01 Revision Introduction SLides Od Design ANd Aalaysis Of aLgo

Analysis of Algorithm
Mr. Mr. Fazl-e-Basit
Prof. Dr. Awais Adnan
Lecture 01: Introduction

Mr. Fazl-e-Basit
Origin of word: Algorithm
• The word Algorithm comes from the name of the muslim author Abu
Ja’far Mohammad ibn Musa-al-Khowarizmi. He was born in the
eighth century at Khwarizm (Kheva), a town south of river Oxus in
present Uzbekistan. Uzbekistan, a Muslim country for over a
thousand years, was taken over by the Russians in 1873.
• Much of al-Khwarizmi’s work was written in a book titled al Kitab al-
mukhatasar fi hisab al-jabrwa’l-muqabalah (The Compendious Book
on Calculation by Completion and Balancing). It is from the titles of
these writings and his name that the words algebra and algorithm are
derived. As a result of his work, al-Khwarizmi is regarded as the most
outstanding mathematician of his time

Mr. Fazl-e-Basit
• Informally, an algorithm is any well-defined computational
procedure that takes some value or set of values as input and
produce some value or set of values as output.
or
• We can also view an algorithm as a tool for solving a well
specified computational problem.
or
• We can say algorithm is a sequence of operations to solve
problems correctly.
Algorithm?

Mr. Fazl-e-Basit
Examples of everyday algorithms:
• Recipe for baking a cake
• Instructions for assembling furniture
• Steps for solving a math problem
• Sorting a deck of cards
•
•
•
Algorithms are not limited to programming but
are used in various contexts.

Mr. Fazl-e-Basit
Formal definitions of Algorithm
• An algorithm is a set of step-by-step instructions to accomplish a task or solve a
problem, often used in computer science.
• It is a well-defined procedure that takes inputs and produces outputs, following a
finite number of steps.
• Algorithms are used in various fields, including computer science, engineering,
finance, and artificial intelligence
• They can be simple or complex, depending on the desired outcome.
• An algorithm must meet certain characteristics, including being clear and
unambiguous, having well-defined inputs and outputs, being finite and feasible,
language-independent, and producing at least one output
• Algorithms can be expressed in various programming languages, but their logic
or plan for solving a problem should remain the same, regardless of the
implementation

Mr. Fazl-e-Basit
Algorithm
Step-by-step: The instructions are clear and well-defined, outlining each step
in the process.
Finite: The algorithm must terminate after a certain number of steps,
avoiding infinite loops.
Problem-solving: The instructions are designed to achieve a specific
outcome or solve a particular problem.
Think of an algorithm like a recipe.
• It provides a clear sequence of instructions (steps) to achieve a desired
outcome (cooking a delicious meal).
• Just like there are many recipes for different dishes, there can be many
algorithms to solve different problems.

Mr. Fazl-e-Basit
7
Example: Algorithms to sort numbers in ascending order
Input
Merge Sort
Insertion Sort
Algorithm A
Algorithm B
Unordered List
Output
ordered List
31,41,59,26,41,58 26,31,41,41,58,59
In above figure the whole set of input numbers are known as
instance of the sorting problem.

Mr. Fazl-e-Basit
8
Data Structure
• A Data Structure is a systematic way of organizing and accessing data with a
specific relationship between the elements, in order to facilitate access and
modifications.
• No single data structure works well for all purposes, so it is important to be
familiar with pros and cons of several Data Structures.

Mr. Fazl-e-Basit
9
Algorithmics
• It is the science that lets designers study and evaluate the effect of
algorithms based on various factors so that the best algorithm is selected
to meet a particular task in given circumstances.
• It is also the science that tells how to design a new algorithm for a
particular job.

