Design and Analysis of algorithms unit 1 notes

SCSX1001-DESIGN AND ANALYSIS OF
ALGORITHMS

Syllabus:
UNIT 1 : INTRODUCTION TO COMPUTER PROBLEM SOLVING 12 Hrs
Introduction, The Problem-Solving aspect, top-down design-Implementation of Algorithms-program
verification-The efficiency of algorithms-The analysis of algorithms-Fundamental Algorithms: Introduction-
Exchanging the values of two variables-Counting-Summation of a set of Numbers-factorial computation-Sine
function computation-Generation of the Fibonacci sequence-Reversing the digits of an integer, base
converstion-Character to Number conversion.
UNIT 2 : FACTORING METHOD 12 Hrs
Introduction-Finding the square root of a number-The smallest divisor of an integer-The greatest common
divisor of two integers-Generating Prime Numbers-Computing the Prime Factors of an integer-Generation of
Psedo-random Numbers-Raising a Number to a Large Power-Computing the nth Fibonacci Number.
UNIT 3 : ARRAY TECHNIQUES 12 Hrs
Introduction-Array Order Reversal-Array Counting or Histogramming-Finding the maximum Number in a
Set-Removal of Duplicates from an Ordered Array-Partitioning an Array-Finding the kth smallest Element-
Longest Monotone Subsequence.
UNIT 4 : MERGING SORTING AND SEARCHING 12 Hrs
Introduction, The Two-way Merge-Sorting by Selection-Sorting by Exchange-Sorting by Insertion-Sorting by
Diminishing Increment-Sorting by Partitioning-Binary Search-Hash Searching.
UNIT 5 : TEXT PROCESSING AND PATTERN SEARCHING 12 Hrs
Introduction-Text Line Length Adjustment-Left and Right Justification of Text-Keyword Searching in Text-
Text Line Editing-Linear Pattern search- Sublinear Pattern Search.
SCSX1001
DESIGN AND ANALYSIS
OF ALGORITHMS
L T P Credits
Total
Marks
3 0 0 3 100

TEXT BOOK :
1.Dromey.R.G,”How to Solve it by Computer”,Prentice-Hall of India,Eighth Indian
Reprint,1996.
REFERENCE BOOKS :
1.Aho.A.V.,Hopcroft.J.E and Ullman.J.D,”The Design and Analysis of Computer
Algorithms”,Addison-Wesley,Reading Mass.
2.Knuth,D.E.,”The Art of computer programming Vol 1:Fundamental
Algorithms”,Addison – Wesley , Reading Mass
3.Knuth,D.E.,”Mathematical Analysis of algorithms”,Proceedings IFIP
congress,Ljubljana
UNIVERSITY EXAM QUESTION PAPER PATTERN
Max Marks : 80 Exam Duration: 3 Hrs.
PART A : 10 questions of 2 marks each – No choice 20 marks
PART B : 5 questions from each of the FIVE units of internal choice, carrying 12
marks each 60 marks

Aim:
 This deals with the complete development of algorithm
for complicated problems. It also deals with the design
techniques and analysis of algorithms for efficiency,
complexity and overall effectiveness.
Objectives:
To analyze the efficiency of different algorithms for the
same problem.
To study the various algorithm design techniques.

UNIT 1 : INTRODUCTION TO COMPUTER PROBLEM SOLVING
Introduction:
• Algorithm
Step by step procedure for solving a problem
Solution to a problem that is independent of any
Programming language
An algorithm is a sequence of computational steps that
transform the input into the output”
Correct algorithm halts with the correct output for every
input instance
“Algorithm is any well-defined computational procedure that
takes some value, or set of values, as input and produces some
value, or set of values, as output.

Algorithm criteria:
All algorithms must satisfy the following criteria:
1) Input
2) Output
3) Definiteness
4) Finiteness
5) Effectiveness.
For example : sorting problem
Input: A sequence of n numbers 31, 41, 59, 26, 41, 58
Output: Reordering of the input sequence26, 31, 41, 41, 58, 59
Definiteness: Sorting Technique
Finiteness: Procedure for proper concerns
Effectiveness: High Throughput
.

