Prepare for your exams
Get points
Guidelines and tips
Sell on Docsity
Docsity AI

Prepare for your exams

Study with the several resources on Docsity

Earn points to download

Earn points by helping other students or get them with a premium plan

Guidelines and tips

Sell on Docsity

Docsity AI

Log in Sign up

Prepare for your exams

Study with the several resources on Docsity

Find documents

Prepare for your exams with the study notes shared by other students like you on Docsity

Search for your university

Find the specific documents for your university's exams

Docsity AINEW

Summarize your documents, ask them questions, convert them into quizzes and concept maps

Explore questions

Clear up your doubts by reading the answers to questions asked by your fellow students

Earn points to download

Earn points by helping other students or get them with a premium plan

Share documents

20 Points

For each uploaded document

Answer questions

5 Points

For each given answer (max 1 per day)

All the ways to get free points

Get points immediately

Choose a premium plan with all the points you need

Study Opportunities

Choose your next study program

Get in touch with the best universities in the world. Search through thousands of universities and official partners

Community

Ask the community

Ask the community for help and clear up your study doubts

Free resources

Our save-the-student-ebooks!

Download our free guides on studying techniques, anxiety management strategies, and thesis advice from Docsity tutors

Algorithms-Analysis of Algorithms-Lecture Slides, Slides of Design and Analysis of Algorithms

Ankit Institute of Technology and Science Design and Analysis of Algorithms

This lecture was delivered by Anshuman Vibha at Ankit Institute of Technology and Science for Analysis of Algorithms course. It includes: Algorithms, Computational, Procedure, Data, Structure, Modifications, Time, Complexity, Running, Examples

Typology: Slides

2011/2012

Uploaded on 07/14/2012

sharma-raju 🇮🇳

4.5

(2)

15 documents

1 / 22

This page cannot be seen from the preview

Don't miss anything!

Algorithms

•An algorithm is a well-defined

computational procedure that takes some

value, or set of values, as input and

produces some value or set of values as

output.

•A data structure is a way to store and

organize data in order to facilitate access

and modifications.

docsity.com

Discover Slides of Design and Analysis of Algorithms Ankit Institute of Technology and Science

Partial preview of the text

Download Algorithms-Analysis of Algorithms-Lecture Slides and more Slides Design and Analysis of Algorithms in PDF only on Docsity!

Algorithms

• An algorithm is a well-defined

computational procedure that takes some

value, or set of values, as input and

produces some value or set of values as

output.

• A data structure is a way to store and

organize data in order to facilitate access

and modifications.

Time Complexity

• Amount of time taken by an algorithm to

run as a function of size

Worst case
Average case
Best case

Running Time

Name

Running time ( T ( n ))

Examples of running times

linear time O ( n ) n

linearithmic time O ( n log n ) n log n , log n!

quadratic time O ( n^2 ) n^2

polynomial time poly( n ) n , n log n , n^10

exponential time 2 poly( n )^ n !, nn , 2 n^2

factorial time O ( n !) n!

Assignment 1 (Cormen Problem 1.1)

Determine the largest size n of a problem that

can be solved in the given time t if the

problem takes f(n) microseconds

1 sec 1 min 1 hr 1 day I year 1 century n

log n n log n n^2 2 n n!

Algorithms-Analysis of Algorithms-Lecture Slides, Slides of Design and Analysis of Algorithms

Related documents

Partial preview of the text

Download Algorithms-Analysis of Algorithms-Lecture Slides and more Slides Design and Analysis of Algorithms in PDF only on Docsity!

Algorithms

• An algorithm is a well-defined

computational procedure that takes some

value, or set of values, as input and

produces some value or set of values as

output.

• A data structure is a way to store and

organize data in order to facilitate access

and modifications.

Time Complexity

• Amount of time taken by an algorithm to

run as a function of size

Running Time

Assignment 1 (Cormen Problem 1.1)

Notations

• Big O ; For the asymptotic upper bound we

use O-notation

O(g(n))={f(n): there exists positive

constants c and n 0 such that

0 ≤ f(n) ≤ c g(n) for all n ≥ n 0

Notations

• Big Omega Notation (Ω) asymptotically

bounds a function from below.

Ω(g(n)) ={f(n) : there exists a positive

constant c and n 0 such that

0 ≤ c g(n) ≤ f(n) for all n ≥ n 0

Notations

• Little o is similar to big but may or may not

be asymptotic tight upper bound.

o(g(n))={f(n): for any positive constants c>

there exists a constant n 0 >0 such that

0 ≤ f(n) < c g(n) for all n ≥ n 0

Notations

• Little ω is similar to big Ω but may or may

not be asymptotic tight lower bound.

ω(g(n))={f(n): for any positive constants c>

there exists a constant n 0 >0 such that

0 ≤ c g(n) < f(n) for all n ≥ n 0

We may define it as

f(n) Є ω(g(n)) if and only if g(n) Є o(f(n))