Design and analysis of algorithms

1. DESIGN AND ANALYSIS OF
ALGORITHMS
Dr. G.Geetha Mohan
Professor
Department of Computer Science and Engineering,
Jerusalem College of Engineering,
Chennai-600100
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2. Outline
 Notion of an Algorithm
 Fundamentals of Algorithmic Problem
Solving
 Important ProblemTypes
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3. What is a Computer Algorithm?
A sequence of unambiguous instructions
for solving a problem
For obtaining a required output for any
legitimate input in a finite amount of time
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4. Computer Algorithm Vs Program
 Computer algorithm
A detailed step-by-step method for
solving a problem using a computer
 Program
An implementation of one or more
algorithms.
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5. Find Greatest Common Divisor of
Two Nonnegative
1. Euclid’s algorithm
gcd(m n) = gcd(n, m mod n)
gcd(60, 24) = gcd(24, 12) = gcd(12, 0) = 12.
2. Consecutive integer checking
algorithm
 For numbers 60 and 24, the algorithm will
try first 24, then 23, and so on, until it
reaches 12, where it stops.
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6. Find Greatest Common Divisor of
Two Nonnegative
 Middle-school procedure
for the numbers 60 and 24, we get
60 = 2 . 2 . 3 . 5
24 = 2 . 2 . 2 . 3
gcd(60, 24) = 2 . 2 . 3 = 12.
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7. Notion of algorithm
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Problem
Algorithm
ComputerInput Output

