Check if a binary tree is a sum tree - binary-tree

Method:
1) If the node is a leaf node then sum of subtree rooted with this node is equal to value of this node.
2) If the node is not a leaf node then sum of subtree rooted with this node is twice the value of this node (Assuming that the tree rooted with this node is SumTree).
[a link]http://www.geeksforgeeks.org/check-if-a-given-binary-tree-is-sumtree/
Qstn: why do we need to initialize ls=0 or rs=0 when it's a leaf node. Considering the tree as given in the link, if we reach node 4
if(node == NULL || isLeaf(node))
return 1;
the above code returns 1 (true) back to the function from where it is called i.e., node 10 similarly the right side returns true back to node 10 so we can now enter the loop below as both conditions are true
if( isSumTree(node->left) && isSumTree(node->right))
the node being 10 we calculate the data on the left and right as given here in the else if conditions, so why is the condition if(node->left==NULL) then ls=0 necessary(isn't it already taken care of as it is a leaf node)?
because return (4=0+0) will be false and the whole loop will become false?
int isLeaf(struct node *node)
{
if(node == NULL)
return 0;
if(node->left == NULL && node->right == NULL)
return 1;
return 0;
}
int isSumTree(struct node* node)
{
int ls; // for sum of nodes in left subtree
int rs; // for sum of nodes in right subtree
/* If node is NULL or it's a leaf node then
return true */
if(node == NULL || isLeaf(node))
return 1;
if( isSumTree(node->left) && isSumTree(node->right))
{
// Get the sum of nodes in left subtree
if(node->left == NULL)
ls = 0;
else if(isLeaf(node->left))
ls = node->left->data;
else
ls = 2*(node->left->data);
// Get the sum of nodes in right subtree
if(node->right == NULL)
rs = 0;
else if(isLeaf(node->right))
rs = node->right->data;
else
rs = 2*(node->right->data);
/* If root's data is equal to sum of nodes in left
and right subtrees then return 1 else return 0*/
return(node->data == ls + rs);
}
return 0;
}

I assume you are referring to lines
if(node->left == NULL)
ls = 0;
and
if(node->right == NULL)
rs = 0;
The author of that solution initialized rs and ls to 0 when the left and right child doesn't exist. This initialization is needed because there was no previous assignment to ls and rs. If you don't have this step, your ls and rs could be garbage value.
You can save the aforementioned checks by initializing your ls and rs values when you declare them.
int ls = 0,rs = 0;
Hope this helps!

Related

Binary tree with disjoint intervals

I'm trying to create an interval binary tree. It is similar to a regular binary search tree, but nodes are disjoint intervals rather than single numbers. If an interval being inserted has common elements with the already existing intervals, then these intervals are combined into one, and the union of them is inserted. I hope the example below will make it easier to understand.
Here is the program I have created: https://onecompiler.com/c/3ymwk8zv2
The main part is:
struct node *delete(struct node* root, int key)
{
if(root == NULL) return root;
if(key < root->low)
root->left = delete(root->left, key);
else if(key > root->low)
root->right = delete(root->right, key);
else {
if(root->left == NULL)
return root->right;
else if(root->right == NULL)
return root->left;
struct node *min = minValue(root->right);
root->low = min->low;
root->high = min->high;
root->id = min->id;
root->right = delete(root->right, root->low);
}
return root;
}
int insertIntersect(struct node *root, int low, int high, int *left, int *right)
{
if(root == NULL)
return 0;
insertIntersect(root->left, low, high, left, right);
insertIntersect(root->right, low, high, left, right);
if(!intersect(root->low, root->high, low, high))
return 0;
else
{
*left = min(*left,min(root->low, low));
*right = max(*right, max(root->high, high));
root->inter = 1;
delete(root, root->low);
}
return 0;
}
struct node *insert(struct node *root, int low, int high)
{
if(root == NULL)
return createNode(low, high);
if(low < root->low && high < root->high)
root->left = insert(root->left, low, high);
else if (low > root->low && high > root->high)
root->right = insert(root->right, low, high);
else
{
int left = INT_MAX, right = INT_MIN;
insertIntersect(root, low, high, &left, &right);
root->low = left;
root->high = right;
}
return root;
}
Executing my code for the above example produces a tree with the following nodes:
(1-3)
(8-9)
(4-25)
even though (8-9) should be incorporated into the (4-25) interval.
I'm looking for help with debugging my solution. I will also appreciate ideas for improving my code.
To be honest, the solution turned out to be way more complex than I thought initially. There are many things to fix in your code, so I decided to write it from scratch. It's hard to describe everything precisely, so apart from the description, I added code snippets with an implementation in C.
Let each node has the following properties:
interval - with a and b defining its ends
left - the left subtree's root
right - the right subtree's root
First, we have to define the delete operation as it'll be used when inserting an interval. When deleting we must take into account the fact existing tree nodes can be removed or divided into two parts. It's a bit complex as there are six cases to consider:
root->interval.a < interval.a and interval.b < root->interval.b - interval is within root->interval. root should be split into two nodes with only left and only right child correspondingly. One of these parts is chosen as the new root and the other becomes its second child.
interval.a < root->interval.a - interval contains values lower than the ones in root. It makes sense to delete interval from the left subtree. If all values in interval are lower, there is nothing more to do.
root->interval.b < interval.b - interval contains values higher than the one in root. It makes sense to delete interval from the right subtree. If all values in interval are higher, there is nothing more to do.
interval.a < root->interval.a and root->interval.b < interval.b - root->interval is within interval. root should be deleted. Both children's subtrees are in their final state as previously if needed interval was deleted from them. One of the children becomes the new root and the second one is inserted into its subtree.
interval.b is within root->interval - After handling the above cases interval.a <= root->interval.a, so the left part of root->interval is removed by adjusting root->interval.a.
interval.a is within root->interval - After handling the above cases root->interval.b <= interval.b, so the right part of root->interval is removed by adjusting root->interval.b.
