Binary TreesBinary Trees36
  1. 1Preorder Traversal of a Binary Tree using Recursion
  2. 2Preorder Traversal of a Binary Tree using Iteration
  3. 3Inorder Traversal of a Binary Tree using Recursion
  4. 4Inorder Traversal of a Binary Tree using Iteration
  5. 5Postorder Traversal of a Binary Tree Using Recursion
  6. 6Postorder Traversal of a Binary Tree using Iteration
  7. 7Level Order Traversal of a Binary Tree using Recursion
  8. 8Level Order Traversal of a Binary Tree using Iteration
  9. 9Reverse Level Order Traversal of a Binary Tree using Iteration
  10. 10Reverse Level Order Traversal of a Binary Tree using Recursion
  11. 11Find Height of a Binary Tree
  12. 12Find Diameter of a Binary Tree
  13. 13Find Mirror of a Binary Tree
  14. 14Left View of a Binary Tree
  15. 15Right View of a Binary Tree
  16. 16Top View of a Binary Tree
  17. 17Bottom View of a Binary Tree
  18. 18Zigzag Traversal of a Binary Tree
  19. 19Check if a Binary Tree is Balanced
  20. 20Diagonal Traversal of a Binary Tree
  21. 21Boundary Traversal of a Binary Tree
  22. 22Construct a Binary Tree from a String with Bracket Representation
  23. 23Convert a Binary Tree into a Doubly Linked List
  24. 24Convert a Binary Tree into a Sum Tree
  25. 25Find Minimum Swaps Required to Convert a Binary Tree into a BST
  26. 26Check if a Binary Tree is a Sum Tree
  27. 27Check if All Leaf Nodes are at the Same Level in a Binary Tree
  28. 28Lowest Common Ancestor (LCA) in a Binary Tree
  29. 29Solve the Tree Isomorphism Problem
  30. 30Check if a Binary Tree Contains Duplicate Subtrees of Size 2 or More
  31. 31Check if Two Binary Trees are Mirror Images
  32. 32Calculate the Sum of Nodes on the Longest Path from Root to Leaf in a Binary Tree
  33. 33Print All Paths in a Binary Tree with a Given Sum
  34. 34Find the Distance Between Two Nodes in a Binary Tree
  35. 35Find the kth Ancestor of a Node in a Binary Tree
  36. 36Find All Duplicate Subtrees in a Binary Tree
GraphsGraphs46
  1. 1Breadth-First Search in Graphs
  2. 2Depth-First Search in Graphs
  3. 3Number of Provinces in an Undirected Graph
  4. 4Connected Components in a Matrix
  5. 5Rotten Oranges Problem - BFS in Matrix
  6. 6Flood Fill Algorithm - Graph Based
  7. 7Detect Cycle in an Undirected Graph using DFS
  8. 8Detect Cycle in an Undirected Graph using BFS
  9. 9Distance of Nearest Cell Having 1 - Grid BFS
  10. 10Surrounded Regions in Matrix using Graph Traversal
  11. 11Number of Enclaves in Grid
  12. 12Word Ladder - Shortest Transformation using Graph
  13. 13Word Ladder II - All Shortest Transformation Sequences
  14. 14Number of Distinct Islands using DFS
  15. 15Check if a Graph is Bipartite using DFS
  16. 16Topological Sort Using DFS
  17. 17Topological Sort using Kahn's Algorithm
  18. 18Cycle Detection in Directed Graph using BFS
  19. 19Course Schedule - Task Ordering with Prerequisites
  20. 20Course Schedule 2 - Task Ordering Using Topological Sort
  21. 21Find Eventual Safe States in a Directed Graph
  22. 22Alien Dictionary Character Order
  23. 23Shortest Path in Undirected Graph with Unit Distance
  24. 24Shortest Path in DAG using Topological Sort
  25. 25Dijkstra's Algorithm Using Set - Shortest Path in Graph
  26. 26Dijkstra’s Algorithm Using Priority Queue
  27. 27Shortest Distance in a Binary Maze using BFS
  28. 28Path With Minimum Effort in Grid using Graphs
  29. 29Cheapest Flights Within K Stops - Graph Problem
  30. 30Number of Ways to Reach Destination in Shortest Time - Graph Problem
  31. 31Minimum Multiplications to Reach End - Graph BFS
  32. 32Bellman-Ford Algorithm for Shortest Paths
  33. 33Floyd Warshall Algorithm for All-Pairs Shortest Path
  34. 34Find the City With the Fewest Reachable Neighbours
  35. 35Minimum Spanning Tree in Graphs
  36. 36Prim's Algorithm for Minimum Spanning Tree
  37. 37Disjoint Set (Union-Find) with Union by Rank and Path Compression
  38. 38Kruskal's Algorithm - Minimum Spanning Tree
  39. 39Minimum Operations to Make Network Connected
  40. 40Most Stones Removed with Same Row or Column
  41. 41Accounts Merge Problem using Disjoint Set Union
  42. 42Number of Islands II - Online Queries using DSU
  43. 43Making a Large Island Using DSU
  44. 44Bridges in Graph using Tarjan's Algorithm
  45. 45Articulation Points in Graphs
  46. 46Strongly Connected Components using Kosaraju's Algorithm

