Lovely Professional University, Punjab

Course Code Course Title Course Planner Lectures Tutorials Practicals Credits

CSE408 DESIGN AND ANALYSIS OF ALGORITHMS 15691::Surmeet Kaur 3.0 0.0 0.0 3.0

Course Category Courses with Placement focus

TextBooks

Sr No Title Author Edition Year Publisher Name

T-1 Introduction to Algorithms C.E. Leiserson, R.L. Rivest and C. Stein

3rd 2007 Thomas Telford Publishing

Reference Books

Sr No Title Author Edition Year Publisher Name

R-1 The Design and Analysis Of Computer Algorithms

A.V.Aho, J.E. Hopcroft and J.D.Ullman

2nd 2007 Pearson Education

R-2 Introduction to the Design and Analysis of Algorithm

Anany Levitin 2nd 2003 Pearson Education

R-3 Computer Algorithms - Introduction to Design and Analysis

Sara Baase and Allen Van Gelder

2nd 2006 Pearson Education

R-4 Fundamentals of Computer Algorithms

Horowitz, S. Sahni 2nd 2005 Galgotia Publishers

Other Reading

Sr No Journals articles as Compulsary reading (specific articles, complete reference)

OR-1 http://www.personal.kent.edu/~rmuhamma/Algorithms/MyAlgorithms/Complexity/npComplete.htm (NP Completeness) ,

OR-2 http://delab.csd.auth.gr/~manolopo/Design/ch03.ppt (Brute Force String Matching) ,

OR-3 http://www.personal.kent.edu/~rmuhamma/Algorithms/algorithm.html (Concepts of Design of Algorithms) ,

OR-4 http://www.csc.villanova.edu/~map/8301/lec03.pdf ,

OR-5 http://www.personal.kent.edu/~rmuhamma/Algorithms/MyAlgorithms/Sorting/quickSort.htmdevices ,

Relevant Websites

Sr No (Web address) (only if relevant to the course) Salient Features

RW-1 http://courses.ncsu.edu/ma103/common/media/05/MA103Lct25.mp4 Prims and Kruskals algorithms

RW-2 http://optlab-server.sce.carleton.ca/POAnimations2007/DijkstrasAlgo.html Dijkstra's shortest path

RW-3 http://www.cse.yorku.ca/~aaw/Zambito/TSP_L/Web/TSPStart.html Travelling Salesman Problem

LTP week distribution: (LTP Weeks)

Week Number

Lecture Number

Broad Topic(Sub Topic) Chapters/Sections of Text/reference books

Other Readings,Relevant Websites, Audio Visual Aids, software and Virtual Labs

Lecture Description Learning Outcomes Pedagogical ToolDemonstration/ Case Study / Images / animation / ppt etc. Planned

Week 1 Lecture 1 Introduction to Basic Concepts of Algorithms(Notion of Algorithm , Fundamentals of Algorithmic Solving , Important Problem types , Fundamentals of the Analysis Framework , Asymptotic Notations and Basic Efficiency Classes.)

T-1:Chapter1(1.1and 1.2)

R-1:Chapter1(1.2)R-3:Chapter3(3.1)

Basic knowledge about algorithms and concepts of complexities of algorithms

Would be knowing about fundamentals of algorithms

Slides

Lecture 2 Introduction to Basic Concepts of Algorithms(Notion of Algorithm , Fundamentals of Algorithmic Solving , Important Problem types , Fundamentals of the Analysis Framework , Asymptotic Notations and Basic Efficiency Classes.)

T-1:Chapter1(1.1and 1.2)

R-1:Chapter1(1.2)R-3:Chapter3(3.1)

Basic knowledge about algorithms and concepts of complexities of algorithms

Would be knowing about fundamentals of algorithms

Slides

Lecture 3 Introduction to Basic Concepts of Algorithms(Notion of Algorithm , Fundamentals of Algorithmic Solving , Important Problem types , Fundamentals of the Analysis Framework , Asymptotic Notations and Basic Efficiency Classes.)

T-1:Chapter1(1.1and 1.2)

R-1:Chapter1(1.2)R-3:Chapter3(3.1)

Basic knowledge about algorithms and concepts of complexities of algorithms

Would be knowing about fundamentals of algorithms

Slides

Week 2 Lecture 4 Introduction to Basic Concepts of Algorithms(Notion of Algorithm , Fundamentals of Algorithmic Solving , Important Problem types , Fundamentals of the Analysis Framework , Asymptotic Notations and Basic Efficiency Classes.)

