Problem Description

In the given problem, we are asked to compare two binary trees to check if they are 'leaf-similar'. Binary trees are a type of data structure in which each node has at most two children, referred to as the left child and the right child. A leaf node is a node with no children. The 'leaf value sequence' is the list of values of these leaf nodes read from left to right. Two binary trees are considered 'leaf-similar' if the sequences of their leaf values are identical.

For example, the leaf value sequence of the tree depicted in the problem is (6, 7, 4, 9, 8) as those are the values of the nodes that have no children, read from left to right. To solve this problem, we need to collect the leaf sequences from both trees and then compare them to determine if the two trees are 'leaf-similar'.

Flowchart Walkthrough

Let's analyze the Leetcode problem 872, Leaf-Similar Trees, using the Flowchart. Here's how to proceed step-by-step:

1. Is it a graph?

   • Yes: Despite being about trees, each tree can be considered a graph.

2. Is it a tree?

   • Yes: Specifically, the problem deals with comparing leaf sequences from two binary trees.

3. Conclusion: According to the flowchart, once we determine that the structure in question is a tree, the recommended approach is to use Depth-First Search (DFS) to traverse and process the trees. In the context of this problem, DFS would be appropriate for efficiently visiting each leaf in the given binary trees to compare their leaf sequences.

Intuition

To solve this problem, we can use a Depth-First Search (DFS) on both trees. DFS is an algorithm for traversing or searching tree or graph data structures. In the context of a binary tree, it starts at the root and explores as far as possible along each branch before backtracking.

Here's why DFS is suitable for this problem:

1. DFS can be easily implemented using recursion.
2. While traversing the tree using DFS, we have a clear path to the leaf nodes, which are the focus of this problem.
3. We can build the leaf sequence during the DFS by adding the node's value to our sequence only when we hit a leaf node (a node with no children).

The provided solution uses the DFS method dfs to traverse each tree starting from the root and collect the leaves values. The function dfs recursively visits the left and right children of the current node and stores the node's value if and only if the node is a leaf (i.e., it has neither a left nor a right child). This collected sequence of values is then returned as a list for each tree. Finally, the solution compares the two lists of leaf values, and if they are equal, it returns true, indicating the two trees are leaf-similar. If not, it returns false.

Learn more about Tree, Depth-First Search and Binary Tree patterns.

Solution Approach

The solution is implemented in Python, and it focuses on a Depth-First Search (DFS) approach to traverse through the binary trees. The implementation defines a nested function dfs within the leafSimilar method, with the purpose of performing the actual DFS traversal, searching the entire tree for its leaves, and building the leaf sequence.

Here's the step-by-step breakdown of the algorithm:

1. The dfs function is defined, which takes a single argument, root, representing the current node in the tree.
2. Upon each invocation of dfs, the function first checks if the current root node is None. If it is, it returns an empty list as there are no leaves to gather from a None subtree.
3. If the current node is not None, the dfs function first recursively calls itself with root.left and root.right to search through the entire left and right subtrees.
4. The leaves are gathered by this part of the code: ans = dfs(root.left) + dfs(root.right). This code concatenates the left and right subtree leaves into one sequence.
5. Finally, the function checks if the node is a leaf node, that means both root.left and root.right are None. If it is a leaf, it returns a list containing the leaf's value: [root.val]. If the node is not a leaf, it returns the concatenated list of leaf values from both the left and right subtrees.
6. The main function leafSimilar calls the dfs function for both root1 and root2 trees, collecting the sequences of leaf values as lists.
7. The solution concludes by comparing these two lists with dfs(root1) == dfs(root2). If they are identical, it means that the leaf value sequences are the same, and therefore the two trees are considered leaf-similar and True is returned. If the sequences differ in any way, the function returns False.

This solution uses recursion as a natural way to navigate a tree's structure and gather leaf values, exploiting the divide-and-conquer nature of the problem. The code is efficient and concise, exploring each node exactly once, which results in a time complexity of O(N), where N is the total number of nodes in the tree.

Example Walkthrough

Let's take two small binary trees as an example to illustrate the solution approach:

Consider the following binary trees, Tree1 and Tree2:

    Tree1              Tree2
      3                 5
     / \               / \
    5   1             3   6
   / \                 \
  6   2                 7
     /
    7

For Tree1, given the binary tree structure, the leaves are the nodes with the values 6, 7, and 1. Therefore, the leaf value sequence for Tree1 is [6, 7, 1].

For Tree2, the leaves are the nodes with the values 3, 7, and 6. Thus, the leaf value sequence for Tree2 is [3, 7, 6].

Now we want to determine if Tree1 and Tree2 are leaf-similar according to the problem statement using the DFS approach.

Step-by-Step Walkthrough:

1. For Tree1, the dfs function begins at the root node 3, and then recursively searches the left child 5. From node 5, it further traverses its children 6 (a leaf node) and 2. The function finds leaf 6 and continues to explore 2, down to leaf 7.

2. Simultaneously, it explores the right child 1 of the root node 3 which is also a leaf node. Here, the dfs function collects the leaf value 1. The sequence for Tree1 is now [6, 7, 1].

3. For Tree2, the dfs function starts at the root node 5, then traverses the left child 3. Node 3 is a leaf so it records the value 3 and stops moving further down this branch.

4. Afterwards, it explores the right side of 5, going down to 6 which is a leaf node. It also records 7 which is the left child leaf node of node 6. The sequence for Tree2 is [3, 7, 6].

5. After collecting the leaf sequences from both trees using DFS, the solution now compares the leaf value sequences of both trees: [6, 7, 1] for Tree1 and [3, 7, 6] for Tree2.

6. Since the leaf sequences are not identical (since [6, 7, 1] is not the same as [3, 7, 6]), our function will return False, indicating that Tree1 and Tree2 are not leaf-similar.

Following this approach, we can see that the DFS algorithm efficiently finds the leaf sequences for both trees, and the comparison step at the end of the process is a simple equality check. This example demonstrates the solution approach on a smaller scale, which is directly applicable to larger and more complex tree structures.

Solution Implementation

Time and Space Complexity

The provided Python code defines a function leafSimilar that takes the roots of two binary trees and returns True if the trees are "leaf similar," which means they have the same sequence of leaf values when traversed in depth-first order.

Time Complexity

The time complexity of the algorithm is determined by the depth-first search (DFS) function, which visits every node in the binary tree exactly once. Therefore, the time complexity is O(N+M), where N is the number of nodes in the first tree and M is the number of nodes in the second tree.

The DFS function is applied to both trees separately, so we traverse every node of both trees once, leading to the combined time complexity.

Space Complexity

The space complexity is mainly governed by the call stack of the recursive DFS calls and the space used to store the leaf values. In the worst case, the depth of the recursion could be O(H1+H2), where H1 is the maximum height of the first tree and H2 is the maximum height of the second tree, if the trees are highly skewed.

Additionally, the space required to store the leaf values is equal to the total number of leaves in both trees. However, since the DFS function concatenates lists and returns them, if we consider the output as a part of the space complexity, it would be equal to the total number of nodes (like a post-order traversal), which in the worst case would be O(N+M), where both trees are full trees, and each node is stored in the list exactly once.

If the output space is not considered in the space complexity (which is common in analysis), then we mainly consider the space taken by the call stack, leading to the space complexity of O(H1+H2).

Thus, the space complexity of the algorithm depends on whether the output space is included, making it either O(H1+H2) if not (recursive call stack only) or O(N+M) if yes (including the output list).

Learn more about how to find time and space complexity quickly using problem constraints.