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You are given two balanced binary search trees e.g., AVL or Red Black Tree. Write a function that merges the two given balanced BSTs into a balanced binary search tree. Let there be m elements in first tree and n elements in the other tree. Your merge function should take O(m+n) time.
In the following solutions, it is assumed that sizes of trees are also given as input. If the size is not given, then we can get the size by traversing the tree (See this).
Method 1 (Insert elements of first tree to second)
Take all elements of first BST one by one, and insert them into the second BST. Inserting an element to a self balancing BST takes Logn time (See this) where n is size of the BST. So time complexity of this method is Log(n) + Log(n+1) … Log(m+n-1). The value of this expression will be between mLogn and mLog(m+n-1). As an optimization, we can pick the smaller tree as first tree.
Take all elements of first BST one by one, and insert them into the second BST. Inserting an element to a self balancing BST takes Logn time (See this) where n is size of the BST. So time complexity of this method is Log(n) + Log(n+1) … Log(m+n-1). The value of this expression will be between mLogn and mLog(m+n-1). As an optimization, we can pick the smaller tree as first tree.
Method 2 (Merge Inorder Traversals)
1) Do inorder traversal of first tree and store the traversal in one temp array arr1[]. This step takes O(m) time.
2) Do inorder traversal of second tree and store the traversal in another temp array arr2[]. This step takes O(n) time.
3) The arrays created in step 1 and 2 are sorted arrays. Merge the two sorted arrays into one array of size m + n. This step takes O(m+n) time.
4) Construct a balanced tree from the merged array using the technique discussed in this post. This step takes O(m+n) time.
1) Do inorder traversal of first tree and store the traversal in one temp array arr1[]. This step takes O(m) time.
2) Do inorder traversal of second tree and store the traversal in another temp array arr2[]. This step takes O(n) time.
3) The arrays created in step 1 and 2 are sorted arrays. Merge the two sorted arrays into one array of size m + n. This step takes O(m+n) time.
4) Construct a balanced tree from the merged array using the technique discussed in this post. This step takes O(m+n) time.
Time complexity of this method is O(m+n) which is better than method 1. This method takes O(m+n) time even if the input BSTs are not balanced.
Following is C++ implementation of this method.
Following is C++ implementation of this method.
#include <stdio.h> #include <stdlib.h> /* A binary tree node has data, pointer to left child and a pointer to right child */ struct node { int data; struct node* left; struct node* right; }; |
// A utility unction to merge two sorted arrays into one
int
*merge(
int
arr1[],
int
arr2[],
int
m,
int
n);
// A helper function that stores inorder traversal of a tree in inorder array
void
storeInorder(
struct
node* node,
int
inorder[],
int
*index_ptr);
/* A function that constructs Balanced Binary Search Tree from a sorted array
struct
node* sortedArrayToBST(
int
arr[],
int
start,
int
end);
/* This function merges two balanced BSTs with roots as root1 and root2.
m and n are the sizes of the trees respectively */
struct
node* mergeTrees(
struct
node *root1,
struct
node *root2,
int
m,
int
n)
{
// Store inorder traversal of first tree in an array arr1[]
int
*arr1 =
new
int
[m];
int
i = 0;
storeInorder(root1, arr1, &i);
// Store inorder traversal of second tree in another array arr2[]
int
*arr2 =
new
int
[n];
int
j = 0;
storeInorder(root2, arr2, &j);
// Merge the two sorted array into one
int
*mergedArr = merge(arr1, arr2, m, n);
// Construct a tree from the merged array and return root of the tree
return
sortedArrayToBST (mergedArr, 0, m+n-1);
}
/* Helper function that allocates a new node with the
given data and NULL left and right pointers. */
struct
node* newNode(
int
data)
{
struct
node* node = (
struct
node*)
malloc
(
sizeof
(
struct
node));
node->data = data;
node->left = NULL;
node->right = NULL;
return
(node);
}
// A utility function to print inorder traversal of a given binary tree
void
printInorder(
struct
node* node)
{
if
(node == NULL)
return
;
/* first recur on left child */
printInorder(node->left);
printf
(
"%d "
, node->data);
/* now recur on right child */
printInorder(node->right);
}
// A utility unction to merge two sorted arrays into one
int
*merge(
int
arr1[],
int
arr2[],
int
m,
int
n)
{
// mergedArr[] is going to contain result
int
*mergedArr =
new
int
[m + n];
int
i = 0, j = 0, k = 0;
// Traverse through both arrays
while
(i < m && j < n)
{
// Pick the smaler element and put it in mergedArr
if
(arr1[i] < arr2[j])
{
mergedArr[k] = arr1[i];
i++;
}
else
{
mergedArr[k] = arr2[j];
j++;
}
k++;
}
// If there are more elements in first array
while
(i < m)
{
mergedArr[k] = arr1[i];
i++; k++;
}
// If there are more elements in second array
while
(j < n)
{
mergedArr[k] = arr2[j];
j++; k++;
}
return
mergedArr;
}
// A helper function that stores inorder traversal of a tree rooted with node
void
storeInorder(
struct
node* node,
int
inorder[],
int
*index_ptr)
{
if
(node == NULL)
return
;
/* first recur on left child */
storeInorder(node->left, inorder, index_ptr);
inorder[*index_ptr] = node->data;
(*index_ptr)++;
// increase index for next entry
/* now recur on right child */
storeInorder(node->right, inorder, index_ptr);
}
/* A function that constructs Balanced Binary Search Tree from a sorted array
struct
node* sortedArrayToBST(
int
arr[],
int
start,
int
end)
{
/* Base Case */
if
(start > end)
return
NULL;
/* Get the middle element and make it root */
int
mid = (start + end)/2;
struct
node *root = newNode(arr[mid]);
/* Recursively construct the left subtree and make it
left child of root */
root->left = sortedArrayToBST(arr, start, mid-1);
/* Recursively construct the right subtree and make it
right child of root */
root->right = sortedArrayToBST(arr, mid+1, end);
return
root;
}
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