Understand what the interviewer is asking for by using test cases and questions about the problem.
- Established a set (2-3) of test cases to verify their own solution later.
- Established a set (1-2) of edge cases to verify their solution handles complexities.
- Have fully understood the problem and have no clarifying questions.
- Have you verified any Time/Space Constraints for this problem?
HAPPY CASE Input: root = [3,9,20,null,null,15,7] Output: 2
Input: root = [2,null,3,null,4,null,5,null,6] Output: 5 EDGE CASE Input: root =  Output: 0
Match what this problem looks like to known categories of problems, e.g. Linked List or Dynamic Programming, and strategies or patterns in those categories.
If you are dealing with Binary Trees some common techniques you can employ to help you solve the problem:
Plan the solution with appropriate visualizations and pseudocode.
General Idea: Recursively ask for child height in tree and add one for parent. Run recursive function on nodes only to check leaves only.
1. Basecase is a Null Node, return 0 2. Recursively check the minimum height from a node a.If both children check min between depths b.If single child check depth of child c.If leaf return it's height.
⚠️ Common Mistakes
Implement the code to solve the algorithm.
# Definition for a binary tree node. # class TreeNode: # def __init__(self, val=0, left=None, right=None): # self.val = val # self.left = left # self.right = right class Solution: def minDepth(self, root: Optional[TreeNode]) -> int: # Basecase is a Null Node, return 0 if not root: return 0 # Recursively check the minimum height from a node if root.left and root.right: # If both children check min between depths return min(self.minDepth(root.left), self.minDepth(root.right)) + 1 elif root.right: # If single child check depth of child return self.minDepth(root.right) + 1 elif root.left: # If single child check depth of child return self.minDepth(root.left) + 1 else: # If leaf return it's height. return 1
Review the code by running specific example(s) and recording values (watchlist) of your code's variables along the way.
Evaluate the performance of your algorithm and state any strong/weak or future potential work.
N represents the number of nodes in binary tree and
H represents the height of the binary tree.
O(N), because we need to visit each node in the binary tree
O(N)because we need to account for unbalanced tree (space is used for the recursion stack). In the general case
O(logN)for balanced tree.