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| 1 | +// Time: O(nlogn) |
| 2 | +// Space: O(n) |
| 3 | + |
| 4 | +class Solution { |
| 5 | +public: |
| 6 | + int jobScheduling(vector<int>& startTime, vector<int>& endTime, vector<int>& profit) { |
| 7 | + vector<tuple<int, int, int>> jobs; |
| 8 | + for (int i = 0; i < startTime.size(); ++i) { |
| 9 | + jobs.emplace_back(endTime[i], startTime[i], profit[i]); |
| 10 | + } |
| 11 | + sort(jobs.begin(), jobs.end()); |
| 12 | + vector<pair<int, int>> dp = {{0, 0}}; |
| 13 | + for (const auto& [e, s, p] : jobs) { |
| 14 | + const auto& it = prev(upper_bound(dp.cbegin(), |
| 15 | + dp.cend(), |
| 16 | + make_pair(s + 1, 0))); |
| 17 | + if (it->second + p > dp.back().second) { |
| 18 | + dp.emplace_back(e, it->second + p); |
| 19 | + } |
| 20 | + } |
| 21 | + return dp.back().second; |
| 22 | + } |
| 23 | +}; |
| 24 | + |
| 25 | + |
| 26 | +// Time: O(nlogn) |
| 27 | +// Space: O(n) |
| 28 | +class Solution2 { |
| 29 | +public: |
| 30 | + int jobScheduling(vector<int>& startTime, vector<int>& endTime, vector<int>& profit) { |
| 31 | + vector<tuple<int, int, int>> min_heap; |
| 32 | + for (int i = 0; i < startTime.size(); ++i) { |
| 33 | + min_heap.emplace_back(startTime[i], endTime[i], profit[i]); |
| 34 | + } |
| 35 | + make_heap(min_heap.begin(), min_heap.end(), greater<>()); |
| 36 | + int result = 0; |
| 37 | + while (!min_heap.empty()) { |
| 38 | + pop_heap(min_heap.begin(), min_heap.end(), greater<>()); |
| 39 | + const auto [s, e, p] = min_heap.back(); |
| 40 | + min_heap.pop_back(); |
| 41 | + if (s < e) { |
| 42 | + min_heap.emplace_back(e, s, result + p); |
| 43 | + push_heap(min_heap.begin(), min_heap.end(), greater<>()); |
| 44 | + } else { |
| 45 | + result = max(result, p); |
| 46 | + } |
| 47 | + } |
| 48 | + return result; |
| 49 | + } |
| 50 | +}; |
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