Much has been said in praise of... this paper, we compare the performance of three different techniques for self-adjusting trees with that of AVL and random binary search trees. Comparisons are made for various tree sizes, levels of key-access-frequency skewness and ratios of insertions and deletions to searches. The results show that, because of the high cost of maintaining self-adjusting trees, in almost all cases the AVL tree outperforms all the self-adjusting trees and in many cases even a random binary search tree has better performance, in terms of CPU time, than any of the self-adjusting trees. Self-adjusting trees seem to perform best in a highly dynamic environment, contrary to intuition.

Till current date in majority books on algorithm and research papers, they talk about height of a binary tree in terms like height balanced binary tree. In this paper the notion of width of a binary tree has been introduced and later the recursive algorithm based on the traversal techniques of the binary tree is given. Later the iterative version of algorithm using the notion of stack is introduced. The width of a binary tree is defined based on the number of nodes at every level. The highest of all is the width of a binary tree. The same concept can be applied to the general tree.