How do you solve Complete Binary Tree Inserter on LeetCode?

Complete Binary Tree Inserter (LeetCode #919) 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: Using a queue allows efficient tracking of the next insertion point.

What is the brute force solution for Complete Binary Tree Inserter?

The brute force approach for Complete Binary Tree Inserter is Brute Force, with O(n²) time complexity and O(1) space complexity. The brute force approach involves traversing the entire tree to find the correct position for the new node. This ensures that we maintain the complete binary tree property but is inefficient. The optimal Optimal Solution approach improves this to O(n).

What algorithm or data structure is used to solve Complete Binary Tree Inserter?

Complete Binary Tree Inserter uses the following concepts: Tree, Breadth-First Search, Design, Binary Tree. The recommended patterns to study are: Breadth-First Search, Queue.

What are the interview tips for Complete Binary Tree Inserter?

Clarify the definition of a complete binary tree before starting. Discuss your thought process and approach before jumping into coding. Test your code with edge cases, such as inserting into a tree with only one node.

What are common mistakes when solving Complete Binary Tree Inserter?

Not properly managing the queue state after each insertion. Failing to maintain the complete property of the binary tree.

#919

Complete Binary Tree Inserter

Medium

TreeBreadth-First SearchDesignBinary TreeBreadth-First SearchQueue

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 uses a queue to keep track of the nodes at the current level. This allows us to efficiently find the parent node for insertion without traversing the entire tree.

⚙️

Algorithm

3 steps

1Step 1: Initialize a queue with the root node.
2Step 2: For each insertion, check the front of the queue to find the parent node.
3Step 3: Insert the new node as a left or right child of the parent node and update the queue accordingly.

solution.py34 lines

1# Full working Python code
2class TreeNode:
3    def __init__(self, val=0, left=None, right=None):
4        self.val = val
5        self.left = left
6        self.right = right
7
8class CBTInserter:
9    def __init__(self, root: TreeNode):
10        self.root = root
11        self.queue = [root]
12        while self.queue:
13            node = self.queue[0]
14            if node.left:
15                self.queue.append(node.left)
16            if node.right:
17                self.queue.append(node.right)
18            if not node.left or not node.right:
19                break
20            self.queue.pop(0)
21
22    def insert(self, v: int) -> int:
23        new_node = TreeNode(v)
24        parent = self.queue[0]
25        if not parent.left:
26            parent.left = new_node
27        else:
28            parent.right = new_node
29            self.queue.pop(0)
30        self.queue.append(new_node)
31        return parent.val
32
33    def get_root(self) -> TreeNode:
34        return self.root

ℹ

Complexity note: The time complexity is O(n) for the insert operation due to the queue operations, but since we are not traversing the entire tree for each insertion, it is efficient. The space complexity is O(n) due to the queue storing nodes.

1Using a queue allows efficient tracking of the next insertion point.
2Maintaining the complete binary tree property is crucial for performance.

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