T-1:Chapter1(1.1and 1.2)

R-1:Chapter1(1.2)R-3:Chapter3(3.1)

Basic knowledge about algorithms and concepts of complexities of algorithms

Would be knowing about fundamentals of algorithms

Slides

Lecture 5 Mathematical Analysis of Non-recursive and Recursive Algorithm(Fibonacci Numbers, Solving recurrences using master method, substitution and iteration method.)

T-1:Chapter4(4.1 and 4.3 and 4.5)

R-1:Chapter2(2.5 and 2.6)

OR-4 Some examples of recursive and non recursive algorithms

Would be learning about iteration and substitution methods

Slides

Detailed Plan For Lectures

Weeks before MTE 7

Weeks After MTE 6

Spill Over 2

Week 2 Lecture 6 Mathematical Analysis of Non-recursive and Recursive Algorithm(Fibonacci Numbers, Solving recurrences using master method, substitution and iteration method.)

T-1:Chapter4(4.1 and 4.3 and 4.5)

R-1:Chapter2(2.5 and 2.6)

OR-4 Some examples of recursive and non recursive algorithms

Would be learning about iteration and substitution methods

Slides

Week 3 Lecture 7 Mathematical Analysis of Non-recursive and Recursive Algorithm(Fibonacci Numbers, Solving recurrences using master method, substitution and iteration method.)

T-1:Chapter4(4.1 and 4.3 and 4.5)

R-1:Chapter2(2.5 and 2.6)

OR-4 Some examples of recursive and non recursive algorithms

Would be learning about iteration and substitution methods

Slides

Lecture 8 Mathematical Analysis of Non-recursive and Recursive Algorithm(Fibonacci Numbers, Solving recurrences using master method, substitution and iteration method.)

T-1:Chapter4(4.1 and 4.3 and 4.5)

R-1:Chapter2(2.5 and 2.6)

OR-4 Some examples of recursive and non recursive algorithms

Would be learning about iteration and substitution methods

Slides

Lecture 9 Mathematical Analysis of Non-recursive and Recursive Algorithm(Fibonacci Numbers, Solving recurrences using master method, substitution and iteration method.)

T-1:Chapter4(4.1 and 4.3 and 4.5)

R-1:Chapter2(2.5 and 2.6)

OR-4 Some examples of recursive and non recursive algorithms

Would be learning about iteration and substitution methods

Slides

Week 4 Lecture 10 Test 1

Lecture 11 Sorting and order statics(Heap sort, Quick sort and sorting in linear time.)

T-1:Chapter 6(6.3 and 6.4) Chapter

7(7.1 and 7.2 and 7.3 and 7.4) Chapter

8(8.1 and 8.2 and 8.3 and 8.4)

OR-5 Different sorting techniques

Would be learning the method to sort the given list by using different methods

Slides

Lecture 12 Sorting and order statics(Heap sort, Quick sort and sorting in linear time.)

T-1:Chapter 6(6.3 and 6.4) Chapter

7(7.1 and 7.2 and 7.3 and 7.4) Chapter

8(8.1 and 8.2 and 8.3 and 8.4)

OR-5 Different sorting techniques

Would be learning the method to sort the given list by using different methods

Slides

Week 5 Lecture 13 Sorting and order statics(Heap sort, Quick sort and sorting in linear time.)

T-1:Chapter 6(6.3 and 6.4) Chapter

7(7.1 and 7.2 and 7.3 and 7.4) Chapter

8(8.1 and 8.2 and 8.3 and 8.4)

OR-5 Different sorting techniques

Would be learning the method to sort the given list by using different methods

Slides

Lecture 14 Sorting and order statics(Heap sort, Quick sort and sorting in linear time.)

T-1:Chapter 6(6.3 and 6.4) Chapter

7(7.1 and 7.2 and 7.3 and 7.4) Chapter

8(8.1 and 8.2 and 8.3 and 8.4)

OR-5 Different sorting techniques

Would be learning the method to sort the given list by using different methods

Slides

Week 5 Lecture 15 Data Structures(Elementary data structures, Hash tables, BST, Red Black trees.)

T-1:Chapter10(10.1 and 10.2 and 10.3 and 10.4) Chapter

11(11.1 and 11.2 and 11.3) Chapter

12(12.1 and 12.2)

Different operations on Binary Search trees and basics of stacks and linked lists and hashing

Would be knowing the fundamentals of red blacks trees and BST and Hashing concept

Slides

Week 6 Lecture 16 Data Structures(Elementary data structures, Hash tables, BST, Red Black trees.)

