LCA in BST - Lowest Common Ancestor in Binary Search Tree
Last Updated :
23 Jul, 2025
Given two nodes n1 and n2 of a Binary Search Tree, find the Lowest Common Ancestor (LCA). You may assume that both values exist in the tree.
The Lowest Common Ancestor between two nodes n1 and n2 is defined as the lowest node that has both n1 and n2 as descendants (where we allow a node to be a descendant of itself). In other words, the LCA of n1 and n2 is the shared ancestor of n1 and n2 that is located farthest from the root [i.e., closest to n1 and n2].
Examples:
Input Tree:
Input: LCA of 10 and 14
Output: 12
Explanation: 12 is the closest node to both 10 and 14, which is also an ancestor of both the nodes.
Input: LCA of 8 and 14
Output: 8
Explanation: 8 is the closest node to both 8 and 14, which is also an ancestor of both the nodes.
LCA of Normal Binary Tree Methods - O(n) Time
We can use all the methods discussed in Lowest Common Ancestor in a Binary Tree. The methods discussed there give us the best possible time complexity as O(n) where n is the number of nodes in BST. We can do better if we use BST properties.
Using BST Properties (Recursive Approach) - O(h) Time and O(h) Space
In a Binary search tree, while traversing the tree from top to bottom the first node which lies in between the two numbers n1 and n2 is the LCA of the nodes, i.e. the first node n with the lowest depth which lies in between n1 and n2 (n1 <= n <= n2, assumingn1 < n2).
So just recursively traverse the BST , if node's value is greater than both n1 and n2 then our LCA lies in the left side of the node, if it is smaller than both n1 and n2, then LCA lies on the right side. Otherwise, the root is LCA (assuming that both n1 and n2 are present in BST).
C++
//Driver Code Starts
// C++ program to find LCA of given node in BST
// Using Properties of BST and Recursion
#include <iostream>
using namespace std;
class Node {
public:
int data;
Node* left;
Node* right;
Node(int val) {
data = val;
left = right = nullptr;
}
};
//Driver Code Ends
// Function to find LCA of nodes n1 and n2, assuming
// both are present in the BST
Node* LCA(Node* root, Node* n1, Node* n2) {
if (root == nullptr)
return nullptr;
// If both n1 and n2 are smaller than
// root, go to left subtree
if (root->data > n1->data && root->data > n2->data)
return LCA(root->left, n1, n2);
// If both n1 and n2 are greater than
// root, go to right subtree
if (root->data < n1->data && root->data < n2->data)
return LCA(root->right, n1, n2);
// If nodes n1 and n2 are on the opposite sides,
// then root is the LCA
return root;
}
//Driver Code Starts
int main() {
// Representation of input BST:
// 20
// / \
// 8 22
// / \
// 4 12
// / \
// 10 14
Node* root = new Node(20);
root->left = new Node(8);
root->right = new Node(22);
root->left->left = new Node(4);
root->left->right = new Node(12);
root->left->right->left = new Node(10);
root->left->right->right = new Node(14);
Node* n1 = root->left->left; // Node 4
Node* n2 = root->left->right->right; // Node 14
Node* res = LCA(root, n1, n2);
cout << res->data << endl;
return 0;
}
//Driver Code Ends
C
//Driver Code Starts
// C program to find LCA of given node in BST
// Using Properties of BST and Recursion
#include <stdio.h>
#include <stdlib.h>
typedef struct Node {
int data;
struct Node* left;
struct Node* right;
} Node;
// Utility function to create a new node
Node* newNode(int val) {
Node* node = (Node*)malloc(sizeof(Node));
node->data = val;
node->left = node->right = NULL;
return node;
}
//Driver Code Ends
// Function to find LCA of nodes n1 and n2, assuming
// both are present in the BST
Node* LCA(Node* root, Node* n1, Node* n2) {
if (root == NULL)
return NULL;
// If both n1 and n2 are smaller than root,
// go to left subtree
if (root->data > n1->data && root->data > n2->data)
return LCA(root->left, n1, n2);
// If both n1 and n2 are greater than root,
// go to right subtree
if (root->data < n1->data && root->data < n2->data)