Node *delete(Node *root, Interval interval) {
if (root == NULL) {
return NULL;
}
if (root->interval.a < interval.a && interval.b < root->interval.b) {
// arbitrary choice of the right part as the new root
Interval leftRootInterval = { .a = root->interval.a, .b = interval.a };
Node *leftRoot = createNode(leftRootInterval);
leftRoot->left = root->left;
root->interval.a = interval.b;
root->left = leftRoot;
return root;
}
if (interval.a < root->interval.a) {
root->left = delete(root->left, interval);
if (interval.b <= root->interval.a) {
return root;
}
}
if (root->interval.b < interval.b) {
root->right = delete(root->right, interval);
if (root->interval.b <= interval.a) {
return root;
}
}
if (interval.a < root->interval.a && root->interval.b < interval.b) {
Node *leftRoot = root->left;
Node *rightRoot = root->right;
free(root);
// arbitrary choice of leftRoot as the new root
if (leftRoot != NULL) {
root = leftRoot;
while (leftRoot->right != NULL) {
leftRoot = leftRoot->right;
}
leftRoot->right = rightRoot;
return root;
}
// there is only the right root
return rightRoot;
}
if (root->interval.a < interval.b && interval.b < root->interval.b) {
root->interval.a = interval.b;
return root;
}
// root->interval.a < interval.a && interval.a < root->interval.b
root->interval.b = interval.a;
return root;
}
Note the comments in the code above saying the new root is selected arbitrarily. Any of the two nodes could be chosen. Selecting always the same node in those places may lead to unbalance in the tree depending on the sequence of operations. One option to mitigate this issue is to make the choice randomly.
When interval is inserted there are four cases to analyze:
interval.b < root->interval.a - interval's values are lower than root->interval's ones. Insert it to the left subtree.
root->interval.b < interval.a - interval's values are higher than root->interval's ones. Insert it into the right subtree.
interval.a < root->interval.a - Because of previously checked case 1 root->interval.a <= interval.b - root->interval should be extended to the left. Note that some nodes from the left subtree can be absorbed by root->interval after the extension. Delete interval from the left subtree to remove them. Then check if the remaining part of root->left->interval should be also combined with root->interval.
root->interval.b < interval.b - Because of previously checked case 2 interval.a <= root->interval.b - root->interval should be extended to the right. Note that some nodes from the right subtree can be absorbed by root->interval after the extension. Delete interval from the right subtree to remove them. Then check if the remaining part of root->right->interval should be also combined with root->interval.
Node *insert(Node *root, Interval interval) {
if (root == NULL) {
return createNode(interval);
}
if (interval.b < root->interval.a) {
root->left = insert(root->left, interval);
return root;
}
if (root->interval.b < interval.a) {
root->right = insert(root->right, interval);
return root;
}
if (interval.a < root->interval.a) {
root->interval.a = interval.a;
root->left = delete(root->left, interval);
Node *leftRoot = root->left;
if (leftRoot != NULL && root->interval.a == leftRoot->interval.b) {
root->interval.a = leftRoot->interval.a;
root->left = leftRoot->left;
free(leftRoot);
}
}
if (root->interval.b < interval.b) {
root->interval.b = interval.b;
root->right = delete(root->right, interval);
Node *rightRoot = root->right;
if (rightRoot != NULL && root->interval.b == rightRoot->interval.a) {
root->interval.b = rightRoot->interval.b;
root->right = rightRoot->right;
free(rightRoot);
}
}
return root;
}
Note that cases 3 and 4 aren't mutually exclusive. root->interval can be extended in both directions. Only one end, however, has to be adjusted at a time as the second one if needed is adjusted when handling the other case.

How to find the smallest number on a right subtree

I want to find the smallest number in a rignt subtree of a node, and this code below is what I thought that was the solution, but its not working properly. What is wrong with this code?
int small; // Where the smallest value is stored
int smallest(Node n)
{
if(n.info < small && aux != 0) small = n.info;
if(aux == 0)
{
aux = 1;
small = n.dir.info;
if(n!=NULL && n.dir!=NULL) return smallest(n.dir);
}
else{
if(n.dir != NULL) return smallest(n.dir);
if(n.esq != NULL) return smallest(n.esq);
}
return small;
}
I am using n.right for right subtree pointer and n.left for left subtree pointer
Just call the function smallest(n.right); smallest is a function that will find the smallest value in a binary tree
int smallest(Node n){
if( n==NULL ) return INF; // replace INF with the maximum value that int can hold in your system like 2147483647
int left_small = smallest(n.left); // smallest value in left subtree
int right_small = smallest(n.right); // smallest value in right subtree
int ans = n.info;
if( left_small < ans ) ans = left_small;
if( right_small < ans ) ans = right_small;
return ans;
}

Remove all nodes in a binary three which don’t lie in any path with sum>= k

Not able to understand answer given HERE
Can someone please help to understand.
My Algo:
Recursively find sum of each path.
If sum >=k, put all the nodes in the path in a hashset
At the end traverse the tree, remove nodes which are not there in hashset.
I am pretty sure, there is a lot of scope of improvement here.
You have tree and you are recursively parsing it like this :
go_node(Node node){
go_node(node.left);
go_node(node.right);
}
At your example, you want to delete any subtree which value is less than a given number. The solution is easy, we change our simple function a little and problem will be solved. I let "K" be the global variable to have this code as simple as possible. However you can parse it in go_node method too.
int go_node(Node node, int value){
this.total_value = value;
total_value += go_node(node.left, value);
if (node.left.total_value < K){
node.left = null;
}
total_value += go_node(node.right, value);
if (node.right.total_value < K){
node.right = null;
}
return total_value;
}
Why I now I can delete them? When some value returns from a left or right subtree, that subtree is "finished", it is processed and what is important - it gives me adding of all that subtree. So when the total_value of this node is less than K, it means THIS node and ALL childs of this node (and childs of childs etc.) is less than K. Cause when the subtree child returns me a value, that child has in total_value stored the value of all the subtree.
Approch is to traverse the tree and delete nodes in bottom up manner. While traversing the tree, recursively calculate the sum of nodes from root to leaf node of each path. For each visited node, checks the total calculated sum against given sum “k”. If sum is less than k, then free(delete) that node (leaf node) and return the sum back to the previous node.
public int removeNodesUtil(Node node, int sum, int k)
{
if (node == null) return sum;
sum =sum + node.data;
if (node.left == null && node.right == null)
{
if (sum < k)
{
node = null;
}
return sum;
}
int leftSum = 0, rightSum = 0, maxSum = 0;
if (node.left != null) {
leftSum = removeNodesUtil(node.left, sum, k);
if (leftSum < k) {
node.left = null;
}
}
if (node.right != null) {
rightSum = removeNodesUtil(node.right, sum, k);
if (rightSum < k) {
node.right = null;
}
}
maxSum = Math.max(leftSum, rightSum);
if (maxSum < k) {
node = null;
}
return maxSum;
}

Given a Binary Tree, find the largest subtree which is a Binary Search Tree

I was asked this question in an interview. I started my answer with the naive approach of finding all the subtree and checking if any of them is a bst. In the process, we will record the size of max bst seen so far.
Is there a better approach than this?
