LeetCode #1483 — HARD

Kth Ancestor of a Tree Node

Break down a hard problem into reliable checkpoints, edge-case handling, and complexity trade-offs.

Solve on LeetCode
The Problem

Problem Statement

You are given a tree with n nodes numbered from 0 to n - 1 in the form of a parent array parent where parent[i] is the parent of ith node. The root of the tree is node 0. Find the kth ancestor of a given node.

The kth ancestor of a tree node is the kth node in the path from that node to the root node.

Implement the TreeAncestor class:

  • TreeAncestor(int n, int[] parent) Initializes the object with the number of nodes in the tree and the parent array.
  • int getKthAncestor(int node, int k) return the kth ancestor of the given node node. If there is no such ancestor, return -1.

Example 1:

Input
["TreeAncestor", "getKthAncestor", "getKthAncestor", "getKthAncestor"]
[[7, [-1, 0, 0, 1, 1, 2, 2]], [3, 1], [5, 2], [6, 3]]
Output
[null, 1, 0, -1]

Explanation
TreeAncestor treeAncestor = new TreeAncestor(7, [-1, 0, 0, 1, 1, 2, 2]);
treeAncestor.getKthAncestor(3, 1); // returns 1 which is the parent of 3
treeAncestor.getKthAncestor(5, 2); // returns 0 which is the grandparent of 5
treeAncestor.getKthAncestor(6, 3); // returns -1 because there is no such ancestor

Constraints:

  • 1 <= k <= n <= 5 * 104
  • parent.length == n
  • parent[0] == -1
  • 0 <= parent[i] < n for all 0 < i < n
  • 0 <= node < n
  • There will be at most 5 * 104 queries.
Patterns Used

Roadmap

  1. Brute Force Baseline
  2. Core Insight
  3. Algorithm Walkthrough
  4. Edge Cases
  5. Full Annotated Code
  6. Interactive Study Demo
  7. Complexity Analysis
Step 01

Brute Force Baseline

Problem summary: You are given a tree with n nodes numbered from 0 to n - 1 in the form of a parent array parent where parent[i] is the parent of ith node. The root of the tree is node 0. Find the kth ancestor of a given node. The kth ancestor of a tree node is the kth node in the path from that node to the root node. Implement the TreeAncestor class: TreeAncestor(int n, int[] parent) Initializes the object with the number of nodes in the tree and the parent array. int getKthAncestor(int node, int k) return the kth ancestor of the given node node. If there is no such ancestor, return -1.

Baseline thinking

Start with the most direct exhaustive search. That gives a correctness anchor before optimizing.

Pattern signal: Binary Search · Dynamic Programming · Bit Manipulation · Tree · Design

Example 1

["TreeAncestor","getKthAncestor","getKthAncestor","getKthAncestor"]
[[7,[-1,0,0,1,1,2,2]],[3,1],[5,2],[6,3]]

Related Problems

  • Minimum Edge Weight Equilibrium Queries in a Tree (minimum-edge-weight-equilibrium-queries-in-a-tree)
Step 02

Core Insight

What unlocks the optimal approach

  • The queries must be answered efficiently to avoid time limit exceeded verdict.
  • Use sparse table (dynamic programming application) to travel the tree upwards in a fast way.
Interview move: turn each hint into an invariant you can check after every iteration/recursion step.
Step 03

Algorithm Walkthrough

Iteration Checklist

  1. Define state (indices, window, stack, map, DP cell, or recursion frame).
  2. Apply one transition step and update the invariant.
  3. Record answer candidate when condition is met.
  4. Continue until all input is consumed.
Use the first example testcase as your mental trace to verify each transition.
Step 04

Edge Cases

Minimum Input
Single element / shortest valid input
Validate boundary behavior before entering the main loop or recursion.
Duplicates & Repeats
Repeated values / repeated states
Decide whether duplicates should be merged, skipped, or counted explicitly.
Extreme Constraints
Largest constraint values
Re-check complexity target against constraints to avoid time-limit issues.
Invalid / Corner Shape
Empty collections, zeros, or disconnected structures
Handle special-case structure before the core algorithm path.
Step 05

Full Annotated Code

Source-backed implementations are provided below for direct study and interview prep.

// Accepted solution for LeetCode #1483: Kth Ancestor of a Tree Node
class TreeAncestor {
    private int[][] p;

    public TreeAncestor(int n, int[] parent) {
        p = new int[n][18];
        for (var e : p) {
            Arrays.fill(e, -1);
        }
        for (int i = 0; i < n; ++i) {
            p[i][0] = parent[i];
        }
        for (int j = 1; j < 18; ++j) {
            for (int i = 0; i < n; ++i) {
                if (p[i][j - 1] == -1) {
                    continue;
                }
                p[i][j] = p[p[i][j - 1]][j - 1];
            }
        }
    }

    public int getKthAncestor(int node, int k) {
        for (int i = 17; i >= 0; --i) {
            if (((k >> i) & 1) == 1) {
                node = p[node][i];
                if (node == -1) {
                    break;
                }
            }
        }
        return node;
    }
}

/**
 * Your TreeAncestor object will be instantiated and called as such:
 * TreeAncestor obj = new TreeAncestor(n, parent);
 * int param_1 = obj.getKthAncestor(node,k);
 */
Step 06

Interactive Study Demo

Use this to step through a reusable interview workflow for this problem.

Press Step or Run All to begin.
Step 07

Complexity Analysis

Time
O(log n)
Space
O(1)

Approach Breakdown

LINEAR SCAN
O(n) time
O(1) space

Check every element from left to right until we find the target or exhaust the array. Each comparison is O(1), and we may visit all n elements, giving O(n). No extra space needed.

BINARY SEARCH
O(log n) time
O(1) space

Each comparison eliminates half the remaining search space. After k comparisons, the space is n/2ᵏ. We stop when the space is 1, so k = log₂ n. No extra memory needed — just two pointers (lo, hi).

Shortcut: Halving the input each step → O(log n). Works on any monotonic condition, not just sorted arrays.
Coach Notes

Common Mistakes

Review these before coding to avoid predictable interview regressions.

Boundary update without `+1` / `-1`

Wrong move: Setting `lo = mid` or `hi = mid` can stall and create an infinite loop.

Usually fails on: Two-element ranges never converge.

Fix: Use `lo = mid + 1` or `hi = mid - 1` where appropriate.

State misses one required dimension

Wrong move: An incomplete state merges distinct subproblems and caches incorrect answers.

Usually fails on: Correctness breaks on cases that differ only in hidden state.

Fix: Define state so each unique subproblem maps to one DP cell.

Forgetting null/base-case handling

Wrong move: Recursive traversal assumes children always exist.

Usually fails on: Leaf nodes throw errors or create wrong depth/path values.

Fix: Handle null/base cases before recursive transitions.