T-1:Chapter10(10.1 and 10.2 and 10.3 and 10.4) Chapter

11(11.1 and 11.2 and 11.3) Chapter

12(12.1 and 12.2)

Different operations on Binary Search trees and basics of stacks and linked lists and hashing

Would be knowing the fundamentals of red blacks trees and BST and Hashing concept

Slides

Lecture 17 Data Structures(Elementary data structures, Hash tables, BST, Red Black trees.)

T-1:Chapter10(10.1 and 10.2 and 10.3 and 10.4) Chapter

11(11.1 and 11.2 and 11.3) Chapter

12(12.1 and 12.2)

Different operations on Binary Search trees and basics of stacks and linked lists and hashing

Would be knowing the fundamentals of red blacks trees and BST and Hashing concept

Slides

Lecture 18 Test 2

Week 7 Lecture 19 Advanced Data Structures(Binomial Heap ,Fibonacci heap.)

T-1:Chapter19(19.1 and 19.2) Chapter

20(20.1 and 20.2 and 20.3) Chapter

23(23.1)

Structure of Fibonacci heaps

Would be knowing about Advanced Data Structures

Slides

Lecture 20 Advanced Data Structures(Binomial Heap ,Fibonacci heap.)

T-1:Chapter19(19.1 and 19.2) Chapter

20(20.1 and 20.2 and 20.3) Chapter

23(23.1)

Structure of Fibonacci heaps

Would be knowing about Advanced Data Structures

Slides

Lecture 21 Advanced Data Structures(Binomial Heap ,Fibonacci heap.)

T-1:Chapter19(19.1 and 19.2) Chapter

20(20.1 and 20.2 and 20.3) Chapter

23(23.1)

Structure of Fibonacci heaps

Would be knowing about Advanced Data Structures

Slides

MID-TERMWeek 8 Lecture 22 Advanced design and analysis

techniques(Dynamic Programming.)

T-1:Chapter 15(15.1 and 15.2 and 15.3) Chapter 16(16.1)

Assembly Line scheduling,Matrix Chain Multiplication

Would be knowing the basics of some Dynamic Algorithms

Slides

Lecture 23 Advanced design and analysis techniques(Dynamic Programming.)

T-1:Chapter 15(15.1 and 15.2 and 15.3) Chapter 16(16.1)

Assembly Line scheduling,Matrix Chain Multiplication

Would be knowing the basics of some Dynamic Algorithms

Slides

Week 8 Lecture 24 Advanced design and analysis techniques(Dynamic Programming.)

T-1:Chapter 15(15.1 and 15.2 and 15.3) Chapter 16(16.1)

Assembly Line scheduling,Matrix Chain Multiplication

Would be knowing the basics of some Dynamic Algorithms

Slides

Week 9 Lecture 25 Advanced design and analysis techniques(2)(Greedy techniques, Brute force techniques, amortized analysis.)

T-1:Chapter 17(17.1 and 17.2 and 17.3 and 17.4) Chapter

16(16.2 and 16.3 and 16.4)

Elements of Greedy Strategy,A task scheduling problem

Would be knowing about potential method and aggregate analysis

Slides

Lecture 26 Advanced design and analysis techniques(2)(Greedy techniques, Brute force techniques, amortized analysis.)

T-1:Chapter 17(17.1 and 17.2 and 17.3 and 17.4) Chapter

16(16.2 and 16.3 and 16.4)

Elements of Greedy Strategy,A task scheduling problem

Would be knowing about potential method and aggregate analysis

Slides

Lecture 27 Advanced design and analysis techniques(2)(Greedy techniques, Brute force techniques, amortized analysis.)

T-1:Chapter 17(17.1 and 17.2 and 17.3 and 17.4) Chapter

16(16.2 and 16.3 and 16.4)

Elements of Greedy Strategy,A task scheduling problem

Would be knowing about potential method and aggregate analysis

Slides

Week 10 Lecture 28 Advanced design and analysis techniques(2)(Greedy techniques, Brute force techniques, amortized analysis.)

T-1:Chapter 17(17.1 and 17.2 and 17.3 and 17.4) Chapter

16(16.2 and 16.3 and 16.4)

Elements of Greedy Strategy,A task scheduling problem

Would be knowing about potential method and aggregate analysis

Slides

Lecture 29 Advanced design and analysis techniques(2)(Greedy techniques, Brute force techniques, amortized analysis.)