What if you do this:
Reverse the weight of your graph
Use Kruskal algorithm in this way.
a. Select the lowest wheighted edge from your set of edges.
b. Create a tree only if adding that edge doesn't break your bst constraint.
c. Remove that edge from your edges set.
You might end up with several trees (Since discarding edges when bst constraint is not satisfied could make you disconnect your original graph), so just select the one with more nodes.
I think of your solution like this:
for each subtree of the tree:
if the subtree is a binary search tree:
compute its size
if it is the largest one found so far:
best = subtree
return best
This is inefficient because it does O(n) work for each subtree, and there are up to n subtrees.
You can do better by walking the whole tree only once.
// Walk the subtree at node. Find the largest subtree that is a binary search tree
// and return that tree in *result. Also return that subtree's size and the range
// of values it covers in *size, *min, and *max.
void
walk(Node *node, Node **result, size_t *size, Value *min, Value *max)
{
Node *result0 = NULL;
size_t size0 = 0;
Value min0, max0;
if (node->left)
walk(node->left, &result0, &size0, &min0, &max0);
Node *result1 = NULL;
size_t size1 = 0;
Value min1, max1;
if (node->right)
walk(node->right, &result1, &size1, &min1, &max1);
// If both subtrees are search trees and node->value falls between them,
// then node is a search tree.
if (result0 == node->left
&& result1 == node->right
&& (node->left == NULL || max0 <= node->value)
&& (node->right == NULL || node->value <= min1))
{
*result = node;
*size = size0 + 1 + size1;
*min = node->left == NULL ? node->value : min0;
*max = node->right == NULL ? node->value : max1;
} else if (size0 >= size1) {
*result = result0;
*size = size0;
*min = min0;
*max = max0;
} else {
*result = result1;
*size = size1;
*min = min1;
*max = max1;
}
}
Node *
findLargestBinarySearchSubtree(Node *root)
{
Node *result;
size_t size;
Value min, max;
walk(root, &result, &size, &min, &max);
return result;
}
This website seems to cover this problem under: Binary Search Tree Checking. Specifically, here's the excerpt to the solution in C++
/*
Returns true if the given tree is a binary search tree
(efficient version).
*/
int isBST2(struct node* node) {
return(isBSTUtil(node, INT_MIN, INT_MAX));
}
/*
Returns true if the given tree is a BST and its
values are >= min and <= max.
*/
int isBSTUtil(struct node* node, int min, int max) {
if (node==NULL) return(true);
// false if this node violates the min/max constraint
if (node->data<min || node->data>max) return(false);
// otherwise check the subtrees recursively,
// tightening the min or max constraint
return
isBSTUtil(node->left, min, node->data) &&
isBSTUtil(node->right, node->data+1, max)
);
}
I assume there is O(n) complexity to solve.
bool is_bst(node * cur)
{
if (cur == NULL)
return true;
// if calculated before cur vertex.
if (hash_set_bst[cur] != -1)
return hash_set_bst[cur];
int left_value = MIN;
int right_value = MAX;
if (cur -> left != NULL)
left_value = cur -> left -> value;
if (cur -> right != NULL)
right_value = cur -> right -> value;
if (cur -> value > left_value && cur -> value < right_value)
{
hash_set_bst[cur] = is_bst(cur->left) && is_bst(cur->right);
return hash_set_bst[cur];
}
else
{
hash_set_bst[cur] = 0;
is_bst(cur->left);
is_bst(cur->right);
return hash_set_bst[cur];
}
}
Now for each node you know if it can start BST or not. Now you need to calculate sub tree sizes and then iterate throgh all nodes and figure out what's the max size having flag if node can start BST.
To calculate sizes you may do the following:
int dfs(node * cur)
{
if (cur == NULL) return 0;
size[cur] = 1 + dfs(cur->left) + dfs(cur->right);
return size[cur];
}
do an in order traversal of the binary tree, if any subtree is BST, in order traversal will produce an ascending sequence, record the size of the tree as you go. when u hit a break point, recursive in order traversal that tree using the break point as root, record its size. pick the biggest one.

How to find the lowest common ancestor of two nodes in any binary tree?

The Binary Tree here is may not necessarily be a Binary Search Tree.
The structure could be taken as -
struct node {
int data;
struct node *left;
struct node *right;
};
The maximum solution I could work out with a friend was something of this sort -
Consider this binary tree :
The inorder traversal yields - 8, 4, 9, 2, 5, 1, 6, 3, 7
And the postorder traversal yields - 8, 9, 4, 5, 2, 6, 7, 3, 1
So for instance, if we want to find the common ancestor of nodes 8 and 5, then we make a list of all the nodes which are between 8 and 5 in the inorder tree traversal, which in this case happens to be [4, 9, 2]. Then we check which node in this list appears last in the postorder traversal, which is 2. Hence the common ancestor for 8 and 5 is 2.
The complexity for this algorithm, I believe is O(n) (O(n) for inorder/postorder traversals, the rest of the steps again being O(n) since they are nothing more than simple iterations in arrays). But there is a strong chance that this is wrong. :-)
But this is a very crude approach, and I'm not sure if it breaks down for some case. Is there any other (possibly more optimal) solution to this problem?
Starting from root node and moving downwards if you find any node that has either p or q as its direct child then it is the LCA. (edit - this should be if p or q is the node's value, return it. Otherwise it will fail when one of p or q is a direct child of the other.)
Else if you find a node with p in its right(or left) subtree and q in its left(or right) subtree then it is the LCA.
The fixed code looks like:
treeNodePtr findLCA(treeNodePtr root, treeNodePtr p, treeNodePtr q) {
// no root no LCA.
if(!root) {
return NULL;
}
// if either p or q is the root then root is LCA.
if(root==p || root==q) {
return root;
} else {
// get LCA of p and q in left subtree.
treeNodePtr l=findLCA(root->left , p , q);
// get LCA of p and q in right subtree.
treeNodePtr r=findLCA(root->right , p, q);
// if one of p or q is in leftsubtree and other is in right
// then root it the LCA.
if(l && r) {
return root;
}
// else if l is not null, l is LCA.
else if(l) {
return l;
} else {
return r;
}
}
}
The below code fails when either is the direct child of other.
treeNodePtr findLCA(treeNodePtr root, treeNodePtr p, treeNodePtr q) {
// no root no LCA.
if(!root) {
return NULL;
}
// if either p or q is direct child of root then root is LCA.
if(root->left==p || root->left==q ||
root->right ==p || root->right ==q) {
return root;
} else {
// get LCA of p and q in left subtree.
treeNodePtr l=findLCA(root->left , p , q);
// get LCA of p and q in right subtree.
treeNodePtr r=findLCA(root->right , p, q);
// if one of p or q is in leftsubtree and other is in right
// then root it the LCA.
if(l && r) {
return root;
}
// else if l is not null, l is LCA.
else if(l) {
return l;
} else {
return r;
}
}
}
Code In Action
Nick Johnson is correct that a an O(n) time complexity algorithm is the best you can do if you have no parent pointers.) For a simple recursive version of that algorithm see the code in Kinding's post which runs in O(n) time.