return LCA(root->right, n1, n2);
// If nodes n1 and n2 are on the opposite sides,
// root is the LCA
return root;
}
//Driver Code Starts
int main() {
// Representation of input BST:
// 20
// / \
// 8 22
// / \
// 4 12
// / \
// 10 14
Node* root = newNode(20);
root->left = newNode(8);
root->right = newNode(22);
root->left->left = newNode(4);
root->left->right = newNode(12);
root->left->right->left = newNode(10);
root->left->right->right = newNode(14);
Node* n1 = root->left->left; // Node 4
Node* n2 = root->left->right->right; // Node 14
Node* res = LCA(root, n1, n2);
printf("%d
", res->data);
return 0;
}
//Driver Code Ends
Java
//Driver Code Starts
// Java program to find LCA of given node in BST
// Using Properties of BST and Recursion
class Node {
int data;
Node left, right;
Node(int val) {
data = val;
left = right = null;
}
}
class GfG {
//Driver Code Ends
// Function to find LCA of nodes n1 and n2, assuming
// both are present in the BST
static Node LCA(Node root, Node n1, Node n2) {
if (root == null)
return null;
// If both n1 and n2 are smaller than root,
// go to left subtree
if (root.data > n1.data && root.data > n2.data)
return LCA(root.left, n1, n2);
// If both n1 and n2 are greater than root,
// go to right subtree
if (root.data < n1.data && root.data < n2.data)
return LCA(root.right, n1, n2);
// If nodes n1 and n2 are on the opposite sides,
// root is the LCA
return root;
}
//Driver Code Starts
public static void main(String[] args) {
// Representation of input BST:
// 20
// / \
// 8 22
// / \
// 4 12
// / \
// 10 14
Node root = new Node(20);
root.left = new Node(8);
root.right = new Node(22);
root.left.left = new Node(4);
root.left.right = new Node(12);
root.left.right.left = new Node(10);
root.left.right.right = new Node(14);
Node n1 = root.left.left; // Node 4
Node n2 = root.left.right.right; // Node 14
Node res = LCA(root, n1, n2);
System.out.println(res.data);
}
}
//Driver Code Ends
Python
#Driver Code Starts
# Python program to find LCA of given node in BST
# Using Properties of BST and Recursion
class Node:
def __init__(self, val):
self.data = val
self.left = None
self.right = None
#Driver Code Ends
# Function to find LCA of nodes n1 and n2, assuming
# both are present in the BST
def LCA(root, n1, n2):
if root is None:
return None
# If both n1 and n2 are smaller than root,
# go to left subtree
if root.data > n1.data and root.data > n2.data:
return LCA(root.left, n1, n2)
# If both n1 and n2 are greater than root,
# go to right subtree
if root.data < n1.data and root.data < n2.data:
return LCA(root.right, n1, n2)
# If nodes n1 and n2 are on the opposite sides,
# root is the LCA
return root
#Driver Code Starts
if __name__ == "__main__":
# Representation of input BST:
# 20
# / \
# 8 22
# / \
# 4 12
# / \
# 10 14
root = Node(20)
root.left = Node(8)
root.right = Node(22)
root.left.left = Node(4)
root.left.right = Node(12)
root.left.right.left = Node(10)
root.left.right.right = Node(14)
n1 = root.left.left # Node 4
n2 = root.left.right.right # Node 14
res = LCA(root, n1, n2)
print(res.data)
#Driver Code Ends
C#
//Driver Code Starts
// C# program to find LCA of given node in BST
// Using Properties of BST and Recursion
using System;
class Node {
public int data;
public Node left, right;
public Node(int val) {
data = val;
left = right = null;
}
}
class GfG {
//Driver Code Ends
// Function to find LCA of nodes n1 and n2, assuming
// both are present in the BST
static Node LCA(Node root, Node n1, Node n2) {
if (root == null)
return null;
// If both n1 and n2 are smaller than root,
// go to left subtree
if (root.data > n1.data && root.data > n2.data)
return LCA(root.left, n1, n2);
// If both n1 and n2 are greater than root,
// go to right subtree
if (root.data < n1.data && root.data < n2.data)
return LCA(root.right, n1, n2);
// If nodes n1 and n2 are on the opposite sides,
// root is the LCA
return root;
}
//Driver Code Starts
static void Main(string[] args) {
// Representation of input BST:
