Parallel Courses II
Time O((n * C(c, min(c, k))) * 2^n) · Space O(2^n) · Official statement on LeetCode
Solutions
// Time: O((n * C(c, min(c, k))) * 2^n)
// Space: O(2^n)
// concise dp solution
class Solution {
public:
int minNumberOfSemesters(int n, vector<vector<int>>& dependencies, int k) {
vector<int> reqs(n);
for (const auto& d : dependencies) {
reqs[d[1] - 1] |= 1 << (d[0] - 1);
}
vector<int> dp(1 << n, n);
dp[0] = 0;
for (int mask = 0; mask < dp.size(); ++mask) {
vector<int> candidates;
for (int v = 0; v < n; ++v) {
if ((mask & (1 << v)) == 0 && (mask & reqs[v]) == reqs[v]) {
candidates.emplace_back(v);
}
}
const auto r = min(int(candidates.size()), k);
combinations(candidates.size(), min(int(candidates.size()), k),
[&dp, &mask, &candidates](const vector<int>& idxs) {
auto new_mask = mask;
new_mask |= accumulate(cbegin(idxs), cend(idxs), 0,
[&candidates](const auto& a, const auto& b) {
return a | (1 << candidates[b]);
});
dp[new_mask] = min(dp[new_mask], dp[mask] + 1);
});
}
return dp.back();
}
private:
void combinations(int n, int k, const function<void (const vector<int>&)>& callback) {
static const auto& next_pos =
[](const auto& n, const auto& k, const auto& idxs) {
int i = k - 1;
for (; i >= 0; --i) {
if (idxs[i] != i + n - k) {
break;
}
}
return i;
};
vector<int> idxs(k);
iota(begin(idxs), end(idxs), 0);
callback(idxs);
for (int i; (i = next_pos(n, k, idxs)) >= 0;) {
++idxs[i];
for (int j = i + 1; j < k; ++j) {
idxs[j] = idxs[j - 1] + 1;
}
callback(idxs);
}
}
};
// Time: O((n * C(c, min(c, k))) * 2^n)
// Space: O(2^n)
// embedded combination dp solution
class Solution2 {
public:
int minNumberOfSemesters(int n, vector<vector<int>>& dependencies, int k) {
static const auto& choice_mask =
[](const auto& nums, const auto& idxs) {
return accumulate(cbegin(idxs), cend(idxs), 0,
[&nums](const auto& a, const auto& b) {
return a | (1 << nums[b]);
});
};
static const auto& next_pos =
[](const auto& n, const auto& r, const auto& idxs) {
int i = r - 1;
for (; i >= 0; --i) {
if (idxs[i] != i + n - r) {
break;
}
}
return i;
};
vector<int> reqs(n);
for (const auto& d : dependencies) {
reqs[d[1] - 1] |= 1 << (d[0] - 1);
}
vector<int> dp(1 << n, n);
dp[0] = 0;
for (int mask = 0; mask < dp.size(); ++mask) {
vector<int> candidates;
for (int v = 0; v < n; ++v) {
if ((mask & (1 << v)) == 0 && (mask & reqs[v]) == reqs[v]) {
candidates.emplace_back(v);
}
}
const auto r = min(int(candidates.size()), k);
vector<int> idxs(r);
iota(begin(idxs), end(idxs), 0);
const auto& new_mask = (mask | choice_mask(candidates, idxs));
dp[new_mask] = min(dp[new_mask], dp[mask] + 1);
for (int i; (i = next_pos(candidates.size(), r, idxs)) >= 0;) {
++idxs[i];
for (int j = i + 1; j < k; ++j) {
idxs[j] = idxs[j - 1] + 1;
}
const auto& new_mask = (mask | choice_mask(candidates, idxs));
dp[new_mask] = min(dp[new_mask], dp[mask] + 1);
}
}
return dp.back();
}
};
// Time: O(nlogn + e), e is the number of edges in graph
// Space: O(n + e)
// wrong greedy solution
// since the priority of courses are hard to decide especially for those courses with zero indegrees are of the same outdegrees and depths
// e.x.
// 9
// [[1,4],[1,5],[3,5],[3,6],[2,6],[2,7],[8,4],[8,5],[9,6],[9,7]]
// 3
class Solution_WA {
public:
int minNumberOfSemesters(int n, vector<vector<int>>& dependencies, int k) {
unordered_map<int, vector<int>> graph;
vector<int> degrees(n);
for (const auto &d: dependencies) {
graph[d[0] - 1].emplace_back(d[1] - 1);
++degrees[d[1] - 1];
}
vector<int> depths(n, -1);
for (int i = 0; i < n; ++i) {
dfs(graph, i, &depths);
}
priority_queue<pair<int, int>> max_heap;
for (int i = 0; i < n; ++i) {
if (!degrees[i]) {
max_heap.emplace(depths[i], i);
}
}
int result = 0;
while (!max_heap.empty()) {
vector<int> new_q;
for (int i = 0; !max_heap.empty() && i < k; ++i) {
const auto [depth, node] = max_heap.top(); max_heap.pop();
for (const auto& child : graph[node]) {
if (!--degrees[child]) {
new_q.emplace_back(child);
}
}
}
++result;
for (const auto& node : new_q) {
max_heap.emplace(depths[node], node);
}
}
return result;
}
private:
int dfs(const unordered_map<int, vector<int>> &graph,
int i, vector<int> *depths) {
if ((*depths)[i] == -1) {
int depth = 0;
if (graph.count(i)) {
for (const auto& child : graph.at(i)) {
depth = max(depth, dfs(graph, child, depths));
}
}
(*depths)[i] = depth + 1;
}
return (*depths)[i];
}
};
Beginner Explanation
What is Parallel Courses II?