But keep in mind that if your nodes have parent pointers, an improved algorithm is possible. For both nodes in question construct a list containing the path from root to the node by starting at the node, and front inserting the parent.
So for 8 in your example, you get (showing steps): {4}, {2, 4}, {1, 2, 4}
Do the same for your other node in question, resulting in (steps not shown): {1, 2}
Now compare the two lists you made looking for the first element where the list differ, or the last element of one of the lists, whichever comes first.
This algorithm requires O(h) time where h is the height of the tree. In the worst case O(h) is equivalent to O(n), but if the tree is balanced, that is only O(log(n)). It also requires O(h) space. An improved version is possible that uses only constant space, with code shown in CEGRD's post
Regardless of how the tree is constructed, if this will be an operation you perform many times on the tree without changing it in between, there are other algorithms you can use that require O(n) [linear] time preparation, but then finding any pair takes only O(1) [constant] time. For references to these algorithms, see the the lowest common ancestor problem page on Wikipedia. (Credit to Jason for originally posting this link)
Here is the working code in JAVA
public static Node LCA(Node root, Node a, Node b) {
if (root == null) {
return null;
}
// If the root is one of a or b, then it is the LCA
if (root == a || root == b) {
return root;
}
Node left = LCA(root.left, a, b);
Node right = LCA(root.right, a, b);
// If both nodes lie in left or right then their LCA is in left or right,
// Otherwise root is their LCA
if (left != null && right != null) {
return root;
}
return (left != null) ? left : right;
}
The answers given so far uses recursion or stores, for instance, a path in memory.
Both of these approaches might fail if you have a very deep tree.
Here is my take on this question.
When we check the depth (distance from the root) of both nodes, if they are equal, then we can safely move upward from both nodes towards the common ancestor. If one of the depth is bigger then we should move upward from the deeper node while staying in the other one.
Here is the code:
findLowestCommonAncestor(v,w):
depth_vv = depth(v);
depth_ww = depth(w);
vv = v;
ww = w;
while( depth_vv != depth_ww ) {
if ( depth_vv > depth_ww ) {
vv = parent(vv);
depth_vv--;
else {
ww = parent(ww);
depth_ww--;
}
}
while( vv != ww ) {
vv = parent(vv);
ww = parent(ww);
}
return vv;
The time complexity of this algorithm is: O(n).
The space complexity of this algorithm is: O(1).
Regarding the computation of the depth, we can first remember the definition: If v is root, depth(v) = 0; Otherwise, depth(v) = depth(parent(v)) + 1. We can compute depth as follows:
depth(v):
int d = 0;
vv = v;
while ( vv is not root ) {
vv = parent(vv);
d++;
}
return d;
Well, this kind of depends how your Binary Tree is structured. Presumably you have some way of finding the desired leaf node given the root of the tree - simply apply that to both values until the branches you choose diverge.
If you don't have a way to find the desired leaf given the root, then your only solution - both in normal operation and to find the last common node - is a brute-force search of the tree.
This can be found at:-
http://goursaha.freeoda.com/DataStructure/LowestCommonAncestor.html
tree_node_type *LowestCommonAncestor(
tree_node_type *root , tree_node_type *p , tree_node_type *q)
{
tree_node_type *l , *r , *temp;
if(root==NULL)
{
return NULL;
}
if(root->left==p || root->left==q || root->right ==p || root->right ==q)
{
return root;
}
else
{
l=LowestCommonAncestor(root->left , p , q);
r=LowestCommonAncestor(root->right , p, q);
if(l!=NULL && r!=NULL)
{
return root;
}
else
{
temp = (l!=NULL)?l:r;
return temp;
}
}
}
Tarjan's off-line least common ancestors algorithm is good enough (cf. also Wikipedia). There is more on the problem (the lowest common ancestor problem) on Wikipedia.
To find out common ancestor of two node :-
Find the given node Node1 in the tree using binary search and save all nodes visited in this process in an array say A1. Time - O(logn), Space - O(logn)
Find the given Node2 in the tree using binary search and save all nodes visited in this process in an array say A2. Time - O(logn), Space - O(logn)
If A1 list or A2 list is empty then one the node does not exist so there is no common ancestor.
If A1 list and A2 list are non-empty then look into the list until you find non-matching node. As soon as you find such a node then node prior to that is common ancestor.
This would work for binary search tree.
I have made an attempt with illustrative pictures and working code in Java,
http://tech.bragboy.com/2010/02/least-common-ancestor-without-using.html
The below recursive algorithm will run in O(log N) for a balanced binary tree. If either of the nodes passed into the getLCA() function are the same as the root then the root will be the LCA and there will be no need to perform any recussrion.
Test cases.
[1] Both nodes n1 & n2 are in the tree and reside on either side of their parent node.
[2] Either node n1 or n2 is the root, the LCA is the root.
[3] Only n1 or n2 is in the tree, LCA will be either the root node of the left subtree of the tree root, or the LCA will be the root node of the right subtree of the tree root.
[4] Neither n1 or n2 is in the tree, there is no LCA.
[5] Both n1 and n2 are in a straight line next to each other, LCA will be either of n1 or n2 which ever is closes to the root of the tree.
//find the search node below root
bool findNode(node* root, node* search)
{
//base case
if(root == NULL)
return false;
if(root->val == search->val)
return true;
//search for the node in the left and right subtrees, if found in either return true
return (findNode(root->left, search) || findNode(root->right, search));
}
//returns the LCA, n1 & n2 are the 2 nodes for which we are
//establishing the LCA for
node* getLCA(node* root, node* n1, node* n2)
{
//base case
if(root == NULL)
return NULL;
//If 1 of the nodes is the root then the root is the LCA
//no need to recurse.
if(n1 == root || n2 == root)
return root;
//check on which side of the root n1 and n2 reside
bool n1OnLeft = findNode(root->left, n1);
bool n2OnLeft = findNode(root->left, n2);
//n1 & n2 are on different sides of the root, so root is the LCA
if(n1OnLeft != n2OnLeft)
return root;
//if both n1 & n2 are on the left of the root traverse left sub tree only
//to find the node where n1 & n2 diverge otherwise traverse right subtree
if(n1OnLeft)
return getLCA(root->left, n1, n2);
else
return getLCA(root->right, n1, n2);
}
Just walk down from the whole tree's root as long as both given nodes ,say p and q, for which Ancestor has to be found, are in the same sub-tree (meaning their values are both smaller or both larger than root's).
