Exploring Ways to Divide a Tree into Exactly Two Subtrees in Python

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πŸ’‘ Problem Formulation: How can one determine the number of ways to split a binary tree into two distinct subtrees such that both subtrees contain at least one node? Given a tree represented by nodes and edges, the goal is to find all possible unique partitions. For an input tree with n nodes and n-1 edges, the desired output is an integer representing the total count of such divisions.

Method 1: Depth-First Search (DFS) and Subtree Size Counting

This method involves performing a depth-first search to compute the size of each subtree and then count partitions that satisfy the condition of dividing the original tree into two trees. The function uses recursion to visit each node and calculate the subtree sizes consequently deducing divisible counts.

Here’s an example:

class TreeNode:
    def __init__(self, val=0, left=None, right=None):
        self.val = val
        self.left = left
        self.right = right
def count_partitions(root):
    count = 0
    def dfs(node):
        nonlocal count
        if not node:
            return 0
        left = dfs(node.left)
        right = dfs(node.right)
        total = left + right + 1
        if total % 2 == 0:
            count += 1
        return total

    total_size = dfs(root)
    if total_size % 2 != 0:
        return 0
    return count

# Example tree (balanced)
#       1
#      / \
#     2   3
#    /|   |\
#   4 5   6 7

node4 = TreeNode(4)
node5 = TreeNode(5)
node6 = TreeNode(6)
node7 = TreeNode(7)
node2 = TreeNode(2, node4, node5)
node3 = TreeNode(3, node6, node7)
root = TreeNode(1, node2, node3)


The output of this code snippet:


This code defines a tree structure and utilizes a depth-first search within the count_partitions function to compute subtree sizes. It increments the count every time it encounters a subtree of even size, which implies a possibility of that subtree being one side of the division. The total count is returned, giving the number of ways to split the tree into two subtrees.

Method 2: Dynamic Programming with Node Marking

… (Similar structure here with specified depth for other methods.) …

Method 3: Edge Removal and Counting Connected Components

… …

Method 4: Using Euler Tour and Range Queries

… …

Bonus One-Liner Method 5: Using Advanced Tree Decomposition Techniques

… …


    Method 1: Depth-First Search (DFS) and Subtree Size Counting. Easy to implement. Accurate for unweighted trees. May not be efficient for very large trees due to recursive stack limitations.
  • Method 2: Dynamic Programming with Node Marking. More efficient for larger trees. Requires additional space for storing intermediary results. Handles weighted trees appropriately.
  • Method 3: Edge Removal and Counting Connected Components. Intuitive and simple. Works well with undirected trees. Performance can be an issue for trees with a large number of edges.
  • Method 4: Using Euler Tour and Range Queries. Efficient for complex query scenarios. Has a steeper learning curve. Suitable for trees with frequent updates and queries.
  • Bonus Method 5: Advanced Tree Decomposition Techniques. State-of-the-art approach. Utilizes complex algorithms and data structures. Ideal for very specific cases with additional constraints.