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Amity University
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**Computer Science Engineering**Downloads:
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Chapter 1. Introduction
1.1 Need for studying algorithms: The study of algorithms is the cornerstone of computer
science.It can be recognized as the core of computer science. Computer programs would
not exist without algorithms. With computers becoming an essential part of our professional & personal life’s, studying algorithms becomes a necessity, more so for computer
science engineers.
Another reason for studying algorithms is that if we know a standard set of important
algorithms ,They further our analytical skills & help us in developing new algorithms for
required applications
1.2 ALGORITHM
An algorithm is finite set of instructions that is followed, accomplishes a particular task. In
addition, all algorithms must satisfy the following criteria:
1. Input. Zero or more quantities are externally supplied.
2. Output. At least one quantity is produced.
3. Definiteness. Each instruction is clear and produced.
4. Finiteness. If we trace out the instruction of an algorithm, then for all cases, the algorithm terminates after a finite number of steps.
5. Effectiveness. Every instruction must be very basic so that it can be carried out, in
principal, by a person using only pencil and paper. It is not enough that each operation
be definite as in criterion 3; it also must be feasible.

COMPUTER
Fig 1.a.
An algorithm is composed of a finite set of steps, each of which may require one or more operations. The possibility of a computer carrying out these operations necessitates that certain
constraints be placed on the type of operations an algorithm can include. The fourth criterion
for algorithms we assume in this book is that they terminate after a finite number of operations.
Criterion 5 requires that each operation be effective; each step must be such that it can, at least
in principal, be done by a person using pencil and paper in a finite amount of time. Performing
arithmetic on integers is an example of effective operation, but arithmetic with real numbers is
not, since some values may be expressible only by infinitely long decimal expansion. Adding
two such numbers would violet the effectiveness property.
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Algorithms that are definite and effective are also called computational procedures.
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The same algorithm can be represented in same algorithm can be represented in several ways
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Several algorithms to solve the same problem
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Different ideas different speed
Example:

Problem:GCD of Two numbers m,n
Input specifiastion :Two inputs,nonnegative,not both zero
Euclids algorithm
-gcd(m,n)=gcd(n,m mod n)
Untill m mod n =0,since gcd(m,0) =m
Another way of representation of the same algorithm
Euclids algorithm
Step1:if n=0 return val of m & stop else proceed step 2
Step 2:Divide m by n & assign the value of remainder to r
Step 3:Assign the value of n to m,r to n,Go to step1.
Another algorithm to solve the same problem
Euclids algorithm
Step1:Assign the value of min(m,n) to t
Step 2:Divide m by t.if remainder is 0,go to step3 else goto step4
Step 3: Divide n by t.if the remainder is 0,return the value of t as the answer and
stop,otherwise proceed to step4
Step4 :Decrease the value of t by 1. go to step 2
1.3 Fundamentals of Algorithmic problem solving
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Understanding the problem
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Ascertain the capabilities of the computational device
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Exact /approximate soln.
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Decide on the appropriate data structure
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Algorithm design techniques
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Methods of specifying an algorithm
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Proving an algorithms correctness
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Analysing an algorithm

Understanding the problem:The problem given should be understood completely.Check if it is similar to some standard problems & if a Known algorithm exists.otherwise a new algorithm has to be devised.Creating an algorithm is an art which
may never be fully automated. An important step in the design is to specify an instance of the problem.
Ascertain the capabilities of the computational device: Once a problem is understood we need to Know the capabilities of the computing device this can be done by
Knowing the type of the architecture,speed & memory availability.
Exact /approximate soln.: Once algorithm is devised, it is necessary to show that it
computes answer for all the possible legal inputs. The solution is stated in two
forms,Exact solution or approximate solution.examples of problems where an exact
solution cannot be obtained are i)Finding a squareroot of number.
ii)Solutions of non linear equations.
Decide on the appropriate data structure:Some algorithms do not demand any ingenuity in representing their inputs.Someothers are in fact are predicted on ingenious
data structures.A data type is a well-defined collection of data with a well-defined set
of operations on it.A data structure is an actual implementation of a particular abstract
data type. The Elementary Data Structures are
ArraysThese let you access lots of data fast. (good) .You can have arrays of any other da
ta type. (good) .However, you cannot make arrays bigger if your program decides it needs
more space. (bad) .
RecordsThese let you organize non-homogeneous data into logical packages to keep everything together. (good) .These packages do not include operations, just data fields (bad, which
is why we need objects) .Records do not help you process distinct items in loops (bad, which
is why arrays of records are used)
SetsThese let you represent subsets of a set with such operations as intersection, union, and
equivalence. (good) .Built-in sets are limited to a certain small size. (bad, but we can build our
own set data type out of arrays to solve this problem if necessary)

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