Lowest Common Ancestor in BST - Algorithm, Visualization, Code Examples

Problem Statement

Given the root of a Binary Search Tree (BST) and two nodes p and q, find the lowest common ancestor (LCA) of the two nodes. The lowest common ancestor is the deepest node that has both p and q as descendants (a node can be a descendant of itself).

Assumptions:

  • All node values are unique.
  • p and q are guaranteed to exist in the BST.

Examples

Input Tree Node 1 Node 2 Lowest Common Ancestor Description
[20, 10, 30, 5, 15, 25, 35]
5 15 10 Both nodes lie in the left subtree of 20; 10 is their direct parent.
[20, 10, 30, 5, 15, 25, 35]
5 35 20 Nodes are on opposite sides of root (left and right); LCA is the root 20.
[20, 10, 30, 5, 15, null, null]
15 30 20 15 is in left subtree, 30 in right; LCA is the first split node: 20.
[10, 5, 15, null, null, null, 20]
15 20 15 20 is a right child of 15, so LCA is 15 itself.
[6, 2, 8, 0, 4, 7, 9, null, null, 3, 5]
3 5 4 3 and 5 are children of 4; LCA is their direct parent node 4.

Solution

Case 1: General Case – Nodes on Different Sides

In a BST, if one node lies in the left subtree and the other in the right, then the current node is the lowest common ancestor. For example, in the tree [6,2,8,0,4,7,9,null,null,3,5], if we look for LCA of 2 and 8, they lie on different sides of 6. So, 6 is the lowest node that connects both branches — the LCA.

Case 2: One Node is Ancestor of the Other

Sometimes, one of the given nodes is actually the ancestor of the other. In BST terms, if both p and q are on the same side and one is directly in the path of the other, then the upper node is the LCA. For instance, in the same tree, the LCA of nodes 2 and 4 is 2 — since 4 is a descendant of 2 and BST traversal leads us to 2 before 4.

Case 3: Both Nodes Are the Same

In this special case, when p == q, the lowest common ancestor is the node itself. The BST structure doesn’t matter in this case — if you are asked for the LCA of a node with itself, the answer is trivially that node.

Case 4: Tree is Empty

If the root of the BST is null, the tree doesn’t contain any nodes. No matter what values p and q are, the answer will be null because there's no structure to even begin a search. This case should be handled upfront to avoid null pointer issues during traversal.

Algorithm Steps

  1. Given a binary search tree (BST) and two nodes, p and q.
  2. Start at the root of the BST.
  3. If both p and q are less than the current node, move to the left child.
  4. If both p and q are greater than the current node, move to the right child.
  5. Otherwise, the current node is the lowest common ancestor (LCA) of p and q.

Code

Python
Java
JavaScript
C
C++
C#
Kotlin
Swift
Go
Php
class TreeNode:
    def __init__(self, val=0, left=None, right=None):
        self.val = val
        self.left = left
        self.right = right

def lowestCommonAncestor(root, p, q):
    current = root
    while current:
        if p.val < current.val and q.val < current.val:
            current = current.left
        elif p.val > current.val and q.val > current.val:
            current = current.right
        else:
            return current
    return None

# Example usage:
if __name__ == '__main__':
    # Construct BST:
    #         6
    #        / \
    #       2   8
    #      / \  / \
    #     0  4 7   9
    root = TreeNode(6)
    root.left = TreeNode(2)
    root.right = TreeNode(8)
    root.left.left = TreeNode(0)
    root.left.right = TreeNode(4)
    root.right.left = TreeNode(7)
    root.right.right = TreeNode(9)
    p = root.left           # Node with value 2
    q = root.left.right     # Node with value 4
    lca = lowestCommonAncestor(root, p, q)
    print(lca.val)  # Output should be 2