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QUESTION
Explain the following data structures:
(a) List
(b) Stack
(c) Queues
Note : your explanation should consist of the definition, operations and examples.
Using insertion sort arrange the following numbers in ascending order. Show each step and intermediate results. Write the algorithm.10 2 3 4 11 9 8 5 12 1 6 7 0 -1 20
What will be depth do , of complete binary tree of n nodes, where nodes are labelled from 1 to n with root as node and last leaf node as node n
What do you understand by tree traversal? The algorithm walks by the tree data structure and performs some computation at everynode in the tree. This process of walking by the
QUESTION (a) Define a tree and list its properties. (b) By showing all your workings, draw the spanning tree for the following graph based on the Breadth-First-Search algori
Q. Explain w hat are the stacks? How can we use the stacks to check whether an expression is correctly parentheses or not. For example (()) is well formed but (() or )()( is not w
For splaying, three trees are maintained, the central, left & right sub trees. At first, the central subtree is the complete tree and left and right subtrees are empty. The target
Simplifying Assumptions of wire frame representation Neglect colour - consider Intensity: For now we shall forget about colour and restrict our discussion just to the intensi
Q. What do you understand by the term Binary Tree? What is the maximum number of nodes which are possible in a Binary Tree of depth d. Explain the terms given below with respect to
Q. Devise a representation for a given list where insertions and deletions can be made at both the ends. Such a structure is called Deque (which means Double ended queue). Write fu
How memory is freed using Boundary tag method in the context of Dynamic memory management? Boundary Tag Method to free Memory To delete an arbitrary block from the free li
This notation gives an upper bound for a function to within a constant factor. Given Figure illustrates the plot of f(n) = O(g(n)) depend on big O notation. We write f(n) = O(g(n))
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