• Programs:
Set of instructions expressed in any programming
Language (C,C++,JAVA,VB,J2EE,.NET etc.,)
A program is the expression of an algorithm in a
programming language.
• Data structure:
A data structure is a way to store and organize data in order to
facilitate access and modifications.
Program=Algorithm + Data structure

Expressing Algorithms:
 English description
 Pseudo-code
Pseudo-code:
 Like a programming language but its rules are less
 Written as a combination of English and programming
constructs
 Based on selection (if, switch) and iteration (while, repeat)
constructs in high-level programming languages
Independent of actual programming language

The Problem-Solving aspect:
“computer”
Problem
Algorithm & Programming Language with data structure
input output
Requirements for solving problems by computer
•Algorithm
• Data Structure
• Programming Language

The Problem solving aspect phases:
1. Problem definition phase
2. Getting started on a problem
3. The use of specific examples
4. Similarities among problems
5. Working backwards from the solution
6. General problem solving strategies
1. Problem Definition Phase:
 Understand the problem (success in solving)
 Work out what must be done rather than how to do it
 Lot of care must be taken in working out what must be done
 Example :
 Finding the square root
 Finding the greatest common divisor
 From the definition develop an algorithm

2.Getting started on a problem
• There may be many ways to solve the problem and also
many solutions to most problems.
• Gather more detail about problem
• You start coding for the problem
3.The use of specific examples
 Use some properties to try to get a solution for the problem
Example : Find out the maximum number from the given set
of numbers
 Use some geometrical or schematic diagrams representing
certain aspects of the problem

4. Similarities among problems:
 See if there are any similarities between the current problem
and other problems that we have solved or we have seen solved
 Try to solve the problem independently
5. Working backwards from the solution
If we do not know where to start on a problem,
•We can work backwards to the starting conditions (if the
expected result and initial conditions are known)
•Whatever attempts that we make to get started on a
problem write down the various steps and explorations we
made
•Once we have solved a problem we must remember the
steps that we went about discovering the solution
•The most important one is practice

6.General Problem solving strategies:
Divide and Conquer
Binary doubling
Dynamic programming
Backtracking
Branch and Bound
Greedy method
Divide and Conquer:
It is defined as one large complex problem is divided into
number of sub problems and finds the solution. The sub
problem solutions are combine to form the solution for large
problem.
Example: Merge sort algorithm
Binary doubling:
convert the binary numbers to decimal numbers
Example:: 1->1,10->2,11->3,100->4,1000->8

Dynamic programming:
Dynamic programming is an algorithm that can be used
when the solution to a problem can be viewed as the result of
a sequence of decisions.
Example: TRAVELING SALESMAN PROBLEM
Backtracking: During the search if infeasible solution is
sensed then backtrack to previous node.
Example: 8-queens problem
Branch and Bound:
Branch- splitting procedure
Bound- computes upper and lower bounds
Example: Knapsack problem
Greedy method: Find the feasible solution from the set of
solution for the given problem
Example: job scheduling

Design
 Technique for Designing algorithm
 Another name for top down design is Stepwise Refinement
 It provides a way of handling complexity
 It allows us to build our solutions to a problem in a stepwise
fashion
2.1 Breaking Problem into Sub problems :

Divide the task into subtasks.
 Way in which the subtask need to interact with each other.
 Subtask into smaller subtask.

 2.2 Choice of a suitable Data Structure
Inappropriate choice DS leads to clumsy, inefficient and
difficult in implementation.
 Small change in Data Organization can have a
significant influence on the algorithm required to solve
the problem.
Influence Of Data Structure :
1.Can the data structure be easily searched?
2. Can the data structure be easily updated?
3. Does the data structure provide a way of recovering
an earlier state in the computation ?
4. Does the data structure involve the excessive use of
storage?
5. Can the problem be formulated in terms of one of the
common data structures (e.g. array, queue, stack, tree
graph, list)

 2.3 Construction of Loops: To implement
subtasks series of Iterative constructs or loops are needed
Loop: It is defined as repetitively execute the set of
instructions defined number of times.
 Eg.. While, for
To construct any loop consider 3 things
• Initial Conditions that need to apply before the loop
begins to execute i=0;
• Invariant relation that must apply after each iteration
of the loop i<=10; i++
• Conditions under which the iterative process must
terminate
for(i=0;i<=10;i++) Trouble In constructing loops
• Getting correct initial condition.
• Getting right number of times to execute loop.