Mr. Fazl-e-Basit
Example of algorithm
• Division ( N1/N2)
• Step 1: the user enters the first and second numbers – the dividend and the divisor
• Step 2: the algorithm written to perform the division takes in the number, then puts a
division sign between the dividend and the divisor. It also checks for a remainder.
• Step 3: the result of the division and remainder is shown to the user
• Step 4: the algorithm terminates
• Division ( N1/N2)
• N1 
• N2 
• R  N1 / N2 // ( R = N1 ÷ N2)
•  R

Mr. Fazl-e-Basit
Algorithm Design Strategies
• Divide and conquer
• Greedy algorithms
• Dynamic programming
• Backtracking
• Brute force
choosing the right algorithm design strategy
depends on the problem at hand

Mr. Fazl-e-Basit
Components and Characteristics of Algorithms
• Effectiveness: Algorithms should be most effective among many ways to solve a
problem.
• Language independent: An algorithm shouldn't include computer code. Instead,
the algorithm should be written in such a way that it can be used in different
programming languages.
• Efficiency: Algorithms should be designed to minimize time and resources.
• Correctness: Algorithms should produce the correct output for all possible inputs.
Input: Algorithm takes defined and precise input
Output: Algorithm returns (provides) an output(s)
Steps: Each step in the algorithm should be clear and unambiguous.
Components
Characteristics

Mr. Fazl-e-Basit
Language independence: Pseudocode
• A Pseudocode is defined as a step-by-step description of an algorithm.
Pseudocode does not use any programming language in its
representation instead it uses the simple English language text as it is
intended for human understanding rather than machine reading.
• Pseudocode is the intermediate state between an idea and its
implementation(code) in a high-level language.
• Pseudocode is a high-level description of an algorithm using a
combination of natural language and programming-like syntax.
• It helps in planning and designing algorithms before actual
implementation in a programming language.

Mr. Fazl-e-Basit
Algorithm Analysis
• Importance of algorithm analysis:
• Evaluates the efficiency and performance of algorithms.
• Helps in comparing different algorithms for the same problem.
• Target metrics for algorithm analysis:
• Time complexity: Measures the time taken by an algorithm to run as a
function of input size.
• Space complexity: Measures the memory space required by an algorithm as a
function of input size.

Mr. Fazl-e-Basit
Time and Space Complexity
• Time complexity:
• Big O notation: Represents the upper bound on the growth rate of
an algorithm in terms of its input size.
• Examples of common time complexities (O(1), O(log n), O(n),
O(n^2), O(2^n), etc.).
• Space Complexity
• Measures the amount of memory required by an algorithm as it
executes.
• Similar notation as time complexity (O(1), O(n), O(n^2), etc.).

Mr. Fazl-e-Basit
16
How do we Analyze?
• Every Algorithm has a parameter N or n that effects its
running time.
• For example, for sorting different numbers the parameter N is
the number of input numbers to be sorted.
• So for analyzing algorithms our starting point is to have n or N
N or n → shows size of the Input.

Mr. Fazl-e-Basit
Hard Problems
• Most of the contents of this course are about to
address/discuss algorithms and their efficiency. Our usual
measure of efficiency is speed.
• There are some problems, however, for which no efficient
solution is known.
• We will study few of these kind of problems later in the
course, which are known as NP-Complete problems.

Mr. Fazl-e-Basit
25 April 2024 Instructor:- Mr. Fazl-e-Basit 18
Parameters for Selection of an Algorithm
• Priority of Task
• Type of Available Computing Equipment
• Nature of Problem
• Speed of Execution
• Storage Requirement
• Programming Effort
A good choice can save both money and time,
and can successfully solve the problem.

Mr. Fazl-e-Basit
Growth of Functions
• We can sometimes determine the exact running time of the
algorithm, however`r, the extra precision is not usually worth
the effort of computing it.
• For large inputs, the multiplicative constants and lower order
terms of an exact running time are dominated by the effects of
the input size itself.

Mr. Fazl-e-Basit
Things to consider when Analyzing Algorithms
1. Ignore constants. For example,
f(n) = 25n²
or
f(n) = 25n² + 2000
f(n) = O (n²)
2. Ignore small terms
f(n) = 25n³+ 30n² + 10n
f(n) = O (n³)
3. Application Independent
i.e. not dependent on any tool, platform or application.

