LeetCode #3812 — HARD

Minimum Edge Toggles on a Tree

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 an undirected tree with n nodes, numbered from 0 to n - 1. It is represented by a 2D integer array edges​​​​​​​ of length n - 1, where edges[i] = [ai, bi] indicates that there is an edge between nodes ai and bi in the tree.

You are also given two binary strings start and target of length n. For each node x, start[x] is its initial color and target[x] is its desired color.

In one operation, you may pick an edge with index i and toggle both of its endpoints. That is, if the edge is [u, v], then the colors of nodes u and v each flip from '0' to '1' or from '1' to '0'.

Return an array of edge indices whose operations transform start into target. Among all valid sequences with minimum possible length, return the edge indices in increasing​​​​​​​ order.

If it is impossible to transform start into target, return an array containing a single element equal to -1.

Example 1:

​​​​​​​

Input: n = 3, edges = [[0,1],[1,2]], start = "010", target = "100"

Output: [0]

Explanation:

Toggle edge with index 0, which flips nodes 0 and 1.
​​​​​​​The string changes from "010" to "100", matching the target.

Example 2:

Input: n = 7, edges = [[0,1],[1,2],[2,3],[3,4],[3,5],[1,6]], start = "0011000", target = "0010001"

Output: [1,2,5]

Explanation:

  • Toggle edge with index 1, which flips nodes 1 and 2.
  • Toggle edge with index 2, which flips nodes 2 and 3.
  • Toggle edge with index 5, which flips nodes 1 and 6.

After these operations, the resulting string becomes "0010001", which matches the target.

Example 3:

​​​​​​​

Input: n = 2, edges = [[0,1]], start = "00", target = "01"

Output: [-1]

Explanation:

There is no sequence of edge toggles that transforms "00" into "01". Therefore, we return [-1].

Constraints:

  • 2 <= n == start.length == target.length <= 105
  • edges.length == n - 1
  • edges[i] = [ai, bi]
  • 0 <= ai, bi < n
  • start[i] is either '0' or '1'.
  • target[i] is either '0' or '1'.
  • The input is generated such that edges represents a valid tree.
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 an undirected tree with n nodes, numbered from 0 to n - 1. It is represented by a 2D integer array edges​​​​​​​ of length n - 1, where edges[i] = [ai, bi] indicates that there is an edge between nodes ai and bi in the tree. You are also given two binary strings start and target of length n. For each node x, start[x] is its initial color and target[x] is its desired color. In one operation, you may pick an edge with index i and toggle both of its endpoints. That is, if the edge is [u, v], then the colors of nodes u and v each flip from '0' to '1' or from '1' to '0'. Return an array of edge indices whose operations transform start into target. Among all valid sequences with minimum possible length, return the edge indices in increasing​​​​​​​ order. If it is impossible to transform start into target, return an array containing a single element equal to -1.

Baseline thinking

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

Pattern signal: Tree · Topological Sort

Example 1

3
[[0,1],[1,2]]
"010"
"100"

Example 2

7
[[0,1],[1,2],[2,3],[3,4],[3,5],[1,6]]
"0011000"
"0010001"

Example 3

2
[[0,1]]
"00"
"01"
Step 02

Core Insight

What unlocks the optimal approach

  • Use a depth-first search (DFS).
  • Root the tree at any node. Traverse the tree, keeping track of flips applied from the parent to the subtree.
  • After processing a child subtree, determine whether the current node still needs to be flipped to match the target. If a flip is needed, record the corresponding edge.
  • Collect all flipped edges; if the transformation is impossible, return <code>[-1]</code>.
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 #3812: Minimum Edge Toggles on a Tree
class Solution {
    private final List<Integer> ans = new ArrayList<>();
    private List<int[]>[] g;
    private char[] start;
    private char[] target;

    public List<Integer> minimumFlips(int n, int[][] edges, String start, String target) {
        g = new List[n];
        Arrays.setAll(g, k -> new ArrayList<>());
        for (int i = 0; i < n - 1; ++i) {
            int a = edges[i][0], b = edges[i][1];
            g[a].add(new int[] {b, i});
            g[b].add(new int[] {a, i});
        }
        this.start = start.toCharArray();
        this.target = target.toCharArray();
        if (dfs(0, -1)) {
            return List.of(-1);
        }
        Collections.sort(ans);
        return ans;
    }

    private boolean dfs(int a, int fa) {
        boolean rev = start[a] != target[a];
        for (var e : g[a]) {
            int b = e[0], i = e[1];
            if (b != fa && dfs(b, a)) {
                ans.add(i);
                rev = !rev;
            }
        }
        return rev;
    }
}
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 × log n)
Space
O(n)

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.

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.