2.4 Establishing initial conditions for
loops
– Set the loop variables
Variable: Value can be changed during run time
– Set the number of iterations ‘n’ in the range I=0 to
n-1
– Example : Find the sum of set of numbers
• Solution :
–Set the loop variable as ‘i’
–Sum variable as ‘S’
–The sum of zero numbers is zero and so
the initial
values of ‘i’ and ‘S’ as zero
i : = 0; S : = 0;

• 2.5 Finding the iterative construct
• Once we know the conditions for solving the
smallest problem next step is try to extend it
to the next smallest problem
Solution to summation problem for n>=0
n= 5;
while i < n do
begin
i= i+1;
S=S+a[i]
end
• Need to consider about the termination of
loops

• 2.6 Termination of loops
 Number of iterations are known in advance
Example :
for i = 1 to n do
begin
.
.
end
The above loop terminates unconditionally after ‘n’ iterations
 Terminates only When some conditional expression becomes
false
Example :
while (x>0) and (x<10) do
begin
.
end

3.Implementation of Algorithms
– Use of procedures to emphasize modularity
– Choice of variable names
– Documentation of programs
– Debugging programs
– Program testing

3.1 Use of procedures to emphasize modularity
Modularity : “one complex larger task is divided into number of
smaller tasks for simplicity”
 In the first phase of implementation just place a write statement
in the skeleton procedure indicating the name of procedure
 Implement and test the procedures one by one
 Implement a set of procedures to perform specific and well
defined tasks
 Procedure should not be lengthy
 Main program can be implemented with calls to various
procedures
Example :
procedure quicksort;
begin
writeln/printf(‘Sort called ’);
end

• 3.2 Choice of variable names
 Make the programs more meaningful and easier to
understand by choosing appropriate variable and
constant names
 Example: To make manipulations on the days of
the week use variable name “day” instead of “a” .
 Each variable should have only one role in a given
program
 Define all variables and constants at the start of
each procedure clearly.
•

• 3.3 Documentation of programs
 Provide an accurate comment with each begin statement
used
 Because begin statement tells that some modular part of
the Computation is about to follow
 Provide an information about the programs to the user
during the execution phase
 Write a program in such a way that they can be used and
executed by the people who are not familiar with
language.
 Provide a user manual
 The program should “catch” incorrect responses to its
requests and inform the user in an appropriate manner

• 3.4 Debugging Programs
Debugging: “process of finding and reducing or fix the
number of errors, or defects, in a computer program”
• While implementing an algorithm check whether the
program is working correctly for the given
specifications
• Detect logical errors during the compilation phase
• Workout the program before attempting to execute it
• The program is executing but producing incorrect
results mean
• Use a debugging trace to print out necessary
information
• Follow the program through by hand stepwise and
check against the computer debugging output

Example : (Binary Search)
lower := 1;
upper := n;
while lower<upper do
begin
middle :=(lower+upper) div 2;
if x>a[middle] then
lower:=middle+1
else
upper:=middle
end;
found:=(x=a[lower])

Design and Analysis of algorithms unit 1 notes

3.5 Program Testing
 Check whether the program solves the
smallest possible problem
 Check whether it can handle the case
when all data values.
 whether it is able to handle all input
condition?