Mr. Fazl-e-Basit
Asymptotic Performance
• In this course, we care most about asymptotic performance.
i.e.
• How does the algorithm behave as the problem size gets very
large?
• Running time
• Memory/storage requirements
• Bandwidth/power requirements/logic gates/etc.

Mr. Fazl-e-Basit
• When we look at the input sizes large enough to make only the order
of growth of the running time relevant, we are studying the asymptotic
efficiency of the algorithm.
• That is we are concerned with how the running time of the algorithm
increases with the size of the input in the limit, as the size of the input
increases without any bound.
• We will study standard methods for simplifying the asymptotic analysis
of the algorithm. We will begin with by defining several types of
“asymptotic notations” like Theta ( Θ ) Notation, Big-Oh ( O ) Notation
and Omega ( Ω ) Notation.
Asymptotic Performance

Mr. Fazl-e-Basit
Theta Notation ( Θ )
• For a given function g(n), we denote by Θ( g(n) ) the set of functions
Θ( g(n) ) = { f(n) : there exist positive constants c1,c2 and
n0 such that
0 ≤ c1.g(n) ≤ f(n) ≤ c2.g(n) for all n ≥ n0 }1
1 colon in set means “such that”

Mr. Fazl-e-Basit
• A function f(n) belongs to the set Θ( g(n) ) if there exist positive
constants c1 and c2 such that it can be sandwiched between c1g(n)
and c2g(n) for sufficiently large n.
• Since Θ( g(n) ) is a set, we could write
f(n) Є Θ( g(n) ) to indicate that f(n) is a member of Θ( g(n) ). Instead
we will usually write
f(n) = Θ( g(n) ).
Є means “belongs to”

Mr. Fazl-e-Basit
25 April 2024
Instructor:- Mr. Fazl-e-Basit 26
C2.g(n)
f(n)
n0 n
C1.g(n)
f(n) = Θ( g(n) )
• In the above figure, for all values of n to the right of n0, the value of f(n)
lies at or above C1.g(n) and at or below C2.g(n).
• In other words, for all n ≥ n0, the function f(n) is equal to g(n) to within
constant factor.
• We say that g(n) is asymptotic tight bound for f(n).

Mr. Fazl-e-Basit
• Lets us justify this intuition by using the formal definition to show that
f(n) = n2/2-3n = Θ( n2 ).
• To do so we must find the constants c1,c2, and n0 such that
c1.n2 ≤ n2/2-3n ≤ c2.n2
for all n ≥ n0. dividing by n2
c1 ≤ 1/2-3/n ≤ c2
The right hand side inequality can be made to hold for any value of n ≥ 1 by
choosing c2 ≥ ½.
Likewise , the left hand side inequality can be made to hold for any value of
n ≥ 7 by choosing c1 ≤ 1/14. Thus, by choosing c1=1/14 , c2=1/2 and
n0 = 7, we can verify that n2/2-3n = Θ( n2 ).

Mr. Fazl-e-Basit
• In above example, certainly other choices for the constants exist, but the
important thing is that some choice exists.
• Since any constant is a degree-0 polynomial, we can express any constant
function as Θ( n0 ) or Θ( 1 ). We shall always use the notation Θ( 1 ) to
mean either constant or constant function with respective to some
variable.

Mr. Fazl-e-Basit
Big-Oh Notation (O )
• When we have only an asymptotic upper bound, we use O – notation. For a
given function g(n), we denote by O( g(n) ) the set of functions
O( g(n) ) = { f(n) : there exist positive constants c, and n0 such
that
0 ≤ f(n) ≤ c.g(n) for all n ≥ n0 }1

Mr. Fazl-e-Basit
c.g(n)
f(n)
n0 n
f(n) = O( g(n) )

Mr. Fazl-e-Basit
• We write f(n) = O( g(n) ) to indicate that f(n) is a member of the set O( g(n) ).
• Note that f(n) = Θ( g(n) ) implies f(n) = O( g(n) ), since Θ notation is stronger
notation than O notation. So for quadratic function an2+bn + c, where a>0, is
in Θ( n2 ) also shows that any quadratic function is in O( n2 ).

