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Winter 2015 ASSIGNMENT

PROGRAM - Master of Science in Information Technology (MSc IT)Revised Fall 2011

SEMESTER - 2

SUBJECT CODE & NAME – mca4040- ANALYSIS AND DESIGN OF ALGORITHMS

CREDIT 4 BK ID B1480 MAX. MARKS 60

Q1. List and explain the properties of Algorithms.

Answer: Properties of algorithmsAn algorithm may have zero or more inputs externally and it should produce one or more output. Also, an algorithm must terminate after a finite number of steps. Properties of algorithm include:

1. Correctness2. Definiteness

Q2. Write a detail note on sequential search.

Answer:

Definition – Sequential search is a process for finding a particular value in a list that checks every element (one at a time) in sequence till the desired element is found.

Sequential search is the simplest brute force search algorithm. It is also known as linear search. This search is a special case of brute-force search.The

Q3. What is mean by Topological sort? And explain with example. 5+5=10

Answer: Topological sort is done using a directed acyclic graph (DAG), which is a linear ordering of all vertices G= (V, E) is an ordering of all vertices such that if G contains an edge (u, v), then u appears before v in the ordering. A topological sort of a particular graph can be looked upon as a horizontal line where all directed edges travel from left to right. Thus, topological sort differs

4. Explain good-suffix and bad-character shift in Boyer-Moore algorithm. 5+5=10

Answer: Good suffix Shift

This shift helps in shifting a matched part of the pattern, and is denoted by Q. Good suffix shift Q is applied after 0 < k < m characters are matched.

Q =

Q5. Explain Lower – Bound Arguments? What are the methods help to make an algorithm more efficient?Answer:Lower–bound means calculating the minimum amount of workrequired to solve the problem. While obtaining the lower–bound of thealgorithm we look for the limits of efficiency of any algorithm

Q6. Write an approximation Algorithms for NP – Hard Problems.Answer:Combinatorial optimization problems lie within a finite but huge feasible region.

Underlying principlesAn NP-Hard problem is one for which the algorithm can be translated to one that can solve any NP-problem (non-deterministic polynomial time). Many optimization problems do not have an algorithm

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