Efficiency of algorithm mainly depends upon
 Design
 Implementation
 Analysis of algorithms
Every algorithm must use some of a computer’s resources to
complete its task (Ex : CPU time, internal memory).
Suggestions to design efficient algorithms
 Redundant computations
 Referencing array elements
 Inefficiency due to late termination
 Early detection of desired output conditions
 Trading storage for efficiency gains

5.1 Redundant computations :
Problem here is
 Unnecessary storage is used
 Effect will be more serious when it is embedded within a loop
The most common mistake is
 Recalculate part of an expression that remains constant throughout
the entire execution phase of the loop repeatedly
Example :
x:=0;
for i:=1 to n do
begin
x:=x+0.01;
y:=(a*a*a+c)*x*x+b*b*x;
writeln(‘x=‘,x,’y=‘,y)
end

The unnecessary multiplications and additions can be
removed by pre computation before executing the loop
a3c := a*a*a+c;
b2:=b*b;
X:=0;
For i:=1 to n do
begin
x:=x+0.01;
y:=a3c*x*x+b2*x;
writeln(‘x=‘,x,’y=‘,y)
end

5.2 Referencing Array elements :
Example : To Find the maximum and its position in an array
Version 1 :
p:=1;
for i:=2 to n do
if a[i]>a[p] then p:=i;
max:=a[p]
Version 2 :
p:=1;
max:=a[1];
for i:=2 to n do
if a[i]>max then
begin
max:=a[i];
p:=i
end
max : = a[p]
–

–The version 2 is preferred because the
conditional test ( a[i] > max) is more
efficient than the test in version 1
– Use of variable max needs only
one memory reference
– Introduction of variable max
makes it clear what task is to be
accomplished
In version 1, a[p] requires 2 memory
references

5.3 Inefficiency due to late termination :
 While linear search an alphabetically ordered list of names
 In an inefficient implementation all names were examined
even if the point in the list was reached
// Inefficient Algorithm
while name s < > c and no end-of-file do
get next name from list
A more efficient implementation would be
while name s > c and not end-of-file do
get next name from list
test if c is equal to name s

5.4 Early detection of desired output conditions :
It sometimes happen due to the nature of the input data, that
algorithm establishes desired output condition before the
general conditions for termination have been met.
Eg) Floating point Add/Sub
5.5 Trading storage for efficiency gains :
 To improve the performance of an algorithm
 Avoid unnecessary test and computation.

VERIFICATION
 Program verification
- Application of mathematical proof techniques
- Check the results obtained by the execution of a
program with
arbitrary inputs according to the output specifications

4.1 Computer Model for program execution :
 We must know what happens when a program is executed under the
given input conditions
 Execution path that is followed for the given input conditions
- Program may have more execution paths leading to successful
termination
- For a given set of input conditions only one of these path will be
followed
 Progress of computation may be thought of as a sequence of
transitions from one computation state to another
 Each state is defined by the values of all variables at the corresponding
point in
time
 A state transition and progress towards completion is made by
• Changing the value of variable
• Transfer of control to the next instruction on the current
execution path
• Instruction that change the computation state
• Instruction that performs a test on the current state

4.2 Input and Output assertion
 Formal statement has two parts
• Input assertion
• Output assertion
- describe the State of executing program’s variables
 Input Assertion
- should specify any constraints that have been placed
on the
values of input variables used by the program
Example : divide by zero (variable x, value 0)
Input assertion becomes x< >0
When there are no restrictions on the values of input
variables the input assertion will have the Boolean value
true

 Output Assertion
- specify symbolically the results for input data that
satisfies the
input assertion
Example :
Design a program to calculate the quotient q and the remainder r
resulting from the division of x by y

4.3 Verification of program segments with
branches
- To handle program segments that contain branches
- Set up and prove verification conditions for each branch separately
Example : x is less than or equal to y
readln(x,y);
A {assert PA:true}
if x>y then
begin
t:=x;
x:=y;
y:=t;
end
B {assert PB((x<=y)^(x=xo^y=y0))V(x=y0^y=x0)}

Design and Analysis of algorithms unit 1 notes

Recommended

More Related Content

Similar to Design and Analysis of algorithms unit 1 notes (20)

Recently uploaded (20)

Design and Analysis of algorithms unit 1 notes