How do you solve Create Binary Tree From Descriptions on LeetCode?

Create Binary Tree From Descriptions (LeetCode #2196) can be solved using Brute Force or Optimal Solution. The optimal approach is Optimal Solution with O(n) time complexity and O(n) space complexity. Key insight: The root node is the only node that does not appear as a child.

What is the brute force solution for Create Binary Tree From Descriptions?

The brute force approach for Create Binary Tree From Descriptions is Brute Force, with O(n²) time complexity and O(1) space complexity. In the brute force approach, we can create a binary tree by iterating through the descriptions and inserting each child into the tree. This is straightforward but inefficient because we may need to search for the parent node each time we insert a child. The optimal Optimal Solution approach improves this to O(n).

What algorithm or data structure is used to solve Create Binary Tree From Descriptions?

Create Binary Tree From Descriptions uses the following concepts: Array, Hash Table, Tree, Binary Tree. The recommended patterns to study are: Hash Map, Array.

What are the interview tips for Create Binary Tree From Descriptions?

Clearly explain your thought process as you build the tree. Consider edge cases, such as trees with only one node. Practice visualizing tree structures to better understand parent-child relationships.

What are common mistakes when solving Create Binary Tree From Descriptions?

Not tracking child nodes correctly, leading to incorrect root identification. Overcomplicating the tree construction process.

#2196

Create Binary Tree From Descriptions

Medium

ArrayHash TableTreeBinary TreeHash MapArray

LeetCode ↗

Approaches

Brute ForceOptimal

Complexity Comparison

	Brute Force	Optimal Solution★
Time	O(n²)	O(n)
Space	O(1)	O(n)

💡

Intuition

Time O(n)Space O(n)

The optimal approach utilizes a hashmap to store nodes and a set to track children, allowing us to efficiently build the tree in a single pass and quickly identify the root node.

⚙️

Algorithm

3 steps

1Step 1: Initialize a hashmap to store TreeNode objects and a set to track all child nodes.
2Step 2: For each description, create the parent and child nodes if they don't exist, and link them based on isLeft.
3Step 3: After processing all descriptions, find the root node (the one not present in the child set).

solution.py22 lines

1class TreeNode:
2    def __init__(self, val=0, left=None, right=None):
3        self.val = val
4        self.left = left
5        self.right = right
6
7def createBinaryTree(descriptions):
8    nodes = {}
9    children = set()
10    for parent, child, isLeft in descriptions:
11        if parent not in nodes:
12            nodes[parent] = TreeNode(parent)
13        if child not in nodes:
14            nodes[child] = TreeNode(child)
15        if isLeft:
16            nodes[parent].left = nodes[child]
17        else:
18            nodes[parent].right = nodes[child]
19        children.add(child)
20    for node in nodes:
21        if node not in children:
22            return nodes[node]

ℹ

Complexity note: The time complexity is O(n) because we process each description exactly once, and the space complexity is O(n) due to storing the nodes in a hashmap.

1The root node is the only node that does not appear as a child.
2Using a hashmap allows for quick access and insertion of nodes.

Solutions and explanations are original Tejav content. Problem titles © LeetCode — use the LeetCode button above for the full problem statement.