K-th Largest Perfect Subtree Size in Binary Tree
Time O(n) · Space O(n) · Official statement on LeetCode
Solutions
// Time: O(n)
// Space: O(n)
// iterative dfs, quick select
class Solution {
public:
int kthLargestPerfectSubtree(TreeNode* root, int k) {
const auto& iter_dfs = [&]() {
vector<int> result;
using RET = int;
RET ret = 0;
vector<tuple<int, TreeNode *, shared_ptr<vector<int>>, RET *>> stk = {{1, root, nullptr, &ret}};
while (!empty(stk)) {
auto [step, curr, new_ret, ret] = stk.back(); stk.pop_back();
if (step == 1) {
if (!curr) {
*ret = 0;
result.emplace_back(*ret);
continue;
}
auto new_ret = make_shared<vector<int>>(2);
stk.emplace_back(2, nullptr, new_ret, ret);
stk.emplace_back(1, curr->right, nullptr, &((*new_ret)[1]));
stk.emplace_back(1, curr->left, nullptr, &((*new_ret)[0]));
} else if (step == 2) {
*ret = (*new_ret)[0] == (*new_ret)[1] && (*new_ret)[1] != -1 ? (*new_ret)[0] + (*new_ret)[1] + 1 : -1;
result.emplace_back(*ret);
}
}
return result;
};
auto result = iter_dfs();
if (k - 1 >= size(result)) {
return -1;
}
nth_element(begin(result), begin(result) + (k - 1), end(result), greater<int>());
return result[k - 1] ? result[k - 1] : -1;
}
};
// Time: O(n)
// Space: O(n)
// dfs, quick select
class Solution2 {
public:
int kthLargestPerfectSubtree(TreeNode* root, int k) {
vector<int> result;
const function<void (TreeNode *)> dfs = [&](auto curr) {
if (!curr) {
result.emplace_back(0);
return;
}
dfs(curr->left);
const int left = result.back();
dfs(curr->right);
const int right = result.back();
result.emplace_back(left == right && right != -1 ? left + right + 1 : -1);
};
dfs(root);
if (k - 1 >= size(result)) {
return -1;
}
nth_element(begin(result), begin(result) + (k - 1), end(result), greater<int>());
return result[k - 1] ? result[k - 1] : -1;
}
};
Beginner Explanation
What is K-th Largest Perfect Subtree Size in Binary Tree?
K-th Largest Perfect Subtree Size in Binary Tree (LeetCode #3319) is a Medium problem that primarily trains depth first search.
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.
- 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, Quick Select.
AlgoForge explanations are original teaching notes. Always open the official problem statement on LeetCode for constraints and examples.
Interview Walkthrough
Interview approach for K-th Largest Perfect Subtree Size in Binary 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.
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
Use the pattern as a checklist:
- dfs backtracking — 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 K-th Largest Perfect Subtree Size in Binary Tree
- Skipping edge cases — empty collections, single-element inputs, max constraints.
- Wrong invariant for dfs backtracking — 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:
- 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: depth first search.
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 K-th Largest Perfect Subtree Size in Binary Tree in a second language (cpp, python).
- Drill 3–5 more problems tagged depth first search.
- 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 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
K-th Largest Perfect Subtree Size in Binary Tree (#3319) — Medium. Pattern: dfs backtracking. Complexity: O(n) time / O(n) space. Re-derive the invariant before coding.
FAQs
What is the time complexity of K-th Largest Perfect Subtree Size in Binary 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 K-th Largest Perfect Subtree Size in Binary Tree use?+
It primarily maps to dfs backtracking, within the broader topic of depth first search.
Is K-th Largest Perfect Subtree Size in Binary 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/k-th-largest-perfect-subtree-size-in-binary-tree/