LeetCode #1993 — MEDIUM

Operations on Tree

Move from brute-force thinking to an efficient approach using array strategy.

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 the ith node. The root of the tree is node 0, so parent[0] = -1 since it has no parent. You want to design a data structure that allows users to lock, unlock, and upgrade nodes in the tree.

The data structure should support the following functions:

  • Lock: Locks the given node for the given user and prevents other users from locking the same node. You may only lock a node using this function if the node is unlocked.
  • Unlock: Unlocks the given node for the given user. You may only unlock a node using this function if it is currently locked by the same user.
  • Upgrade: Locks the given node for the given user and unlocks all of its descendants regardless of who locked it. You may only upgrade a node if all 3 conditions are true:
    • The node is unlocked,
    • It has at least one locked descendant (by any user), and
    • It does not have any locked ancestors.

Implement the LockingTree class:

  • LockingTree(int[] parent) initializes the data structure with the parent array.
  • lock(int num, int user) returns true if it is possible for the user with id user to lock the node num, or false otherwise. If it is possible, the node num will become locked by the user with id user.
  • unlock(int num, int user) returns true if it is possible for the user with id user to unlock the node num, or false otherwise. If it is possible, the node num will become unlocked.
  • upgrade(int num, int user) returns true if it is possible for the user with id user to upgrade the node num, or false otherwise. If it is possible, the node num will be upgraded.

Example 1:

Input
["LockingTree", "lock", "unlock", "unlock", "lock", "upgrade", "lock"]
[[[-1, 0, 0, 1, 1, 2, 2]], [2, 2], [2, 3], [2, 2], [4, 5], [0, 1], [0, 1]]
Output
[null, true, false, true, true, true, false]

Explanation
LockingTree lockingTree = new LockingTree([-1, 0, 0, 1, 1, 2, 2]);
lockingTree.lock(2, 2);    // return true because node 2 is unlocked.
                           // Node 2 will now be locked by user 2.
lockingTree.unlock(2, 3);  // return false because user 3 cannot unlock a node locked by user 2.
lockingTree.unlock(2, 2);  // return true because node 2 was previously locked by user 2.
                           // Node 2 will now be unlocked.
lockingTree.lock(4, 5);    // return true because node 4 is unlocked.
                           // Node 4 will now be locked by user 5.
lockingTree.upgrade(0, 1); // return true because node 0 is unlocked and has at least one locked descendant (node 4).
                           // Node 0 will now be locked by user 1 and node 4 will now be unlocked.
lockingTree.lock(0, 1);    // return false because node 0 is already locked.

Constraints:

  • n == parent.length
  • 2 <= n <= 2000
  • 0 <= parent[i] <= n - 1 for i != 0
  • parent[0] == -1
  • 0 <= num <= n - 1
  • 1 <= user <= 104
  • parent represents a valid tree.
  • At most 2000 calls in total will be made to lock, unlock, and upgrade.
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 the ith node. The root of the tree is node 0, so parent[0] = -1 since it has no parent. You want to design a data structure that allows users to lock, unlock, and upgrade nodes in the tree. The data structure should support the following functions: Lock: Locks the given node for the given user and prevents other users from locking the same node. You may only lock a node using this function if the node is unlocked. Unlock: Unlocks the given node for the given user. You may only unlock a node using this function if it is currently locked by the same user. Upgrade: Locks the given node for the given user and unlocks all of its descendants regardless of who locked it. You may only upgrade a node if all 3 conditions are true: The node is unlocked, It has at least

Baseline thinking

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

Pattern signal: Array · Hash Map · Tree · Design

Example 1

["LockingTree","lock","unlock","unlock","lock","upgrade","lock"]
[[[-1,0,0,1,1,2,2]],[2,2],[2,3],[2,2],[4,5],[0,1],[0,1]]

Related Problems

  • Throne Inheritance (throne-inheritance)
Step 02

Core Insight

What unlocks the optimal approach

  • How can we use the small constraints to help us solve the problem?
  • How can we traverse the ancestors and descendants of a node?
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
Upper-end input sizes
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 #1993: Operations on Tree
class LockingTree {
    private int[] locked;
    private int[] parent;
    private List<Integer>[] children;

    public LockingTree(int[] parent) {
        int n = parent.length;
        locked = new int[n];
        this.parent = parent;
        children = new List[n];
        Arrays.fill(locked, -1);
        Arrays.setAll(children, i -> new ArrayList<>());
        for (int i = 1; i < n; i++) {
            children[parent[i]].add(i);
        }
    }

    public boolean lock(int num, int user) {
        if (locked[num] == -1) {
            locked[num] = user;
            return true;
        }
        return false;
    }

    public boolean unlock(int num, int user) {
        if (locked[num] == user) {
            locked[num] = -1;
            return true;
        }
        return false;
    }

    public boolean upgrade(int num, int user) {
        int x = num;
        while (x != -1) {
            if (locked[x] != -1) {
                return false;
            }
            x = parent[x];
        }
        boolean[] find = new boolean[1];
        dfs(num, find);
        if (!find[0]) {
            return false;
        }
        locked[num] = user;
        return true;
    }

    private void dfs(int x, boolean[] find) {
        for (int y : children[x]) {
            if (locked[y] != -1) {
                locked[y] = -1;
                find[0] = true;
            }
            dfs(y, find);
        }
    }
}

/**
 * Your LockingTree object will be instantiated and called as such:
 * LockingTree obj = new LockingTree(parent);
 * boolean param_1 = obj.lock(num,user);
 * boolean param_2 = obj.unlock(num,user);
 * boolean param_3 = obj.upgrade(num,user);
 */
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(n)
Space
O(h)

Approach Breakdown

LEVEL ORDER
O(n) time
O(n) space

BFS with a queue visits every node exactly once — O(n) time. The queue may hold an entire level of the tree, which for a complete binary tree is up to n/2 nodes = O(n) space. This is optimal in time but costly in space for wide trees.

DFS TRAVERSAL
O(n) time
O(h) space

Every node is visited exactly once, giving O(n) time. Space depends on tree shape: O(h) for recursive DFS (stack depth = height h), or O(w) for BFS (queue width = widest level). For balanced trees h = log n; for skewed trees h = n.

Shortcut: Visit every node once → O(n) time. Recursion depth = tree height → O(h) space.
Coach Notes

Common Mistakes

Review these before coding to avoid predictable interview regressions.

Off-by-one on range boundaries

Wrong move: Loop endpoints miss first/last candidate.

Usually fails on: Fails on minimal arrays and exact-boundary answers.

Fix: Re-derive loops from inclusive/exclusive ranges before coding.

Mutating counts without cleanup

Wrong move: Zero-count keys stay in map and break distinct/count constraints.

Usually fails on: Window/map size checks are consistently off by one.

Fix: Delete keys when count reaches zero.

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.