// 20
// / \
// 8 22
// / \
// 4 12
// / \
// 10 14
Node root = new Node(20);
root.left = new Node(8);
root.right = new Node(22);
root.left.left = new Node(4);
root.left.right = new Node(12);
root.left.right.left = new Node(10);
root.left.right.right = new Node(14);
Node n1 = root.left.left; // Node 4
Node n2 = root.left.right.right; // Node 14
Node res = LCA(root, n1, n2);
Console.WriteLine(res.data);
}
}
//Driver Code Ends
JavaScript
//Driver Code Starts
// JavaScript program to find LCA of given node in BST
// Using Properties of BST and Recursion
class Node {
constructor(val) {
this.data = val;
this.left = null;
this.right = null;
}
}
//Driver Code Ends
// Function to find LCA of nodes n1 and n2, assuming
// both are present in the BST
function LCA(root, n1, n2) {
if (root === null)
return null;
// If both n1 and n2 are smaller than root, go to left subtree
if (root.data > n1.data && root.data > n2.data)
return LCA(root.left, n1, n2);
// If both n1 and n2 are greater than root, go to right subtree
if (root.data < n1.data && root.data < n2.data)
return LCA(root.right, n1, n2);
// If nodes n1 and n2 are on the opposite sides, root is the LCA
return root;
}
//Driver Code Starts
// Driver Code
// Representation of input BST:
// 20
// / \
// 8 22
// / \
// 4 12
// / \
// 10 14
const root = new Node(20);
root.left = new Node(8);
root.right = new Node(22);
root.left.left = new Node(4);
root.left.right = new Node(12);
root.left.right.left = new Node(10);
root.left.right.right = new Node(14);
const n1 = root.left.left; // Node 4
const n2 = root.left.right.right; // Node 14
const res = LCA(root, n1, n2);
console.log(res.data);
//Driver Code Ends
Using BST Properties (Iterative Method) - O(h) Time and O(1) Space
The auxiliary space in the above method can be optimized by eliminating recursion. Below is the iterative implementation of this approach.
C++
// C++ program to find LCA of given node in BST
// Using Properties of BST and Iteration
#include <iostream>
using namespace std;
class Node {
public:
int data;
Node* left;
Node* right;
Node(int val) {
data = val;
left = right = nullptr;
}
};
// Function to find LCA of n1 and n2, assuming
// that both nodes n1 and n2 are present in BST
Node* LCA(Node* root, Node* n1, Node* n2) {
while (root != nullptr) {
// If both n1 and n2 are smaller than root,
// then LCA lies in left
if (root->data > n1->data && root->data > n2->data)
root = root->left;
// If both n1 and n2 are greater than root,
// then LCA lies in right
else if (root->data < n1->data && root->data < n2->data)
root = root->right;
// Else Ancestor is found
else
break;
}
return root;
}
int main() {
// Representation of input BST:
// 20
// / \
// 8 22
// / \
// 4 12
// / \
// 10 14
Node* root = new Node(20);
root->left = new Node(8);
root->right = new Node(22);
root->left->left = new Node(4);
root->left->right = new Node(12);
root->left->right->left = new Node(10);
root->left->right->right = new Node(14);
Node* n1 = root->left->left; // Node 4
Node* n2 = root->left->right->right; // Node 14
Node* res = LCA(root, n1, n2);
cout << res->data << endl;
return 0;
}
C
// C program to find LCA of given node in BST
// Using Properties of BST and Iteration
#include <stdio.h>
#include <stdlib.h>
// Definition of Node structure
typedef struct Node {
int data;
struct Node* left;
struct Node* right;
} Node;
// Function to create a new node
Node* createNode(int val) {
Node* newNode = (Node*)malloc(sizeof(Node));
newNode->data = val;
newNode->left = newNode->right = NULL;
return newNode;
}
// Function to find LCA of n1 and n2, assuming
// that both nodes n1 and n2 are present in BST
Node* LCA(Node* root, Node* n1, Node* n2) {
while (root != NULL) {
// If both n1 and n2 are smaller than root,
// then LCA lies in left
if (root->data > n1->data && root->data > n2->data)
root = root->left;
// If both n1 and n2 are greater than root,
// then LCA lies in right
else if (root->data < n1->data && root->data < n2->data)