This walks straight from the root to the Least Common Ancestor , not looking at the rest of the tree, so it's pretty much as fast as it gets. A few ways to do it.
Iterative, O(1) space
Python
def lowestCommonAncestor(self, root, p, q):
while (root.val - p.val) * (root.val - q.val) > 0:
root = (root.left, root.right)[p.val > root.val]
return root
Java
public TreeNode lowestCommonAncestor(TreeNode root, TreeNode p, TreeNode q) {
while ((root.val - p.val) * (root.val - q.val) > 0)
root = p.val < root.val ? root.left : root.right;
return root;
}
in case of overflow, I'd do (root.val - (long)p.val) * (root.val - (long)q.val)
Recursive
Python
def lowestCommonAncestor(self, root, p, q):
next = p.val < root.val > q.val and root.left or \
p.val > root.val < q.val and root.right
return self.lowestCommonAncestor(next, p, q) if next else root
Java
public TreeNode lowestCommonAncestor(TreeNode root, TreeNode p, TreeNode q) {
return (root.val - p.val) * (root.val - q.val) < 1 ? root :
lowestCommonAncestor(p.val < root.val ? root.left : root.right, p, q);
}
Node *LCA(Node *root, Node *p, Node *q) {
if (!root) return NULL;
if (root == p || root == q) return root;
Node *L = LCA(root->left, p, q);
Node *R = LCA(root->right, p, q);
if (L && R) return root; // if p and q are on both sides
return L ? L : R; // either one of p,q is on one side OR p,q is not in L&R subtrees
}
Consider this tree
If we do postorder and preorder traversal and find the first occuring common predecessor and successor, we get the common ancestor.
postorder => 0,2,1,5,4,6,3,8,10,11,9,14,15,13,12,7
preorder => 7,3,1,0,2,6,4,5,12,9,8,11,10,13,15,14
eg :1
Least common ancestor of 8,11
in postorder we have = >9,14,15,13,12,7 after 8 & 11
in preorder we have =>7,3,1,0,2,6,4,5,12,9 before 8 & 11
9 is the first common number that occurs after 8& 11 in postorder and before 8 & 11 in preorder, hence 9 is the answer
eg :2
Least common ancestor of 5,10
11,9,14,15,13,12,7 in postorder
7,3,1,0,2,6,4 in preorder
7 is the first number that occurs after 5,10 in postorder and before 5,10 in preorder, hence 7 is the answer
If it is full binary tree with children of node x as 2*x and 2*x+1 than there is a faster way to do it
int get_bits(unsigned int x) {
int high = 31;
int low = 0,mid;
while(high>=low) {
mid = (high+low)/2;
if(1<<mid==x)
return mid+1;
if(1<<mid<x) {
low = mid+1;
}
else {
high = mid-1;
}
}
if(1<<mid>x)
return mid;
return mid+1;
}
unsigned int Common_Ancestor(unsigned int x,unsigned int y) {
int xbits = get_bits(x);
int ybits = get_bits(y);
int diff,kbits;
unsigned int k;
if(xbits>ybits) {
diff = xbits-ybits;
x = x >> diff;
}
else if(xbits<ybits) {
diff = ybits-xbits;
y = y >> diff;
}
k = x^y;
kbits = get_bits(k);
return y>>kbits;
}
How does it work
get bits needed to represent x & y which using binary search is O(log(32))
the common prefix of binary notation of x & y is the common ancestor
whichever is represented by larger no of bits is brought to same bit by k >> diff
k = x^y erazes common prefix of x & y
find bits representing the remaining suffix
shift x or y by suffix bits to get common prefix which is the common ancestor.
This works because basically divide the larger number by two recursively until both numbers are equal. That number is the common ancestor. Dividing is effectively the right shift opearation. So we need to find common prefix of two numbers to find the nearest ancestor
In scala, you can:
abstract class Tree
case class Node(a:Int, left:Tree, right:Tree) extends Tree
case class Leaf(a:Int) extends Tree
def lca(tree:Tree, a:Int, b:Int):Tree = {
tree match {
case Node(ab,l,r) => {
if(ab==a || ab ==b) tree else {
val temp = lca(l,a,b);
val temp2 = lca(r,a,b);
if(temp!=null && temp2 !=null)
tree
else if (temp==null && temp2==null)
null
else if (temp==null) r else l
}
}
case Leaf(ab) => if(ab==a || ab ==b) tree else null
}
}
The lowest common ancestor between two nodes node1 and node2 is the lowest node in a tree that has both nodes as descendants.
The binary tree is traversed from the root node, until both nodes are found. Every time a node is visited, it is added to a dictionary (called parent).
Once both nodes are found in the binary tree, the ancestors of node1 are obtained using the dictionary and added to a set (called ancestors).
This step is followed in the same manner for node2. If the ancestor of node2 is present in the ancestors set for node1, it is the first common ancestor between them.
Below is the iterative python solution implemented using stack and dictionary with the following points:
A node can be a descendant of itself
All nodes in the binary tree are unique
node1 and node2 will exist in the binary tree
class Node:
def __init__(self, data=None, left=None, right=None):
self.data = data
self.left = left
self.right = right
def lowest_common_ancestor(root, node1, node2):
parent = {root: None}
stack = [root]
while node1 not in parent or node2 not in parent:
node = stack[-1]
stack.pop()
if node.left:
parent[node.left] = node
stack.append(node.left)
if node.right:
parent[node.right] = node
stack.append(node.right)
ancestors = set()
while node1:
ancestors.add(node1)
node1 = parent[node1]
while node2 not in ancestors:
node2 = parent[node2]
return node2.data
def main():
'''
Construct the below binary tree:
30
/ \
/ \
/ \
11 29
/ \ / \
8 12 25 14
'''
root = Node(30)
root.left = Node(11)
root.right = Node(29)
root.left.left = Node(8)
root.left.right = Node(12)
root.right.left = Node(25)
root.right.right = Node(14)
print(lowest_common_ancestor(root, root.left.left, root.left.right)) # 11
print(lowest_common_ancestor(root, root.left.left, root.left)) # 11
print(lowest_common_ancestor(root, root.left.left, root.right.right)) # 30
if __name__ == '__main__':
main()
The complexity of this approach is: O(n)
public TreeNode lowestCommonAncestor(TreeNode root, TreeNode p, TreeNode q) {
if(root==null || root == p || root == q){
return root;
}
TreeNode left = lowestCommonAncestor(root.left,p,q);
TreeNode right = lowestCommonAncestor(root.right,p,q);
return left == null ? right : right == null ? left : root;
}
Here is the C++ way of doing it. Have tried to keep the algorithm as much easy as possible to understand:
// Assuming that `BinaryNode_t` has `getData()`, `getLeft()` and `getRight()`
class LowestCommonAncestor
{
typedef char type;
// Data members which would behave as place holders
const BinaryNode_t* m_pLCA;
type m_Node1, m_Node2;
static const unsigned int TOTAL_NODES = 2;
// The core function which actually finds the LCA; It returns the number of nodes found
// At any point of time if the number of nodes found are 2, then it updates the `m_pLCA` and once updated, we have found it!
