间的余数为0，我们只需要检测a[i]是否为a[i-1]的倍数即可，如果成立，则可以确保a[i]也是a[i-2], a[i-3], … a[1]的倍数。这样就可以大大降低问题的复杂度。

接下来就需要dp就发挥威力了。我们将原数组排序，用dp[i]来表示以a[i]结尾（集合中的最大值），能够构建的子集的最大长度。

转移方程：dp[j] = max(dp[j], dp[i] + 1) if nums[j] % nums[i] = 0.

这表示，如果nums[j] 是 nums[i]的倍数，那么我们可以把nums[j]加入以nums[i]结尾的最大子集中，构建一个新的最大子集，长度+1。当然对于j，可能有好多个满足条件的i，我们需要找一个最大的。

举个例子：
nums = {2, 3, 4, 12}
以3结尾的最大子集{3}，长度为1。由于12%3==0，那么我们可以把12追加到{3} 中，构成{3,12}。
以4结尾的最大子集是{2,4}，长度为2。由于12%4==0，那么我们可以把12追加到{2,4} 中，构成{2,4,12} 。得到最优解。

这样我们只需要双重循环，枚举i，再枚举j (0 <= i < j)。时间复杂度：O(n^2)。空间复杂度：O(n)。

最后需要输出一个最大子集，如果不记录递推过程，则需要稍微判断一下。

class Solution {

public:

vector<int> largestDivisibleSubset(vector<int>& nums) {

const int n = nums.size();

sort(begin(nums), end(nums));

// dp[i] = max size of subset ends with nums[i]

vector<int> dp(n);

for (int i = 0; i < n; ++i)

for (int j = i + 1; j < n; ++j)

if (nums[j] % nums[i] == 0)

dp[j] = max(dp[j], dp[i] + 1);

int s = *max_element(begin(dp), end(dp));

vector<int> ans;

for (int i = n - 1; i >= 0; --i) {

if (dp[i] == s && (ans.empty() || ans.back() % nums[i] == 0)) {

ans.push_back(nums[i]);

--s;

}

return ans;

}

};

花花酱 LeetCode 2044. Count Number of Maximum Bitwise-OR Subsets

By zxi on October 17, 2021

Given an integer array nums, find the maximum possible bitwise OR of a subset of nums and return the number of different non-empty subsets with the maximum bitwise OR.

An array a is a subset of an array b if a can be obtained from b by deleting some (possibly zero) elements of b. Two subsets are considered different if the indices of the elements chosen are different.

The bitwise OR of an array a is equal to a[0] OR a[1] OR ... OR a[a.length - 1] (0-indexed).

Example 1:

Input: nums = [3,1]
Output: 2
Explanation: The maximum possible bitwise OR of a subset is 3. There are 2 subsets with a bitwise OR of 3:
- [3]
- [3,1]

Example 2:

Input: nums = [2,2,2]
Output: 7
Explanation: All non-empty subsets of [2,2,2] have a bitwise OR of 2. There are 2³ - 1 = 7 total subsets.

Example 3:

Input: nums = [3,2,1,5]
Output: 6
Explanation: The maximum possible bitwise OR of a subset is 7. There are 6 subsets with a bitwise OR of 7:
- [3,5]
- [3,1,5]
- [3,2,5]
- [3,2,1,5]
- [2,5]
- [2,1,5]

Constraints:

1 <= nums.length <= 16
1 <= nums[i] <= 10⁵

Solution: Brute Force

Try all possible subsets

Time complexity: O(n*2ⁿ)
Space complexity: O(1)

C++

// Author: Huahua

class Solution {

public:

int countMaxOrSubsets(vector<int>& nums) {

const int n = nums.size();

int max_or = 0;

int count = 0;

for (int s = 0; s < 1 << n; ++s) {

int cur_or = 0;

for (int i = 0; i < n; ++i)

if (s >> i & 1) cur_or |= nums[i];

if (cur_or > max_or) {

max_or = cur_or;

count = 1;

} else if (cur_or == max_or) {

++count;

}

return count;

}

};

花花酱 LeetCode 1863. Sum of All Subset XOR Totals

By zxi on August 4, 2021

The XOR total of an array is defined as the bitwise XOR of all its elements, or 0 if the array is empty.