root = root->right;
// Else Ancestor is found
else
break;
}
return root;
}
int main() {
// Representation of input BST:
// 20
// / \
// 8 22
// / \
// 4 12
// / \
// 10 14
Node* root = createNode(20);
root->left = createNode(8);
root->right = createNode(22);
root->left->left = createNode(4);
root->left->right = createNode(12);
root->left->right->left = createNode(10);
root->left->right->right = createNode(14);
Node* n1 = root->left->left; // Node 4
Node* n2 = root->left->right->right; // Node 14
Node* res = LCA(root, n1, n2);
printf("%d\n", res->data);
return 0;
}
Java
// Java program to find LCA of given node in BST
// Using Properties of BST and Iteration
class Node {
int data;
Node left, right;
Node(int val) {
data = val;
left = right = null;
}
}
class GfG {
// Function to find LCA of n1 and n2, assuming
// that both nodes n1 and n2 are present in BST
static Node LCA(Node root, Node n1, Node n2) {
while (root != null) {
// If both n1 and n2 are smaller than root,
// then LCA lies in left
if (root.data > n1.data && root.data > n2.data)
root = root.left;
// If both n1 and n2 are greater than root,
// then LCA lies in right
else if (root.data < n1.data && root.data < n2.data)
root = root.right;
// Else Ancestor is found
else
break;
}
return root;
}
public static void main(String[] args) {
// Representation of input BST:
// 20
// / \
// 8 22
// / \
// 4 12
// / \
// 10 14
Node root = new Node(20);
root.left = new Node(8);
root.right = new Node(22);
root.left.left = new Node(4);
root.left.right = new Node(12);
root.left.right.left = new Node(10);
root.left.right.right = new Node(14);
Node n1 = root.left.left; // Node 4
Node n2 = root.left.right.right; // Node 14
Node res = LCA(root, n1, n2);
System.out.println(res.data);
}
}
Python
# C++ program to find LCA of given node in BST
# Using Properties of BST and Iteration
class Node:
def __init__(self, data):
self.data = data
self.left = None
self.right = None
# Function to find LCA of n1 and n2, assuming
# that both nodes n1 and n2 are present in BST
def LCA(root, n1, n2):
while root:
# If both n1 and n2 are smaller than root,
# then LCA lies in left
if root.data > n1.data and root.data > n2.data:
root = root.left
# If both n1 and n2 are greater than root,
# then LCA lies in right
elif root.data < n1.data and root.data < n2.data:
root = root.right
# Else Ancestor is found
else:
break
return root
if __name__ == "__main__":
# Representation of input BST:
# 20
# / \
# 8 22
# / \
# 4 12
# / \
# 10 14
root = Node(20)
root.left = Node(8)
root.right = Node(22)
root.left.left = Node(4)
root.left.right = Node(12)
root.left.right.left = Node(10)
root.left.right.right = Node(14)
n1 = root.left.left # Node 4
n2 = root.left.right.right # Node 14
res = LCA(root, n1, n2)
print(res.data)
C#
// C# program to find LCA of given node in BST
// Using Properties of BST and Iteration
using System;
class Node {
public int data;
public Node left, right;
public Node(int val) {
data = val;
left = right = null;
}
}
class GfG {
// Function to find LCA of n1 and n2, assuming
// that both nodes n1 and n2 are present in BST
static Node LCA(Node root, Node n1, Node n2) {
while (root != null) {
// If both n1 and n2 are smaller than root,
// then LCA lies in left
if (root.data > n1.data && root.data > n2.data)
root = root.left;
// If both n1 and n2 are greater than root,
// then LCA lies in right
else if (root.data < n1.data && root.data < n2.data)
root = root.right;
// Else Ancestor is found
else
break;
}
return root;
}
static void Main(string[] args) {
// Representation of input BST:
// 20
// / \
// 8 22
// / \
// 4 12
// / \
// 10 14
Node root = new Node(20);
root.left = new Node(8);
root.right = new Node(22);
root.left.left = new Node(4);
root.left.right = new Node(12);
root.left.right.left = new Node(10);
root.left.right.right = new Node(14);
Node n1 = root.left.left; // Node 4
Node n2 = root.left.right.right; // Node 14
Node res = LCA(root, n1, n2);
Console.WriteLine(res.data);
}