unsigned int Search (const BinaryNode_t* const pNode)
{
if(pNode == 0)
return 0;
unsigned int found = 0;
found += (pNode->getData() == m_Node1);
found += (pNode->getData() == m_Node2);
found += Search(pNode->getLeft()); // below condition can be after this as well
found += Search(pNode->getRight());
if(found == TOTAL_NODES && m_pLCA == 0)
m_pLCA = pNode; // found !
return found;
}
public:
// Interface method which will be called externally by the client
const BinaryNode_t* Search (const BinaryNode_t* const pHead,
const type node1,
const type node2)
{
// Initialize the data members of the class
m_Node1 = node1;
m_Node2 = node2;
m_pLCA = 0;
// Find the LCA, populate to `m_pLCANode` and return
(void) Search(pHead);
return m_pLCA;
}
};
How to use it:
LowestCommonAncestor lca;
BinaryNode_t* pNode = lca.Search(pWhateverBinaryTreeNodeToBeginWith);
if(pNode != 0)
...
The easiest way to find the Lowest Common Ancestor is using the following algorithm:
Examine root node
if value1 and value2 are strictly less that the value at the root node
Examine left subtree
else if value1 and value2 are strictly greater that the value at the root node
Examine right subtree
else
return root
public int LCA(TreeNode root, int value 1, int value 2) {
while (root != null) {
if (value1 < root.data && value2 < root.data)
return LCA(root.left, value1, value2);
else if (value2 > root.data && value2 2 root.data)
return LCA(root.right, value1, value2);
else
return root
}
return null;
}
I found a solution
Take inorder
Take preorder
Take postorder
Depending on 3 traversals, you can decide who is the LCA.
From LCA find distance of both nodes.
Add these two distances, which is the answer.
Here is what I think,
Find the route for the fist node , store it on to arr1.
Start finding the route for the 2 node , while doing so check every value from root to arr1.
time when value differs , exit. Old matched value is the LCA.
Complexity :
step 1 : O(n) , step 2 =~ O(n) , total =~ O(n).
Here are two approaches in c# (.net) (both discussed above) for reference:
Recursive version of finding LCA in binary tree (O(N) - as at most each node is visited)
(main points of the solution is LCA is (a) only node in binary tree where both elements reside either side of the subtrees (left and right) is LCA. (b) And also it doesn't matter which node is present either side - initially i tried to keep that info, and obviously the recursive function become so confusing. once i realized it, it became very elegant.
Searching both nodes (O(N)), and keeping track of paths (uses extra space - so, #1 is probably superior even thought the space is probably negligible if the binary tree is well balanced as then extra memory consumption will be just in O(log(N)).
so that the paths are compared (essentailly similar to accepted answer - but the paths is calculated by assuming pointer node is not present in the binary tree node)
Just for the completion (not related to question), LCA in BST (O(log(N))
Tests
Recursive:
private BinaryTreeNode LeastCommonAncestorUsingRecursion(BinaryTreeNode treeNode,
int e1, int e2)
{
Debug.Assert(e1 != e2);
if(treeNode == null)
{
return null;
}
if((treeNode.Element == e1)
|| (treeNode.Element == e2))
{
//we don't care which element is present (e1 or e2), we just need to check
//if one of them is there
return treeNode;
}
var nLeft = this.LeastCommonAncestorUsingRecursion(treeNode.Left, e1, e2);
var nRight = this.LeastCommonAncestorUsingRecursion(treeNode.Right, e1, e2);
if(nLeft != null && nRight != null)
{
//note that this condition will be true only at least common ancestor
return treeNode;
}
else if(nLeft != null)
{
return nLeft;
}
else if(nRight != null)
{
return nRight;
}
return null;
}
where above private recursive version is invoked by following public method:
public BinaryTreeNode LeastCommonAncestorUsingRecursion(int e1, int e2)
{
var n = this.FindNode(this._root, e1);
if(null == n)
{
throw new Exception("Element not found: " + e1);
}
if (e1 == e2)
{
return n;
}
n = this.FindNode(this._root, e2);
if (null == n)
{
throw new Exception("Element not found: " + e2);
}
var node = this.LeastCommonAncestorUsingRecursion(this._root, e1, e2);
if (null == node)
{
throw new Exception(string.Format("Least common ancenstor not found for the given elements: {0},{1}", e1, e2));
}
return node;
}
Solution by keeping track of paths of both nodes:
public BinaryTreeNode LeastCommonAncestorUsingPaths(int e1, int e2)
{
var path1 = new List<BinaryTreeNode>();
var node1 = this.FindNodeAndPath(this._root, e1, path1);
if(node1 == null)
{
throw new Exception(string.Format("Element {0} is not found", e1));
}
if(e1 == e2)
{
return node1;
}
List<BinaryTreeNode> path2 = new List<BinaryTreeNode>();
var node2 = this.FindNodeAndPath(this._root, e2, path2);
if (node1 == null)
{
throw new Exception(string.Format("Element {0} is not found", e2));
}
BinaryTreeNode lca = null;
Debug.Assert(path1[0] == this._root);
Debug.Assert(path2[0] == this._root);
int i = 0;
while((i < path1.Count)
&& (i < path2.Count)
&& (path2[i] == path1[i]))
{
lca = path1[i];
i++;
}
Debug.Assert(null != lca);
return lca;
}
where FindNodeAndPath is defined as
private BinaryTreeNode FindNodeAndPath(BinaryTreeNode node, int e, List<BinaryTreeNode> path)
{
if(node == null)
{
return null;
}
if(node.Element == e)
{
path.Add(node);
return node;
}
var n = this.FindNodeAndPath(node.Left, e, path);
if(n == null)
{
n = this.FindNodeAndPath(node.Right, e, path);
}
if(n != null)
{
path.Insert(0, node);