Parallel Courses II (LeetCode #1494) is a Hard problem that primarily trains dynamic programming.
How to think about it
- Restate the goal in your own words before coding.
- Work a tiny example by hand so the invariant becomes obvious.
- Identify the pattern — this problem aligns with dynamic programming.
- Only then translate the idea into code.
Why this problem matters
Hard problems force you to combine patterns and prove complexity carefully — interview gold. Official solution notes mention: Combinations.
AlgoForge explanations are original teaching notes. Always open the official problem statement on LeetCode for constraints and examples.
Interview Walkthrough
Interview approach for Parallel Courses II
Opening (30–60 seconds)
- Clarify inputs/outputs and edge cases (empty input, single element, duplicates, overflow).
- State a brute force so the interviewer knows you can solve it naively.
- Propose the optimal direction tied to dynamic programming.
Core solution narrative
- Define the state you track (pointers, DP cell, set membership, stack top, etc.).
- Explain the transition when you process the next element.
- Call out time (O((n * C(c, min(c, k))) * 2^n)) and space (O(2^n)) before coding.
- Code cleanly; narrate variable names.
What interviewers listen for
- Correctness on edge cases
- Complexity honesty
- Ability to discuss trade-offs (e.g., hash map space vs. sort + two pointers)
Follow-up questions they may ask
- Can you solve it with less memory?
- What if the input stream is infinite / doesn't fit in RAM?
- How would tests look for adversarial inputs?
Optimized Approach
Optimized solution notes
The reference solutions on AlgoForge target O((n * C(c, min(c, k))) * 2^n) time and O(2^n) space.
Pattern focus: dynamic programming
Use the pattern as a checklist:
- dynamic programming — confirm the invariant holds after each step
Multiple methods appear in the source solutions — compare them and explain when each is preferable.
Implementation tips
- Prefer readable names over micro-optimizations in interviews.
- Extract helpers only when they clarify (e.g., expand-around-center, DFS visit).
- After AC-level logic, re-scan for off-by-one and null checks.
Complexity Analysis
Complexity
| Measure | Bound |
|---|---|
| Time | O((n * C(c, min(c, k))) * 2^n) |
| Space | O(2^n) |
How to justify this in an interview
- Time: count loops, map/set operations, and recursive branching; state average vs worst case if relevant.
- Space: include hash maps, recursion stack, and output allocation when the problem asks for it.
If your implementation differs from the reference, re-derive big-O from your code — never memorize a complexity you cannot defend.
Common Mistakes
Common mistakes on Parallel Courses II
- Skipping edge cases — empty collections, single-element inputs, max constraints.
- Wrong invariant for dynamic programming — updating state too early or too late.
- Mutating input unexpectedly when the problem forbids it.
- Off-by-one in windows, ranges, or binary search bounds.
- Ignoring overflow / precision for integer arithmetic problems.
- Overengineering — jumping to an advanced structure when a simpler approach works.
Alternative Approaches
Alternatives
The source file includes more than one method. Compare:
- Primary optimized path — best complexity for typical interviews.
- Secondary approach — often brute force, sorting-based, or space-optimized variant.
Practice articulating when you would pick each (constraints, readability, follow-ups).
Edge Cases
Edge cases checklist
- Minimum input size
- Maximum input size / time limits
- Duplicates and already-sorted input
- Negative numbers / zeros (if applicable)
- Disconnected structures (graphs/trees)
- Single path vs branching recursion depth
Pattern Recognition
Spotting this pattern
Signal phrases that point to dynamic programming:
- Sorted input or ability to sort without changing the answer class
- Need for contiguous subarray / substring → consider sliding window
- Need for O(1) membership → hash set/map
- Optimal substructure + overlapping subproblems → DP
- Connectivity / components → graph DFS/BFS or Union-Find
Primary topics: dynamic programming.
Follow-up Interview Questions
Follow-ups
- How does the solution change if the input is a stream?
- Can you solve it in-place?
- What if duplicates must be handled differently?
- How would you parallelize the approach?
- Design tests that would break a buggy implementation.
Practice Recommendations
What to practice next
- Re-solve Parallel Courses II in a second language (cpp, python).
- Drill 3–5 more problems tagged dynamic programming.
- Teach the solution out loud in under 5 minutes.
- Add this problem to your revision calendar in 3 days and 14 days.
Visualization
Study checklist
- Read the official problem statement on LeetCode
- Solve on paper / whiteboard first
- Implement the dynamic programming approach
- Verify edge cases from the checklist
- State time and space complexity aloud
- Compare with the AlgoForge reference solution
- Schedule a revision session
Revision notes
Parallel Courses II (#1494) — Hard. Pattern: dynamic programming. Complexity: O((n * C(c, min(c, k))) * 2^n) time / O(2^n) space. Re-derive the invariant before coding.
FAQs
What is the time complexity of Parallel Courses II?+
The reference solutions aim for O((n * C(c, min(c, k))) * 2^n) time and O(2^n) space. Always re-derive complexity from the code you write in the interview.
What pattern does Parallel Courses II use?+
It primarily maps to dynamic programming, within the broader topic of dynamic programming.
Is Parallel Courses II good for interviews?+
Yes — as a Hard problem it is a solid practice target. Pair it with related problems in the same pattern family for spaced repetition.
Where can I read the official statement?+
Open the official LeetCode page for constraints and examples: https://leetcode.com/problems/parallel-courses-ii/