}
JavaScript
// JavaScript program to find LCA of given node in BST
// Using Properties of BST and Iteration
class Node {
constructor(data) {
this.data = data;
this.left = null;
this.right = null;
}
}
// Function to find LCA of n1 and n2, assuming
// that both nodes n1 and n2 are present in BST
function LCA(root, n1, n2) {
while (root !== null) {
// If both n1 and n2 are smaller than root,
// then LCA lies in left
if (root.data > n1.data && root.data > n2.data)
root = root.left;
// If both n1 and n2 are greater than root,
// then LCA lies in right
else if (root.data < n1.data && root.data < n2.data)
root = root.right;
// Else Ancestor is found
else
break;
}
return root;
}
// Representation of input BST:
// 20
// / \
// 8 22
// / \
// 4 12
// / \
// 10 14
const root = new Node(20);
root.left = new Node(8);
root.right = new Node(22);
root.left.left = new Node(4);
root.left.right = new Node(12);
root.left.right.left = new Node(10);
root.left.right.right = new Node(14);
const n1 = root.left.left; // Node 4
const n2 = root.left.right.right; // Node 14
const res = LCA(root, n1, n2);
console.log(res.data);
How to handle cases when key(s) not present?
In a BST, we can search a key in O(h) time. So we can first explicitly search the keys and then do find LCA using the above method and still have time complexity as O(h).
Related Articles:
Lowest Common Ancestor in a Binary Tree, LCA using Parent Pointer, Find LCA in Binary Tree using RMQ
LCA in BST – Lowest Common Ancestor in Binary Search Tree
Visit Course
Similar Reads
Basics & Prerequisites
Data Structures
Array Data StructureIn this article, we introduce array, implementation in different popular languages, its basic operations and commonly seen problems / interview questions. An array stores items (in case of C/C++ and Java Primitive Arrays) or their references (in case of Python, JS, Java Non-Primitive) at contiguous
3 min read
String in Data StructureA string is a sequence of characters. The following facts make string an interesting data structure.Small set of elements. Unlike normal array, strings typically have smaller set of items. For example, lowercase English alphabet has only 26 characters. ASCII has only 256 characters.Strings are immut
2 min read
Hashing in Data StructureHashing is a technique used in data structures that efficiently stores and retrieves data in a way that allows for quick access. Hashing involves mapping data to a specific index in a hash table (an array of items) using a hash function. It enables fast retrieval of information based on its key. The
2 min read
Linked List Data StructureA linked list is a fundamental data structure in computer science. It mainly allows efficient insertion and deletion operations compared to arrays. Like arrays, it is also used to implement other data structures like stack, queue and deque. Hereâs the comparison of Linked List vs Arrays Linked List:
2 min read
Stack Data StructureA Stack is a linear data structure that follows a particular order in which the operations are performed. The order may be LIFO(Last In First Out) or FILO(First In Last Out). LIFO implies that the element that is inserted last, comes out first and FILO implies that the element that is inserted first
2 min read
Queue Data StructureA Queue Data Structure is a fundamental concept in computer science used for storing and managing data in a specific order. It follows the principle of "First in, First out" (FIFO), where the first element added to the queue is the first one to be removed. It is used as a buffer in computer systems
2 min read
Tree Data StructureTree Data Structure is a non-linear data structure in which a collection of elements known as nodes are connected to each other via edges such that there exists exactly one path between any two nodes. Types of TreeBinary Tree : Every node has at most two childrenTernary Tree : Every node has at most
4 min read