For example, the XOR total of the array [2,5,6] is 2 XOR 5 XOR 6 = 1.

Given an array nums, return the sum of all XOR totals for every subset of nums.

Note: Subsets with the same elements should be counted multiple times.

An array a is a subset of an array b if a can be obtained from b by deleting some (possibly zero) elements of b.

Example 1:

Input: nums = [1,3]
Output: 6
Explanation: The 4 subsets of [1,3] are:
- The empty subset has an XOR total of 0.
- [1] has an XOR total of 1.
- [3] has an XOR total of 3.
- [1,3] has an XOR total of 1 XOR 3 = 2.
0 + 1 + 3 + 2 = 6

Example 2:

Input: nums = [5,1,6]
Output: 28
Explanation: The 8 subsets of [5,1,6] are:
- The empty subset has an XOR total of 0.
- [5] has an XOR total of 5.
- [1] has an XOR total of 1.
- [6] has an XOR total of 6.
- [5,1] has an XOR total of 5 XOR 1 = 4.
- [5,6] has an XOR total of 5 XOR 6 = 3.
- [1,6] has an XOR total of 1 XOR 6 = 7.
- [5,1,6] has an XOR total of 5 XOR 1 XOR 6 = 2.
0 + 5 + 1 + 6 + 4 + 3 + 7 + 2 = 28

Example 3:

Input: nums = [3,4,5,6,7,8]
Output: 480
Explanation: The sum of all XOR totals for every subset is 480.

Constraints:

1 <= nums.length <= 12
1 <= nums[i] <= 20

Solution 1: Brute Force

Use an array A to store all the xor subsets, for a given number x
A = A + [x ^ a for a in A]

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

Python3

# Author: Huahua

class Solution:

def subsetXORSum(self, nums: List[int]) -> int:

xors = [0]

for x in nums:

xors += [xor ^ x for xor in xors]

return sum(xors)

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

By zxi on May 16, 2020

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] 
Explanation: 
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]

Constraints:

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

C++

// Author: Huahua, 664 ms 58.4 MB

class Solution {

public:

vector<int> peopleIndexes(vector<vector<string>>& favoriteCompanies) {

const int n = favoriteCompanies.size();

vector<unordered_set<string>> m;

for (const auto& c : favoriteCompanies)

m.emplace_back(begin(c), end(c));

vector<int> ans;

for (int i = 0; i < n; ++i) {

bool valid = true;

for (int j = 0; j < n && valid; ++j) {

if (i == j) continue;

bool subset = true;

for (const auto& s : m[i])

if (!m[j].count(s)) {

subset = false;

break;

}

if (subset) {

valid = false;

break;

}

if (valid)

ans.push_back(i);

}

return ans;

}

};

Use int as key to make it faster.

C++

// Author: Huahua, 476 ms, 48.7 MB

class Solution {

public:

vector<int> peopleIndexes(vector<vector<string>>& favoriteCompanies) {

const int n = favoriteCompanies.size();

unordered_map<string, int> ids;

vector<unordered_set<int>> m;

for (const auto& companies : favoriteCompanies) {

m.push_back({});

for (const auto& c : companies) {

if (!ids.count(c))

ids[c] = ids.size();

m.back().insert(ids[c]);

}

vector<int> ans;

for (int i = 0; i < n; ++i) {

bool valid = true;

for (int j = 0; j < n && valid; ++j) {

if (i == j) continue;

bool subset = true;

for (const auto& s : m[i])

if (!m[j].count(s)) {

subset = false;

break;

}

if (subset) {

valid = false;

break;

}

if (valid) ans.push_back(i);

}

return ans;

}

};