Rank | Name | Score | Finish Time | Q1 (3) | Q2 (4) | Q3 (5) | Q4 (6) |
---|---|---|---|---|---|---|---|
231 / 7926 | YoungForest | 18 | 0:42:16 | 0:04:51 | 0:10:55 | 0:22:31 1 | 0:37:16 |
质量还可以的手速场。有些问题值得思考,只有发现本质,才能迅速解决。
由于对reverse操作的数目不限,我们可以采用这样的策略构造将2个array转成相同的array。用类似select sort的思想,每次reverse可以将一个位置排好序。所以问题转化为,2个数组排好序后是否相等。
C++中vector的==
的作用正是如此。
时间复杂度: O(N log N),
空间复杂度: O(1).
class Solution {
public:
bool canBeEqual(vector<int>& target, vector<int>& arr) {
sort(target.begin(), target.end());
sort(arr.begin(), arr.end());
return target == arr;
}
};
滑动窗口。窗口大小K,判断所有0 - 2^K - 1
的二进制是否出现。
时间复杂度: O(s.size()),
空间复杂度: O(1 << K).
class Solution {
public:
bool hasAllCodes(string s, int k) {
vector<bool> seen(1 << k, false);
int current = 0;
for (int i = 0; i < k && i < s.size(); ++i) {
current = current * 2 - '0' + s[i];
}
seen[current] = true;
for (int i = k; i < s.size(); ++i) {
current = (current - (s[i-k]-'0') * (1 << (k-1))) * 2 - '0' + s[i];
seen[current] = true;
}
return all_of(seen.begin(), seen.end(), [](const auto& a) -> bool {
return a;
});
}
};
DFS. 这里要注意queries本身很大,但是n很小。所以需要采用记忆化的DFS,防止重复计算。
时间复杂度: O(n ^ 3 + queries),
空间复杂度: O(n ^ 2).
class Solution {
public:
vector<bool> checkIfPrerequisite(int n, vector<vector<int>>& prerequisites, vector<vector<int>>& queries) {
unordered_map<int, vector<int>> edges;
for (const auto& v : prerequisites) {
edges[v[1]].push_back(v[0]);
}
map<pair<int,int>, bool> memo;
function<bool(int,int,unordered_set<int>&)> dfs = [&](int root, int target, unordered_set<int>& seen) -> bool {
if (memo.find({root, target}) == memo.end()) {
if (target == root) return memo[{root, target}] = true;
for (int neighbor : edges[root]) {
if (seen.find(neighbor) == seen.end()) {
seen.insert(neighbor);
if (dfs(neighbor, target, seen)) return memo[{root, target}] = true;
}
}
return memo[{root, target}] = false;
} else {
return memo[{root, target}];
}
};
vector<bool> ans(queries.size());
for (int i = 0; i < queries.size(); ++i) {
unordered_set<int> seen;
ans[i] = dfs(queries[i][1], queries[i][0], seen);
}
return ans;
}
};
Discuss中提到可以用 Floyd-Warshall 算法,计算有向图中2点的最短距离。
DP.
dp[i][first][second]: 机器人走到第i行,第一个机器人的位置在first, 第二个在second 时,最大的cherry pick。
dp[i][first][second] = max(dp[i-1][for last_first in range(first - 1, first + 2)])[for last_second in range(second - 1, second + 2)] + (grid[i][first] + grid[i][second]) if first != second else grid[i][first]
.
时间复杂度: O(rows * cols^2 * 9),
空间复杂度: O(rows * cols^2) -> O(cols^2).
class Solution {
const int INF = 0x3f3f3f3f;
public:
int cherryPickup(vector<vector<int>>& grid) {
const int rows = grid.size();
const int cols = grid[0].size();
vector<vector<vector<int>>> dp (rows, vector<vector<int>>(cols, vector<int>(cols, -INF)));
dp[0][0][cols-1] = grid[0][0] + grid[0][cols-1];
vector<int> dj = {-1, 0, 1};
for (int i = 1; i < rows; ++i) {
for (int first = 0; first < cols; ++first) {
for (int second = 0; second < cols; ++second) {
int add = first == second ? grid[i][first] : grid[i][first] + grid[i][second];
for (int dfirst : dj) {
int last_first = first + dfirst;
if (last_first >= 0 && last_first < cols) {
for (int dsecond : dj) {
int last_second = second + dsecond;
if (last_second >= 0 && last_second < cols) {
dp[i][first][second] = max(dp[i][first][second], dp[i-1][last_first][last_second] + add);
}
}
}
}
}
}
}
int ans = 0;
for (int first = 0; first < cols; ++first) {
for (int second = 0; second < cols; ++second) {
ans = max(ans, dp[rows-1][first][second]);
}
}
return ans;
}
};