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In this unit, we described about the data structure Queue. It had two ends. One is front from where the elements can be removed and the other is rear where the elements can be inserted. A queue can be implemented via Arrays or Linked lists. Each illustration is having it's own advantages & disadvantages. The problems along arrays are that they are restricted in space. Therefore, the queue is having a restricted capacity. If queues are implemented via linked lists, then this problem is solved. Now, there is no restriction on the capacity of the queue. The only overhead is the memory occupied though the pointers.
There are a number of variants of the queues. Queues normally mean circular queues. In spite of linear queues, we also discussed circular queues in this unit. A particular type of queue called Dequeue was also discussed in this unit. Dequeues allows elements to be inserted or deleted at either of the rear or front. We also discussed the array & linked list implementations of Dequeue.
Q. Explain various graph traversal schemes and write their advantages and disadvantages. A n s . Graph Traversal Scheme is explained below In many troubles we wish
implement multiple stacks ina single dimensional array. write algorithams for various stack operation for them.
How to create an General Tree and how to search general tree?
Q. Illustrate the result of running BFS and DFS on the directed graph given below using vertex 3 as source. Show the status of the data structure used at each and every stage.
Suppose we have a set of N agents and a set of N tasks.Each agent can only perform exactly one task and there is a cost associated with each assignment. We would like to find out a
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Example which cause problems for some hidden-surface algorithms Some special cases, which cause problems for some hidden-surface algorithms, are penetrating faces and cyclic ov
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Encryption the plain-text using the round keys: 1. (Key schedule) Implement an algorithm that will take a 128 bit key and generate the round keys for the AES encryption/decryp
Explain principle of Optimality It indicates that an optimal solution to any instance of an optimization problem is composed of optimal solutions to its subinstances.
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