Minimum Time to Collect All Apples in a Tree
Time O(n) · Space O(n) · Official statement on LeetCode
Solutions
// Time: O(n)
// Space: O(n)
class Solution {
public:
int minTime(int n, vector<vector<int>>& edges, vector<bool>& hasApple) {
unordered_map<int, vector<int>> graph;
for (const auto& edge : edges) {
graph[edge[0]].emplace_back(edge[1]);
graph[edge[1]].emplace_back(edge[0]);
}
using RET = pair<int, int>;
RET result{};
vector<tuple<int, int, int, shared_ptr<RET>, RET *>> stk = {{1, -1, 0, nullptr, &result}};
while (!stk.empty()) {
const auto [step, par, node, new_ret, ret] = stk.back(); stk.pop_back();
if (step == 1) {
ret->second = int(hasApple[node]);
for (const auto& nei : graph[node]) {
if (nei == par) {
continue;
}
const auto& new_ret = make_shared<RET>();
stk.emplace_back(2, -1, -1, new_ret, ret);
stk.emplace_back(1, node, nei, nullptr, new_ret.get());
}
} else {
ret->first += new_ret->first + new_ret->second;
ret->second |= bool(new_ret->first + new_ret->second);
}
}
return 2 * result.first;
}
};
// Time: O(n)
// Space: O(n)
class Solution_Recu {
public:
int minTime(int n, vector<vector<int>>& edges, vector<bool>& hasApple) {
unordered_map<int, vector<int>> graph;
for (const auto& edge : edges) {
graph[edge[0]].emplace_back(edge[1]);
graph[edge[1]].emplace_back(edge[0]);
}
return 2 * dfs(graph, -1, 0, hasApple).first;
}
private:
pair<int, int> dfs(const unordered_map<int, vector<int>>& graph,
int par, int node,
const vector<bool>& hasApple) {
int result = 0, extra = hasApple[node];
for (const auto& nei : graph.at(node)) {
if (nei == par) {
continue;
}
const auto& [count, found] = dfs(graph, node, nei, hasApple);
result += count + found;
extra |= bool(count + found);
}
return {result, extra};
}
};
// Time: O(n)
// Space: O(n)
class Solution2 {
public:
int minTime(int n, vector<vector<int>>& edges, vector<bool>& hasApple) {
unordered_map<int, vector<int>> graph;
for (const auto& edge : edges) {
graph[edge[0]].emplace_back(edge[1]);
graph[edge[1]].emplace_back(edge[0]);
}
using RET = int;
RET result{};
vector<tuple<int, int, int, shared_ptr<RET>, shared_ptr<RET>, RET *>> stk = {{1, -1, 0, nullptr, nullptr, &result}};
while (!stk.empty()) {
const auto [step, par, node, new_ret, tmp, ret] = stk.back(); stk.pop_back();
if (step == 1) {
const auto& tmp = make_shared<RET>(hasApple[node]);
stk.emplace_back(3, -1, -1, nullptr, tmp, ret);
for (const auto& nei : graph[node]) {
if (nei == par) {
continue;
}
const auto& new_ret = make_shared<RET>();
stk.emplace_back(2, -1, -1, new_ret, tmp, ret);
stk.emplace_back(1, node, nei, nullptr, nullptr, new_ret.get());
}
} else if (step == 2) {
*ret += *new_ret;
*tmp |= bool(*new_ret);
} else {
*ret += *tmp;
}
}
return 2 * max(result - 1, 0);
}
};
// Time: O(n)
// Space: O(n)
class Solution2_Recu {
public:
int minTime(int n, vector<vector<int>>& edges, vector<bool>& hasApple) {
unordered_map<int, vector<int>> graph;
for (const auto& edge : edges) {
graph[edge[0]].emplace_back(edge[1]);
graph[edge[1]].emplace_back(edge[0]);
}
return 2 * max(dfs(graph, -1, 0, hasApple) - 1, 0);
}
private:
int dfs(const unordered_map<int, vector<int>>& graph,
int par, int node,
const vector<bool>& hasApple) {
int result = 0, extra = hasApple[node];
for (const auto& nei : graph.at(node)) {
if (nei == par) {
continue;
}
const auto& count = dfs(graph, node, nei, hasApple);
result += count;
extra |= bool(count);
}
return result + extra;
}
};
Beginner Explanation
What is Minimum Time to Collect All Apples in a Tree?
Minimum Time to Collect All Apples in a Tree (LeetCode #1443) is a Medium problem that primarily trains tree.
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 dfs backtracking and stack.
- Only then translate the idea into code.
Why this problem matters
It sits in the sweet spot of interview difficulty: multiple valid approaches, clear trade-offs. Official solution notes mention: DFS, Stack.
AlgoForge explanations are original teaching notes. Always open the official problem statement on LeetCode for constraints and examples.
Interview Walkthrough
Interview approach for Minimum Time to Collect All Apples in a Tree
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 dfs backtracking and stack.
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)) and space (O(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) time and O(n) space.
Pattern focus: dfs backtracking and stack
Use the pattern as a checklist:
- dfs backtracking — confirm the invariant holds after each step
- stack — 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) |
| Space | O(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 Minimum Time to Collect All Apples in a Tree
- Skipping edge cases — empty collections, single-element inputs, max constraints.
- Wrong invariant for dfs backtracking and stack — 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 dfs backtracking and stack:
- 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: tree.
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 Minimum Time to Collect All Apples in a Tree in a second language (cpp, python).
- Drill 3–5 more problems tagged tree.
- 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 dfs backtracking and stack 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
Minimum Time to Collect All Apples in a Tree (#1443) — Medium. Pattern: dfs backtracking and stack. Complexity: O(n) time / O(n) space. Re-derive the invariant before coding.
FAQs
What is the time complexity of Minimum Time to Collect All Apples in a Tree?+
The reference solutions aim for O(n) time and O(n) space. Always re-derive complexity from the code you write in the interview.
What pattern does Minimum Time to Collect All Apples in a Tree use?+
It primarily maps to dfs backtracking and stack, within the broader topic of tree.
Is Minimum Time to Collect All Apples in a Tree good for interviews?+
Yes — as a Medium 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/minimum-time-to-collect-all-apples-in-a-tree/