Mr. Fazl-e-Basit
Omega Notation ( Ω )
• Just as O-notation provides an upper bound on a function, Ω
notation provides an asymptotic lower bound.
• For a given function g(n), we denote by Ω( g(n) ) the set of
functions
Ω( g(n) ) = { f(n) : there exist positive constants
c, and n0 such that
0 ≤ c.g(n) ≤ f(n) for all n ≥ n0 }1

Mr. Fazl-e-Basit
c.g(n)
f(n)
n
n0
f(n) = Ω( g(n) )

Mr. Fazl-e-Basit
Example:
• Objective is to find the maximum of an unordered set having N
elements.
• Input : An unordered list.
• Output : Maximum of input set.
• Algorithm
1. int Max = 0 //assume S[N] is filled with +ve numbers
2. For (int I = 0;I<N;I++)
3. {
4. If (S[I] > Max)
5. Max = S[I]
6. }
7. cout<< Max

Mr. Fazl-e-Basit
Simple Analysis of previous Algorithm
Instruction No of times Executed cost
1 1 1
2 N N
3 - -
4 N N
5 N N
6 - -
7 1 1
So f(n) = 1 + N + N + N + 1 = 2 + 3N = 3N+2
so f(n) = O (N) or O (n)

Mr. Fazl-e-Basit
NOTE : we always look for worst case of each instruction to be
executed while finding Big-Oh (O). (Assume all instructions take
unit time in above example).
• The worst case running time of an algorithm is an upper bound on
the running time for an input.
• Knowing it, gives us guarantee that algorithm will never take any
longer.
• We need not make some educated guess about the running time
and hope that it never gets much worse.

Mr. Fazl-e-Basit
Types of Functions (Bounding Functions )
1. Constant Function: O ( 1 )
For example, addition of two numbers will take same for Worst case, Best case and
Average case.
2. Logarithmic Function: O ( log(n) )
3. Linear Function : O ( n )
4. O ( n.log(n) )
5. Quadratic Function : O (n²)
6. Cubic Function : O ( n³ )
7. Polynomial Function : O ( nk )
8. Exponential Function : O ( 2n )
etc…

Mr. Fazl-e-Basit
Some facts about summation
• If c is a constant
And

Mr. Fazl-e-Basit
Mathematical Series
➢ If 0 < x < 1 then this is Θ(1), and if x > 1, then this is Θ(xn).

Mr. Fazl-e-Basit
Harmonic series For n ≥ 0

Analysis: A Harder Example
➢ Let us consider a harder example.
➢ How do we analyze the running time of an algorithm that has complex nested
loop? The answer is we write out the loops as summations and then solve the
summations. To convert loops into summations, we work from inside-out.

Mr. Fazl-e-Basit
• Consider the inner most while loop.
• It is executed for k = j, j - 1, j - 2, . . . , 0. Time spent inside the while
loop is constant. Let I() be the time spent in the while loop. Thus

➢ Consider the middle for loop.
➢ Its running time is determined by i.
Let M() be the time spent in the for loop:

➢ Finally the outer-most for loop.
➢ Let T() be running time of the entire algorithm

Mr. Fazl-e-Basit
Some searching and sorting
Searching Algorithms
• Linear search
• Binary search
• Hashing
Sorting Algorithms
• Bubble sort
• Insertion sort
• Selection sort
• Merge sort
• Quick sort
• Heap sort

Mr. Fazl-e-Basit
Search
Complete Search
(Exhaustive
Search):
Linear
Direct Search
Linear/sequential
Search Binary Search
Non-linear
Partial Search
(Heuristic Search):
A Search*:
Greedy Best-First
Search
Hill Climbing:
Other Techniques
. . . . . .

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