Graph Data StructureGraph Data Structure is a collection of nodes connected by edges. It's used to represent relationships between different entities. If you are looking for topic-wise list of problems on different topics like DFS, BFS, Topological Sort, Shortest Path, etc., please refer to Graph Algorithms. Basics of
3 min read
Trie Data StructureThe Trie data structure is a tree-like structure used for storing a dynamic set of strings. It allows for efficient retrieval and storage of keys, making it highly effective in handling large datasets. Trie supports operations such as insertion, search, deletion of keys, and prefix searches. In this
15+ min read
Algorithms
Searching AlgorithmsSearching algorithms are essential tools in computer science used to locate specific items within a collection of data. In this tutorial, we are mainly going to focus upon searching in an array. When we search an item in an array, there are two most common algorithms used based on the type of input
2 min read
Sorting AlgorithmsA Sorting Algorithm is used to rearrange a given array or list of elements in an order. For example, a given array [10, 20, 5, 2] becomes [2, 5, 10, 20] after sorting in increasing order and becomes [20, 10, 5, 2] after sorting in decreasing order. There exist different sorting algorithms for differ
3 min read
Introduction to RecursionThe process in which a function calls itself directly or indirectly is called recursion and the corresponding function is called a recursive function. A recursive algorithm takes one step toward solution and then recursively call itself to further move. The algorithm stops once we reach the solution
14 min read
Greedy AlgorithmsGreedy algorithms are a class of algorithms that make locally optimal choices at each step with the hope of finding a global optimum solution. At every step of the algorithm, we make a choice that looks the best at the moment. To make the choice, we sometimes sort the array so that we can always get
3 min read
Graph AlgorithmsGraph is a non-linear data structure like tree data structure. The limitation of tree is, it can only represent hierarchical data. For situations where nodes or vertices are randomly connected with each other other, we use Graph. Example situations where we use graph data structure are, a social net
3 min read
Dynamic Programming or DPDynamic Programming is an algorithmic technique with the following properties.It is mainly an optimization over plain recursion. Wherever we see a recursive solution that has repeated calls for the same inputs, we can optimize it using Dynamic Programming. The idea is to simply store the results of
3 min read
Bitwise AlgorithmsBitwise algorithms in Data Structures and Algorithms (DSA) involve manipulating individual bits of binary representations of numbers to perform operations efficiently. These algorithms utilize bitwise operators like AND, OR, XOR, NOT, Left Shift, and Right Shift.BasicsIntroduction to Bitwise Algorit
4 min read
Advanced
Segment TreeSegment Tree is a data structure that allows efficient querying and updating of intervals or segments of an array. It is particularly useful for problems involving range queries, such as finding the sum, minimum, maximum, or any other operation over a specific range of elements in an array. The tree
3 min read
Pattern SearchingPattern searching algorithms are essential tools in computer science and data processing. These algorithms are designed to efficiently find a particular pattern within a larger set of data. Patten SearchingImportant Pattern Searching Algorithms:Naive String Matching : A Simple Algorithm that works i
2 min read
GeometryGeometry is a branch of mathematics that studies the properties, measurements, and relationships of points, lines, angles, surfaces, and solids. From basic lines and angles to complex structures, it helps us understand the world around us.Geometry for Students and BeginnersThis section covers key br
2 min read
Interview Preparation
Practice Problem