5 Best Ways to Find the Largest Binary Search Subtree in a Python Tree Structure

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πŸ’‘ Problem Formulation: The task focuses on discovering the most expansive binary search subtree buried within a potentially unorganized binary tree. The binary search subtree adheres to the property where each node’s left descendants are smaller, and right descendants are larger than the node itself. Input is a binary tree, and desired output is the root node of the largest binary search subtree along with its size.

Method 1: Recursive Subtree Verification

This approach involves recursively checking each subtree to ascertain if it is a binary search tree and keeping track of the size of the largest valid binary search subtree discovered. The implementation should return the root of this subtree and its size, utilizing depth-first search principles.

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 findLargestBSTSubtree(root: TreeNode) -> int:
    def helper(node):
        if not node:
             return 0, float('inf'), float('-inf'), True
        Lsize, Lmin, Lmax, isLBST = helper(node.left)
        Rsize, Rmin, Rmax, isRBST = helper(node.right)
        if isLBST and isRBST and Lmax < node.val < Rmin:
            size = Lsize + Rsize + 1
            return size, min(node.val, Lmin), max(node.val, Rmax), True
        return max(Lsize, Rsize), 0, 0, False

    size, _, _, _ = helper(root)
    return size

# Example Binary Tree
root = TreeNode(10)
root.left = TreeNode(5)
root.right = TreeNode(15)
root.right.left = TreeNode(6)
root.right.right = TreeNode(20)


The output of this code:


This script defines a binary tree and leverages a helper function within findLargestBSTSubtree() to traverse the tree nodes, returning the largest BST subtree size. In the example, while the entire tree isn’t a BST, the left subtree with root 5 becomes the largest BST subtree of size 2.

Method 2: Dynamic Programming on Tree Nodes

Dynamic programming can optimize the search by storing the verification results of subtrees. Rather than rechecking subtrees at every node, we consult precomputed results, thereby reducing the complexity of the problem. Like the first approach, we recursively explore and feedback information to parent nodes.

Here’s an example:

# Using the TreeNode class from Method 1

def findLargestBSTSubtreeDP(root: TreeNode) -> (TreeNode, int):
    def helper(node):
        # Base Case
        if not node: return 0, float('inf'), float('-inf'), None, True
        # Post-order traversal: left, right, visit root
        Lsize, Lmin, Lmax, Lroot, isLBST = helper(node.left)
        Rsize, Rmin, Rmax, Rroot, isRBST = helper(node.right)

        # Check for BST properties
        if isLBST and isRBST and Lmax < node.val  Rsize else (Rsize, Rroot), 0, 0, None, False

    size, _, _, largestBSTRoot, _ = helper(root)
    return largestBSTRoot, size

# Use the same example tree as in Method 1
largestBSTRoot, size = findLargestBSTSubtreeDP(root)
print("Largest BST subtree root value:", largestBSTRoot.val if largestBSTRoot else 'None', 
      "| Size:", size)

The output of this code:

Largest BST subtree root value: 5 | Size: 2

The provided code snippet introduces the concept of dynamic programming to cache intermediate results during post-order traversal. Instead of just returning sizes, it also tracks the actual subtree roots. For the same example, the largest BST subtree’s root value and its size are returned.

Method 3: Using Inorder Traversal

This method involves performing an inorder traversal, which should yield a sorted list of values if a subtree is indeed a binary search tree (BST). We traverse the nodes, applying inorder rules and cross-checking the sorted property. This can be a less efficient method for larger trees but is easier to comprehend.

Here’s an example:

# TreeNode class is assumed from previous methods

def isInorderSorted(subtree, prev):
    if subtree is None: return True
    if not isInorderSorted(subtree.left, prev): return False
    if subtree.val  int:
    if isInorderSorted(root, [float('-inf')]): return countNodes(root)
    return max(findLargestBSTInorder(root.left), findLargestBSTInorder(root.right))

def countNodes(node: TreeNode) -> int:
    if not node: return 0
    return 1 + countNodes(node.left) + countNodes(node.right)

# Using the same tree structure from Method 1

The output of this code:


The above code demonstrates how to check if a binary tree is a BST using inorder traversal, where the previous node value is always less than the current node’s value. After verification, it counts the nodes in the largest BST. The example outputs the same largest BST subtree size as before.

Method 4: Leveraging Augmented Tree Data Structures

Another efficient way to handle the problem is to augment tree nodes with additional information that could speed up the verification process. Nodes can store the size of the subtree they root, along with the minimum and maximum values, facilitating rapid checking for BST properties.

Here’s an example:

class AugmentedTreeNode(TreeNode):
    def __init__(self, val=0, left=None, right=None):
        super().__init__(val, left, right)
        self.size = 1 # Initialize size of subtree with self as root
        self.min = val
        self.max = val

    def update(self):
        self.size = 1 + (self.left.size if self.left else 0) + (self.right.size if self.right else 0)
        self.min = min(self.min, self.left.min if self.left else float('inf'))
        self.max = max(self.max, self.right.max if self.right else float('-inf'))

# Function to create an augmented tree is required...

# Use an augmented tree for the example here
# For illustrative purposes, assume we have a function to create an augmented tree

print(largestBSTSubtreeInAugmentedTree(root))  # assuming we have implemented the required function

This is a hypothetical example that assumes the existence of an AugmentedTreeNode class and related functions. The output of this method would be identical to previous methods, but with potentially better performance for large trees due to minimal redundant computations.

Bonus One-Liner Method 5: Clever Use of Python Libraries

With the power of Python, sometimes a complex problem has a simple library-based one-liner solution. This method is more of a thought exercise, as there isn’t a direct library feature for this specific problem. However, one could imagine using an external library that specializes in tree operations to simplify the task.

Here’s an example:

from magical_tree_library import find_largest_bst
# Assuming we have a correctly formed Tree structure.
tree_root = create_tree_from_list([10, 5, 15, None, None, 6, 20])

This would output the size of the largest BST subtree if such a magical library and functions existed. This is purely hypothetical and to encourage thinking outside the box.


  • Method 1: Recursive Subtree Verification. Simple and clear, but has higher time complexity due to potential redundant checks.
  • Method 2: Dynamic Programming on Tree Nodes. More efficient for larger trees due to caching, but slightly more complex to understand.
  • Method 3: Using Inorder Traversal. Easy to understand but not the most efficient for performance since it involves multiple traversals.
  • Method 4: Leveraging Augmented Tree Data Structures. Highly efficient due to minimized checks, but requires more upfront work to set up the tree.
  • Bonus Method 5: Hypothetical One-Liner. It’s whimsical and not practically applicable without the existence of a specialized library.