How do you solve Find Largest Value in Each Tree Row on LeetCode?

Find Largest Value in Each Tree Row (LeetCode #515) 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 BFS allows us to efficiently traverse the tree level by level.

What is the time complexity of Find Largest Value in Each Tree Row?

The optimal solution for Find Largest Value in Each Tree Row runs in O(n) time and uses O(n) space. The complexity is O(n) because we visit each node exactly once, and O(n) space is used for the queue in the worst case (a complete binary tree).

What is the brute force solution for Find Largest Value in Each Tree Row?

The brute force approach for Find Largest Value in Each Tree Row is Brute Force, with O(n²) time complexity and O(1) space complexity. The brute force approach involves traversing each level of the tree and finding the maximum value at that level. This is straightforward but inefficient as it requires multiple passes through the tree. The optimal Optimal Solution approach improves this to O(n).

What algorithm or data structure is used to solve Find Largest Value in Each Tree Row?

Find Largest Value in Each Tree Row uses the following concepts: Tree, Depth-First Search, Breadth-First Search, Binary Tree. The recommended patterns to study are: Breadth-First Search, Tree Traversal.

What are the interview tips for Find Largest Value in Each Tree Row?

Always clarify the problem requirements and constraints. Discuss your thought process and approach before coding. Test your code with different tree structures to ensure correctness.

What are common mistakes when solving Find Largest Value in Each Tree Row?

Not handling edge cases like an empty tree. Forgetting to enqueue child nodes during traversal.

#515

Find Largest Value in Each Tree Row

Medium

TreeDepth-First SearchBreadth-First SearchBinary TreeBreadth-First SearchTree Traversal

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 solution uses a breadth-first search (BFS) approach to traverse the tree level by level while keeping track of the maximum value at each level. This is efficient and avoids unnecessary repeated traversals.

⚙️

Algorithm

4 steps

1Step 1: Initialize a queue with the root node.
2Step 2: While the queue is not empty, determine the number of nodes at the current level.
3Step 3: For each node, update the maximum value for the current level and enqueue its children.
4Step 4: After processing all nodes at the current level, add the maximum value to the result list.

solution.py20 lines

1# Full working Python code
2from collections import deque
3
4def largestValues(root):
5    if not root:
6        return []
7    result = []
8    queue = deque([root])
9    while queue:
10        level_size = len(queue)
11        max_value = float('-inf')
12        for _ in range(level_size):
13            node = queue.popleft()
14            max_value = max(max_value, node.val)
15            if node.left:
16                queue.append(node.left)
17            if node.right:
18                queue.append(node.right)
19        result.append(max_value)
20    return result

ℹ

Complexity note: The complexity is O(n) because we visit each node exactly once, and O(n) space is used for the queue in the worst case (a complete binary tree).

1Using BFS allows us to efficiently traverse the tree level by level.
2Tracking the maximum value at each level is crucial for the solution.

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