T-1:Chapter 17(17.1 and 17.2 and 17.3 and 17.4) Chapter

16(16.2 and 16.3 and 16.4)

Elements of Greedy Strategy,A task scheduling problem

Would be knowing about potential method and aggregate analysis

Slides

Lecture 30 Graph Algorithm(Minimum Spanning trees,Single source and all source shortest path algorithm,Maximum flow)

T-1:Chapter 23(23.2) Chapter 24(24.1 and

24.2 and 24.3) Chapter 25(25.1 and

25.2) Chapter 26(26.1 and 26.2)

RW-1RW-2

Minimum Spanning Tree and Shortest path Algorithms

Would be knowing about graphs

Slides

Week 11 Lecture 31 Graph Algorithm(Minimum Spanning trees,Single source and all source shortest path algorithm,Maximum flow)

T-1:Chapter 23(23.2) Chapter 24(24.1 and

24.2 and 24.3) Chapter 25(25.1 and

25.2) Chapter 26(26.1 and 26.2)

RW-1RW-2

Minimum Spanning Tree and Shortest path Algorithms

Would be knowing about graphs

Slides

Week 11 Lecture 32 Graph Algorithm(Minimum Spanning trees,Single source and all source shortest path algorithm,Maximum flow)

T-1:Chapter 23(23.2) Chapter 24(24.1 and

24.2 and 24.3) Chapter 25(25.1 and

25.2) Chapter 26(26.1 and 26.2)

RW-1RW-2

Minimum Spanning Tree and Shortest path Algorithms

Would be knowing about graphs

Slides

Lecture 33 Quiz 1

Week 12 Lecture 34 String Matching Techniques(Brute force, Robin Karp, Bellman Ford,KMP)

T-1:Chapter 32(32.2 and 32.4)

OR-2 Some string matching techniques

Would be knowing the methods to match two given strings

Slides

Lecture 35 String Matching Techniques(Brute force, Robin Karp, Bellman Ford,KMP)

T-1:Chapter 32(32.2 and 32.4)

OR-2 Some string matching techniques

Would be knowing the methods to match two given strings

Slides

Lecture 36 String Matching Techniques(Brute force, Robin Karp, Bellman Ford,KMP)

T-1:Chapter 32(32.2 and 32.4)

OR-2 Some string matching techniques

Would be knowing the methods to match two given strings

Slides

Week 13 Lecture 37 Problem classes P(NP, NP-hard and NP-complete, deterministic and non deterministic polynomial time algorithms)

T-1:Chapter 34(34.5)R-1:Chapter10(10.1

and 10.2)

OR-1 Distinction between Deterministic and Non Deterministic Algorithms

Would be leaning about NP complete problems

Slides

Lecture 38 Problem classes P(NP, NP-hard and NP-complete, deterministic and non deterministic polynomial time algorithms)

T-1:Chapter 34(34.5)R-1:Chapter10(10.1

and 10.2)

OR-1 Distinction between Deterministic and Non Deterministic Algorithms

Would be leaning about NP complete problems

Slides

Lecture 39 Problem classes P(NP, NP-hard and NP-complete, deterministic and non deterministic polynomial time algorithms)

T-1:Chapter 34(34.5)R-1:Chapter10(10.1

and 10.2)

OR-1 Distinction between Deterministic and Non Deterministic Algorithms

Would be leaning about NP complete problems

Slides

SPILL OVERWeek 14 Lecture 40 T-1:Chapter 35 Approximation

algorithms for some NPcomplete

Some more NP complete problems

Slides

Lecture 41 T-1:Chapter 35 Approximation algorithms for some NPcomplete

Some more NP complete problems

Slides

Scheme for CA:Component Frequency Out Of Each Marks Total Marks

Test 1 2 10 10

Quiz 1 10 10

Total :- 20 20

Details of Academic Task(s)

AT No. Objective Topic of the Academic Task Nature of Academic Task(group/individuals/field

work

Evaluation Mode Allottment / submission Week

Quiz 1 To pursue the students practicing the objective type questions related to placement activities

WEEK 1 TO WEEK 11 Individual MCQ with 1/4 of negative marking

10 / 11

Test 2 Advanced Data Structure

WEEK 1 TO WEEK 6 Individual Step by step evaluation of selected questions

5 / 6

Test 1 Fundamentals of algorithm

WEEK1 TO WEEK4 Individual Step by Step evaluation of selected questions

3 / 4