In regards to binary search tree, can you answer why a BST with N nodes has at least log2N levels and at most N levels. so the runtime complexity is best case 0(logN) and worst case 0(N). Can you explain this with the following numbers in this order? 7,1,64,28,77
There are two cases for a BST, balanced BST and unbalanced BST. Unbalanced BST is a skewed BST. For a balanced BST, for every node, the difference between the left height and right height is atmost 1.
For a balanced BST, there will be logN levels and for an unbalanced tree, there will be N levels. Time complexity is O(height) where height is the height of the tree. For a balanced BST, height is logN and for an unbalanced BST, height is N, hence the time complexity.
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In regards to binary search tree, can you answer why a BST with N nodes has...
a. The INORDER traversal output of a binary tree is U,N,I,V,E,R,S,I,T,Y and the POSTORDER traversal output of the same tree is N,U,V,R,E,T,I,S,I,Y. Construct the tree and determine the output of the PREORDER traversal output. b. One main difference between a binary search tree (BST) and an AVL (Adelson-Velski and Landis) tree is that an AVL tree has a balance condition, that is, for every node in the AVL tree, the height of the left and right subtrees differ by at most 1....
Show that any binary search tree with n nodes can be transformed into any other search tree using O(n) rotations. Also show that you need at most n - 1 right rotations to transform a tree into a chain.
In general, assuming a balanced BST with n nodes (A balanced binary tree has roughly the same number of nodes in the left and right subtrees of the root), what is the maximum number of operations required to search for a key? Please notice that the tree in this exercise is not balanced. Trace the algorithm for creating a parse tree for the expression (((4 x 8)/6)–3 Please help me understand :(
Use the Binary Search Tree (BST) insertion algorithm to insert 0078 into the BST below. List the nodes of the resulting tree in pre-order traversal order separated by one blank character. For example, the tree below can be described in the above format as: 75 53 24 57 84 77 76 82 92 0075 0053 0084 0024 0057 0077 OON 0076 0082
A binary search tree includes n nodes and has an height h. Check all that applies about the space complexity of TREE-MINIMUMX) TREE-MINIMUM () 1 while x. left NIL 2 3 return x x x.left O it is e (lg n) ■ it is 0(h). D it is e (1) ■ It is in place ■ it is Θ (n) A binary search tree includes n nodes and has an height h. Check all that applies about the space complexity...
Use the Binary Search Tree (BST) deletion algorithm to delete 0075 from the BST below. List the nodes of the resulting tree in pre-order traversal order separated by one blank character. For example, the tree below can be described in the above format as: 75 53 24 57 84 77 76 82 92 0075 0053 0034 0024 0057 0077 0092 0078 0082
a. How can I show that any node of a binary search tree of n nodes can be made the root in at most n − 1 rotations? b. using a, how can I show that any binary search tree can be balanced with at most O(n log n) rotations (“balanced” here means that the lengths of any two paths from root to leaf differ by at most 1)?
1. What is the worst case time complexity of insertion into a binary search tree with n elements? You should use the most accurate asymptotic notation for your answer. 2. A binary search tree is given in the following. Draw the resulting binary search tree (to the right of the given tree) after deleting the node with key value 8. 10 3. You have a sorted array B with n elements, where n is very large. Array C is obtained...
17) Which of the following is a valid binary search tree? 23 12 40 19 30 61 13 21 41 50 23 43 10 18 34 51 15 21 40 50 23 43 10 18 51 15 27 40 50 What is the worst-case runtime of searching for a value in a binary search tree? (a) constant O(1) (b) logarithmic O(logn) (c) linear O(n) 18) Pag
fill in the blank Binary Search Tree AVL Tree Red-Black Tree complexity O(log N), O(N) in the worst case O(log N) O(log N) Advantages - Increasing and decreasing order traversal is easy - Can be implemented - The complexity remains O(Log N) for a large number of input data. - Insertion and deletion operation is very efficient - The complexity remains O(Log N) for a large number of input data. Disadvantages - The complexity is O(N) in the worst case...