Even in the worst case scenario, using a self-balancing binary search tree to do a binary tree sort takes O(n log n) time, which is much longer than the time required for merge sort.

Binary tree sort employing a self-balancing binary search tree takes O(n log n) time in the worst scenario, slower than merge sort.

Even when employing a self-balancing binary search tree to construct binary tree sort, the worst-case time required is O(n log n), making it slower than merge sort.

Trace the execution of Bubble-sort on the list: 30, 7, 21, 42, 57, 90, 3, 11

Write a detailed algorithm/description explaining how to efficiently solve this problem: Suppose two binary search trees (T1, T2) have already been created. Determine whether these two trees are the same--they have the same number of elements and have the same elements. You may use any of the tree and BST methods discussed in lectures, and you can choose whether the trees are implemented as linked trees, or are stored in arrays. Note: While T1 and T2 might be the same (as defined above) they do not need to have the same structure (shape). They might organize the data in the tree in different ways. Estimate the growth function and big O value ofyour algorithm; explain how you arrive at your answer.

Given a sorted array of n comparable items A, create a binary search tree from the items in A which has height h <= log: n. Your algorithm must create the tree in O(n) time.