return n;
}
return null;
}
BST (LCA) - not related (just for completion for reference)
public BinaryTreeNode BstLeastCommonAncestor(int e1, int e2)
{
//ensure both elements are there in the bst
var n1 = this.BstFind(e1, throwIfNotFound: true);
if(e1 == e2)
{
return n1;
}
this.BstFind(e2, throwIfNotFound: true);
BinaryTreeNode leastCommonAcncestor = this._root;
var iterativeNode = this._root;
while(iterativeNode != null)
{
if((iterativeNode.Element > e1 ) && (iterativeNode.Element > e2))
{
iterativeNode = iterativeNode.Left;
}
else if((iterativeNode.Element < e1) && (iterativeNode.Element < e2))
{
iterativeNode = iterativeNode.Right;
}
else
{
//i.e; either iterative node is equal to e1 or e2 or in between e1 and e2
return iterativeNode;
}
}
//control will never come here
return leastCommonAcncestor;
}
Unit Tests
[TestMethod]
public void LeastCommonAncestorTests()
{
int[] a = { 13, 2, 18, 1, 5, 17, 20, 3, 6, 16, 21, 4, 14, 15, 25, 22, 24 };
int[] b = { 13, 13, 13, 2, 13, 18, 13, 5, 13, 18, 13, 13, 14, 18, 25, 22};
BinarySearchTree bst = new BinarySearchTree();
foreach (int e in a)
{
bst.Add(e);
bst.Delete(e);
bst.Add(e);
}
for(int i = 0; i < b.Length; i++)
{
var n = bst.BstLeastCommonAncestor(a[i], a[i + 1]);
Assert.IsTrue(n.Element == b[i]);
var n1 = bst.LeastCommonAncestorUsingPaths(a[i], a[i + 1]);
Assert.IsTrue(n1.Element == b[i]);
Assert.IsTrue(n == n1);
var n2 = bst.LeastCommonAncestorUsingRecursion(a[i], a[i + 1]);
Assert.IsTrue(n2.Element == b[i]);
Assert.IsTrue(n2 == n1);
Assert.IsTrue(n2 == n);
}
}
If someone interested in pseudo code(for university home works) here is one.
GETLCA(BINARYTREE BT, NODE A, NODE B)
IF Root==NIL
return NIL
ENDIF
IF Root==A OR root==B
return Root
ENDIF
Left = GETLCA (Root.Left, A, B)
Right = GETLCA (Root.Right, A, B)
IF Left! = NIL AND Right! = NIL
return root
ELSEIF Left! = NIL
Return Left
ELSE
Return Right
ENDIF
Although this has been answered already, this is my approach to this problem using C programming language. Although the code shows a binary search tree (as far as insert() is concerned), but the algorithm works for a binary tree as well. The idea is to go over all nodes that lie from node A to node B in inorder traversal, lookup the indices for these in the post order traversal. The node with maximum index in post order traversal is the lowest common ancestor.
This is a working C code to implement a function to find the lowest common ancestor in a binary tree. I am providing all the utility functions etc. as well, but jump to CommonAncestor() for quick understanding.
#include <stdio.h>
#include <malloc.h>
#include <stdlib.h>
#include <math.h>
static inline int min (int a, int b)
{
return ((a < b) ? a : b);
}
static inline int max (int a, int b)
{
return ((a > b) ? a : b);
}
typedef struct node_ {
int value;
struct node_ * left;
struct node_ * right;
} node;
#define MAX 12
int IN_ORDER[MAX] = {0};
int POST_ORDER[MAX] = {0};
createNode(int value)
{
node * temp_node = (node *)malloc(sizeof(node));
temp_node->left = temp_node->right = NULL;
temp_node->value = value;
return temp_node;
}
node *
insert(node * root, int value)
{
if (!root) {
return createNode(value);
}
if (root->value > value) {
root->left = insert(root->left, value);
} else {
root->right = insert(root->right, value);
}
return root;
}
/* Builds inorder traversal path in the IN array */
void
inorder(node * root, int * IN)
{
static int i = 0;
if (!root) return;
inorder(root->left, IN);
IN[i] = root->value;
i++;
inorder(root->right, IN);
}
/* Builds post traversal path in the POST array */
void
postorder (node * root, int * POST)
{
static int i = 0;
if (!root) return;
postorder(root->left, POST);
postorder(root->right, POST);
POST[i] = root->value;
i++;
}
int
findIndex(int * A, int value)
{
int i = 0;
for(i = 0; i< MAX; i++) {
if(A[i] == value) return i;
}
}
int
CommonAncestor(int val1, int val2)
{
int in_val1, in_val2;
int post_val1, post_val2;
int j=0, i = 0; int max_index = -1;
in_val1 = findIndex(IN_ORDER, val1);
in_val2 = findIndex(IN_ORDER, val2);
post_val1 = findIndex(POST_ORDER, val1);
post_val2 = findIndex(POST_ORDER, val2);
for (i = min(in_val1, in_val2); i<= max(in_val1, in_val2); i++) {
for(j = 0; j < MAX; j++) {
if (IN_ORDER[i] == POST_ORDER[j]) {
if (j > max_index) {
max_index = j;
}
}
}
}
printf("\ncommon ancestor of %d and %d is %d\n", val1, val2, POST_ORDER[max_index]);
return max_index;
}
int main()
{
node * root = NULL;
/* Build a tree with following values */
//40, 20, 10, 30, 5, 15, 25, 35, 1, 80, 60, 100
root = insert(root, 40);
insert(root, 20);
insert(root, 10);
insert(root, 30);
insert(root, 5);
insert(root, 15);
insert(root, 25);
insert(root, 35);
insert(root, 1);
insert(root, 80);
insert(root, 60);
insert(root, 100);
/* Get IN_ORDER traversal in the array */
inorder(root, IN_ORDER);
/* Get post order traversal in the array */
postorder(root, POST_ORDER);
CommonAncestor(1, 100);
}
There can be one more approach. However it is not as efficient as the one already suggested in answers.
Create a path vector for the node n1.
Create a second path vector for the node n2.
Path vector implying the set nodes from that one would traverse to reach the node in question.
Compare both path vectors. The index where they mismatch, return the node at that index - 1. This would give the LCA.
Cons for this approach:
Need to traverse the tree twice for calculating the path vectors.
Need addtional O(h) space to store path vectors.
However this is easy to implement and understand as well.