8. Notion of algorithm
 Non-ambiguity requirement for each step of an
algorithm
 Range of inputs for which an algorithm works has
to be specified
 Same algorithm can be represented in several
different ways.
 Exist several algorithms for solving the same
problem.
 Algorithms for the same problem can be based on
very different ideas and can solve the problem
with dramatically different speeds.
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9. Understand the problem
Decide on: Computational means
Exact vs Approximate solution
Algorithm design technique
Design an algorithm
Data structures
Prove correctness
Analyze the algorithm
Code the algorithm
Algorithm Design And Analysis Process
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10. 1.Understand the problem
 Understand completely the problem
given
 Input
 Output
 An input to an algorithm specifies an
instance of the problem the algorithm
solves.
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11. 2.1 Ascertaining the Capabilities of
the Computational Device
 Sequential Algorithms
Instructions are executed one after another,
one operation at a time.
Random-access Machine (RAM)
 Parallel Algorithms
Execute operations concurrently( parallel)
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12. 2.2 Choosing between Exact and
Approximate Problem Solving
 Exact algorithm
 Approximation algorithm
 Extracting square roots,
 Solving nonlinear equations,
 Evaluating definite integrals
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13. 2.3 Algorithm Design Technique
 An algorithm design technique
(“strategy” or “paradigm”)
General approach to solving problems
algorithmically that is applicable to a variety
of problems from different areas of
computing.
 Algorithm design techniques make it
possible to classify algorithms according to
an underlying design idea
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14. 3.Designing an Algorithm and Data
Structures
 Several techniques need to be combined,
 Hard to pinpoint as applications of the
known design techniques
 Algorithms+ data structures = programs
 Object-oriented programming
Data structures remain crucially
important for both design and analysis of
algorithms.
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15. Methods of Specifying an Algorithm
 Pseudo code
A mixture of a natural language and
programming language
 Flowchart
A method of expressing an algorithm by a
collection of connected geometric shapes
containing descriptions of the algorithm’s
steps.
 Programs
Another way of specifying the algorithm
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16. 4.Prove Correctness
To prove that the algorithm yields a
required result for every legitimate input in
a finite amount of time
 A common technique for proving correctness is to use
mathematical induction
 Tracing the algorithm’s performance for a few specific
inputs to show that an algorithm is incorrect
The notion of correctness for
 Exact algorithms -straight forward
 Approximation algorithms- less straight forward
to be able to show that the error produced by the
algorithm does not exceed a predefined limit.
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17. 5.Analyze the Algorithm
 Efficiency
 Time efficiency,
How fast the algorithm runs,
 Space efficiency,
How much extra memory it uses.
Simplicity
Easier to understand and easier to program
Generality
Issues - Algorithm solves
Set of inputs it accepts
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18. 6. Code the Algorithm
 Programming an algorithm
both a peril and an opportunity.
 Test and debug program thoroughly
whenever implement an algorithm.
 Implementing an algorithm correctly is
necessary but not sufficient
 An analysis is based on timing the
program on several inputs and then
analyzing the results obtained.
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19. 6. Code the Algorithm (cond…)
 Algorithm’s Optimality.
 Not about the efficiency of an algorithm
 About the complexity of the problem it solves
 What is the minimum amount of effort any
algorithm will need to exert to solve the
problem?
 Another important issue of algorithmic problem
solving
 Whether or not every problem can be solved
by an algorithm.
 Not talking about problems that do not have a
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20. Algorithms
 Inventing (or discovering?) Algorithms is
a very creative and rewarding process.
 A good algorithm is a result of repeated
effort and rework
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21. Important Problem Types
 Sorting
 Searching
 String processing
 Graph problems
 Combinatorial problems
 Geometric problems
 Numerical problems
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22. 1. Sorting
 Rearrange the items of a given list in non
decreasing order
 Specially chosen piece of information- key
 Used as an auxiliary step in several important
algorithms in other areas
 Geometric Algorithms and Data Compression
 Greedy approach an important algorithm design
technique requires a sorted input.
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23. 1. Sorting
Two properties
 Stable
 Preserves the relative order of any two
equal elements in its input.
 In-place
 The amount of extra memory the
algorithm requires
 An algorithm is said to be in-place if it
does not require extra memory
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24. 2. Searching
 Deals with finding a given value, called a
search key, in a given set
(or a multiset, which permits several
elements to have the same value)
 Sequential Search or linear Search
 Binary search
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25. 2. Searching
 Considered in conjunction with two
other operations:
 Addition to
 Deletion from the data set of an item.
 Data structures and Algorithms should be
chosen to strike a balance among the
requirements of each operation.
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26. 3. String processing
 Sequence of characters from an alphabet
 Strings of particular interest are
 Text strings, which comprise letters,
numbers, and special characters;
 Bit strings, which comprise zeros and ones;
 Gene sequences, which can be modeled by
strings of characters from the four-
character alphabet {A,C, G,T}.
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27. String Matching
 Searching for a given word in a text
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28. 4. Graph Problems
 Collection of points called vertices,
 Some of which are connected by line
segments called edges.
 Used for modeling a wide variety of
applications
 Transportation,
 Communication,
 Social and Economic networks
 Project scheduling
 Games.
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29. Graph Problems
 Basic graph algorithms
 Graph traversal algorithms
How can one reach all the points in a
network?
 Shortest-path algorithms
What is the best route between two cities?
 Topological sorting for graphs with
directed edges
set of courses with their prerequisites
consistent or self-contradictory?.
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30. Graph Problems
 Traveling Salesman Problem (TSP)
Problem of finding the shortest tour
through n cities that visits every city
exactly once.
 Route Planning
 Circuit board
 VLSI chip fabrication
 X-ray crystallography
 Genetic Engineering
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31. Graph Problems
 Graph-coloring problem
 seeks to assign the smallest number of
colors to the vertices of a graph so that
no two adjacent vertices are the same
color
Event Scheduling:
Events are represented by vertices that are connected
by an edge if and only if the corresponding events
cannot be scheduled at the same time, a solution to the
graph-coloring problem yields an optimal schedule.
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32. 5. Combinatorial Problems
 Traveling Salesman Problem and the
Graph Coloring Problem are examples of
combinatorial problems.
 These are problems that ask, explicitly or
implicitly, to find a combinatorial object
such as a permutation, a combination, or a
subset that satisfies certain constraints.
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33. 5. Combinatorial Problems
 Issues
 Number of combinatorial objects typically grows
extremely fast with a problem’s size, reaching
unimaginable magnitudes even for moderate-
sized instances.
 There are no known algorithms for solving most
such problems exactly in an acceptable amount
of time.
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34. 6.Geometric Algorithms
 Geometric algorithms deal with geometric
objects such as points, lines, and polygons.
 Ancient Greeks -developing procedures
For solving a variety of geometric problems,
 Constructing simple geometric shapes
-triangles, circles
 Computer graphics
 Robotics
 Tomography
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35. 6.Geometric Algorithms
 Closest-pair problem
Given n points in the plane, find the closest
pair among them.
 Convex-hull problem
To find the smallest convex polygon that
would include all the points of a given set.
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36. 7. Numerical problems
 Problems that involve mathematical
objects of continuous nature
 Solving equations
 Systems of equations,
 Computing definite integrals,
 Evaluating functions
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37. 7. Numerical problems
 Mathematical problems can be solved
only approximately
 Problems typically require manipulating
real numbers, which can be represented
in a computer only approximately.
 Large number of arithmetic operations
performed on approximately represented
numbers can lead to an accumulation of
the round-off
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38. Modeling the Real World
 Cast your application in terms of well-studied abstract
data structures
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Concrete Abstract
arrangement, tour, ordering, sequence permutation
cluster, collection, committee, group, packaging, selection subsets
hierarchy, ancestor/descendants, taxonomy trees
network, circuit, web, relationship graph
sites, positions, locations points
shapes, regions, boundaries polygons
text, characters, patterns strings

39. Real-World Applications
 Hardware design: VLSI
chips
 Compilers
 Computer graphics: movies,
video games
 Routing messages in the
Internet
 Searching the Web
 Distributed file sharing
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• Computer aided design and
manufacturing
• Security: e-commerce,
voting machines
• Multimedia: CD player, DVD,
MP3, JPG, HDTV
• DNA sequencing, protein
folding
• and many more!

40. Some Important Problem Types
 Sorting
◦ a set of items
 Searching
◦ among a set of items
 String processing
◦ text, bit strings, gene
sequences
 Graphs
◦ model objects and their
relationships
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• Combinatorial
– find desired permutation,
combination or subset
• Geometric
– graphics, imaging,
robotics
• Numerical
– continuous math: solving
equations, evaluating
functions

41. Algorithm Design Techniques
• Brute Force & Exhaustive
Search
– follow definition / try all
possibilities
• Divide & Conquer
– break problem into
distinct subproblems
• Transformation
– convert problem to
another one
• Dynamic Programming
– break problem into overlapping
sub problems
• Greedy
– repeatedly do what is best now
• Iterative Improvement
– repeatedly improve current
solution
• Randomization
– use random numbers
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