Python algorithm

ALGORITHMIC EFFICIENCY
Prof. K. Adisesha
BE, M.Sc., M.Th., NET, (Ph.D.)

2
Learning objectives
• Introduction
• Algorithm Definition
• What is computational Complexity
• Estimating Complexity of Algorithm
• Asymptotic Notations
• Complexity of an Algorithm

3
Algorithm
What is an Algorithm?
• An algorithm is a step-by-step procedure for solving a
problem in a finite amount of time.
• The word algorithm comes from the name of a Persian
Mathematician Abu Ja’far Mohammed ibn-I Musa al
Khowarizmi.
• For a given problem:
 There can be more than one solution (more than one
algorithm) .
 An algorithm can be implemented using different
programming languages on different platforms.

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Algorithm
Designing of an Algorithm
• Design of an algorithm is an area of computer science which
minimizes the cost.
• Always design algorithms which minimize the cost.
Analysis of Algorithm
• Analysis of Algorithms is the area of computer science that
provides tools to analyze the efficiency of different methods
of solutions.
• In short predict the cost of an algorithm in terms of
resources and performance is called analysis of Algorithm.

5
Properties of Algorithm
Donald Ervin Knuth has given a list of five properties for an
algorithm, these properties are:
1. FINITENESS
2. DEFINITENESS
3. INPUT
4. OUTPUT
5. EFFECTIVENESS

6
FINITENESS
 An algorithm must always terminate after a finite number of steps.
 It means after every step one reach closer to solution of the problem
and after a finite number of steps algorithm reaches to an end point.
DEFINITENESS
 Each step of an algorithm must be precisely defined.
 It is done by well thought actions to be performed at each step of
the algorithm.
 Also the actions are defined unambiguously for each activity in the
algorithm.
INPUT
 Any operation you perform need some beginning value/ quantities
associated with different activities in the operation.
 So the value/quantities are given to the algorithm before it begins.

7
OUTPUT
 One always expects output/result (expected value/quantities) in
terms of output from an algorithm.
 The result may be obtained at different stages of the algorithm.
 Result is obtained from the intermediate stage of the operation then
it is known as intermediate result
 Result obtained at the end of algorithm is known as end result.
 The output is expected value/quantities always have a specified
relation to the inputs.
EFFECTIVENESS
 Algorithms to be developed/written using basic operations.
 Algorithms operations should be done exactly and in a finite amount
of time by a person, by using paper and pencil only.

8
Performance of an algorithm depends on
many factors:
• Internal Factors: Specify algorithm’s efficiency in terms of
 Time required to run
 Space (Memory)required to run
• External Factors: affect the algorithm’s performance
 Size of the input to the algorithm
 Speed of computer on which it is run
 Quality of the Computer
Efficiency of Algorithm

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There are two aspects of algorithmic performance or
efficiency:
• TIME COMPLEXITY: It is essentially efficiency, or how long a
program function takes to process a given input.
 Instructions take time.
 How fast does the algorithm perform?
 What affects its runtime?
• SPACE COMPLEXITY: of an algorithm is total space taken by the
algorithm with respect to the input size. Space complexity includes
both Auxiliary space and space used by input.
 Data structures take space
 What kind of data structures can be used?
 How does choice of data structure affect the runtime?
Internal Factors

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Asymptotic Analysis:
• In Asymptotic Analysis, we evaluate the performance of an
algorithm in terms of input size
• Asymptotic analysis of an algorithm refers to defining the
mathematical framing of its run-time performance.
• Usually, the time required by an algorithm falls under three
types −
 Best Case − Minimum time required for program execution.
 Average Case − Average time required for program execution.
 Worst Case − Maximum time required for program execution.
Asymptotic Analysis

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• Following are the commonly used asymptotic notations to
calculate the running time complexity of an algorithm.
Asymptotic Analysis

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• Following graph is commonly used to calculate the running
time complexity of an algorithm.
Asymptotic Analysis

13
• Big O specifically describes the worst-case scenario, and can
be used to describe the execution time required or the space
used (e.g. in memory or on disk) by an algorithm.
• Big O complexity can be visualized with this graph:
Ο Notation ( Big Oh Notation)

14
• Big Ω describes the set of all algorithms that run no better
than a certain speed (it’s a lower bound)
• It measures the best case time complexity or the best amount
of time an algorithm can possibly take to complete.
• Best case performance of an algorithm 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}.
• Best case performance of an algorithm is generally not useful,
the Omega notation is the least used notation among all
three.
Omega Notation - Ω

15
θ notation
• You can use the big-Theta notation to describe the average-case
complexity.
• The θ notation describes asymptotic tight bounds
• If an algorithm has the average-case time complexity of,
say, 3*n^2 - 5n + 13 ,
then it is true that its average-case time complexity is
Theta(n^2) , O(n^2) , and O(n^3)
Theta Notation - θ

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TIME COMPLEXITY OF SORTING ALGORITHMS
TIME COMPLEXITY OF SEARCHING ALGORITHMS
TIME COMPLEXITY

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SPACE COMPLEXITY
SPACE COMPLEXITY OF SEARCHING AND SORTING ALGORITHMS

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• We learned about the Algorithm Definition
• What is computational Complexity
• Estimating Complexity of Algorithm
• Asymptotic Notations
• Complexity of an Algorithm
Thank you
Conclusion!

Python algorithm

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