Code for calculating the path vector:
private boolean findPathVector (TreeNode treeNode, int key, int pathVector[], int index) {
if (treeNode == null) {
return false;
}
pathVector [index++] = treeNode.getKey ();
if (treeNode.getKey () == key) {
return true;
}
if (findPathVector (treeNode.getLeftChild (), key, pathVector, index) ||
findPathVector (treeNode.getRightChild(), key, pathVector, index)) {
return true;
}
pathVector [--index] = 0;
return false;
}
Try like this
node * lca(node * root, int v1,int v2)
{
if(!root) {
return NULL;
}
if(root->data == v1 || root->data == v2) {
return root;}
else
{
if((v1 > root->data && v2 < root->data) || (v1 < root->data && v2 > root->data))
{
return root;
}
if(v1 < root->data && v2 < root->data)
{
root = lca(root->left, v1, v2);
}
if(v1 > root->data && v2 > root->data)
{
root = lca(root->right, v1, v2);
}
}
return root;
}
Crude way:
At every node
X = find if either of the n1, n2 exist on the left side of the Node
Y = find if either of the n1, n2 exist on the right side of the Node
if the node itself is n1 || n2, we can call it either found on left
or right for the purposes of generalization.
If both X and Y is true, then the Node is the CA
The problem with the method above is that we will be doing the "find" multiple times, i.e. there is a possibility of each node getting traversed multiple times.
We can overcome this problem if we can record the information so as to not process it again (think dynamic programming).
So rather than doing find every node, we keep a record of as to whats already been found.
Better Way:
We check to see if for a given node if left_set (meaning either n1 | n2 has been found in the left subtree) or right_set in a depth first fashion. (NOTE: We are giving the root itself the property of being left_set if it is either n1 | n2)
If both left_set and right_set then the node is a LCA.
Code:
struct Node *
findCA(struct Node *root, struct Node *n1, struct Node *n2, int *set) {
int left_set, right_set;
left_set = right_set = 0;
struct Node *leftCA, *rightCA;
leftCA = rightCA = NULL;
if (root == NULL) {
return NULL;
}
if (root == n1 || root == n2) {
left_set = 1;
if (n1 == n2) {
right_set = 1;
}
}
if(!left_set) {
leftCA = findCA(root->left, n1, n2, &left_set);
if (leftCA) {
return leftCA;
}
}
if (!right_set) {
rightCA= findCA(root->right, n1, n2, &right_set);
if(rightCA) {
return rightCA;
}
}
if (left_set && right_set) {
return root;
} else {
*set = (left_set || right_set);
return NULL;
}
}
Code for A Breadth First Search to make sure both nodes are in the tree.
Only then move forward with the LCA search.
Please comment if you have any suggestions to improve.
I think we can probably mark them visited and restart the search at a certain point where we left off to improve for the second node (if it isn't found VISITED)
public class searchTree {
static boolean v1=false,v2=false;
public static boolean bfs(Treenode root, int value){
if(root==null){
return false;
}
Queue<Treenode> q1 = new LinkedList<Treenode>();
q1.add(root);
while(!q1.isEmpty())
{
Treenode temp = q1.peek();
if(temp!=null) {
q1.remove();
if (temp.value == value) return true;
if (temp.left != null) q1.add(temp.left);
if (temp.right != null) q1.add(temp.right);
}
}
return false;
}
public static Treenode lcaHelper(Treenode head, int x,int y){
if(head==null){
return null;
}
if(head.value == x || head.value ==y){
if (head.value == y){
v2 = true;
return head;
}
else {
v1 = true;
return head;
}
}
Treenode left = lcaHelper(head.left, x, y);
Treenode right = lcaHelper(head.right,x,y);
if(left!=null && right!=null){
return head;
}
return (left!=null) ? left:right;
}
public static int lca(Treenode head, int h1, int h2) {
v1 = bfs(head,h1);
v2 = bfs(head,h2);
if(v1 && v2){
Treenode lca = lcaHelper(head,h1,h2);
return lca.value;
}
return -1;
}
}
Some of the solutions here assumes that there is reference to the root node, some assumes that tree is a BST.
Sharing my solution using hashmap, without reference to root node and tree can be BST or non-BST:
var leftParent : Node? = left
var rightParent : Node? = right
var map = [data : Node?]()
while leftParent != nil {
map[(leftParent?.data)!] = leftParent
leftParent = leftParent?.parent
}
while rightParent != nil {
if let common = map[(rightParent?.data)!] {
return common
}
rightParent = rightParent?.parent
}
Solution 1: Recursive - Faster
The idea is to traverse the tree starting from root. If any of the given keys p and q matches with root, then root is LCA, assuming that both keys are present. If root doesn’t match with any of the keys, we recurse for left and right subtree.
The node which has one key present in its left subtree and the other key present in right subtree is the LCA. If both keys lie in left subtree, then left subtree has LCA also, otherwise LCA lies in right subtree.
Time Complexity: O(n)
Space Complexity: O(h) - for recursive call stack
class Solution
{
public TreeNode lowestCommonAncestor(TreeNode root, TreeNode p, TreeNode q)
{
if(root == null || root == p || root == q)
return root;
TreeNode left = lowestCommonAncestor(root.left, p, q);
TreeNode right = lowestCommonAncestor(root.right, p, q);
if(left == null)
return right;
else if(right == null)
return left;
else
return root; // If(left != null && right != null)
}
}
Solution 2: Iterative - Using parent pointers - Slower
Create an empty hash table.
Insert p and all of its ancestors in hash table.
Check if q or any of its ancestors exist in hash table, if yes then return the first existing ancestor.
Time Complexity: O(n) - In the worst case we might be visiting all the nodes of binary tree.
Space Complexity: O(n) - Space utilized the parent pointer Hash-table, ancestor_set and queue, would be O(n) each.
class Solution
{
public TreeNode lowestCommonAncestor(TreeNode root, TreeNode p, TreeNode q)
{
HashMap<TreeNode, TreeNode> parent_map = new HashMap<>();
HashSet<TreeNode> ancestors_set = new HashSet<>();
Queue<TreeNode> queue = new LinkedList<>();
parent_map.put(root, null);
queue.add(root);
while(!parent_map.containsKey(p) || !parent_map.containsKey(q))
{
TreeNode node = queue.poll();
if(node.left != null)
{
parent_map.put(node.left, node);
queue.add(node.left);
}
if(node.right != null)
{
parent_map.put(node.right, node);
queue.add(node.right);
}
}
while(p != null)
{
ancestors_set.add(p);
p = parent_map.get(p);
}
while(!ancestors_set.contains(q))
q = parent_map.get(q);
return q;
}
}

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