Classic Tree algorithm question from Uber interviews. This article demonstrates flexible application of Inorder Traversal and optimization techniques via the "Kth Largest Element in BST" problem. oavoservice helps you master tree algorithms.
📋 Problem Definition
Given a Binary Search Tree (BST) and an integer k, find the kth largest element in the tree.
Constraints:
- 1 <= k <= BST node count
- Optimize space and time complexity
🎯 Solution Evolution
Method 1: Inorder Traversal + Array
def kthLargest_v1(root, k):
def inorder(node):
if not node:
return []
return inorder(node.left) + [node.val] + inorder(node.right)
sorted_vals = inorder(root)
return sorted_vals[-k]
Time: O(n) Space: O(n)
Method 2: Reverse Inorder Traversal (Optimal)
oavoservice recommended optimized approach:
def kthLargest_v2(root, k):
def reverse_inorder(node):
nonlocal k, result
if not node or result is not None:
return
reverse_inorder(node.right)
k -= 1
if k == 0:
result = node.val
return
reverse_inorder(node.left)
result = None
reverse_inorder(root)
return result
Time: O(k) Space: O(h)
Method 3: Morris Traversal (Space O(1))
def kthLargest_morris(root, k):
current = root
count = 0
result = None
while current:
if not current.right:
count += 1
if count == k:
result = current.val
current = current.left
else:
predecessor = current.right
while predecessor.left and predecessor.left != current:
predecessor = predecessor.left
if not predecessor.left:
predecessor.left = current
current = current.right
else:
predecessor.left = None
count += 1
if count == k:
result = current.val
current = current.left
return result
Time: O(n) Space: O(1)
💼 How oavoservice Helps
Multiple Solutions - From basic to optimal Space Optimization - Morris Traversal Time Optimization - Early termination
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Tags: #Uber #BST #TreeTraversal #MorrisTraversal #VOHelp #InterviewPrep #1point3acres
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