Saturday, 19 September 2020

PICT Random Experiment Number Generator

 Your Experiment number is: 



Wednesday, 13 May 2020

Particle Swarm Optimization (PSO) : Introduction and Velocity Equation explained


This video explains the Concept of Particle Swarm Optimization. It is part 1 of PSO series and introduces PSO along with the discussion on velocity updation equation.

The particle swarm optimization (PSO) algorithm is a population-based search algorithm based on the simulation of the social behavior of birds within a flock. The initial intent of the particle swarm concept was to graphically simulate the graceful and unpredictable choreography of a bird flock , with the aim of discovering patterns that govern the ability of birds to fly synchronously, and to suddenly change direction with a regrouping in an optimal formation. From this initial objective, the concept evolved into a simple and efficient optimization algorithm.





Friday, 7 December 2018

Design and Analysis of Algorithm (DAA), TE : CE, Savitribai Phule Pune University (SPPU)



Class :-T.E.  [Computer Engg.]         
Subject Title: Design and Analysis of Algorithms 
Subject Code: 310250
Examination Scheme:
In Semester: 30 Marks
End Semester: 70 Marks
Lectures: 4 Hrs/Week       
   
Introduction:

What are algorithms? Why is the study of algorithms worthwhile? What is the role of algorithms relative to other technologies used in computers? In this course we will answer these questions. You will learn about algorithms that operate on common data structures, for instance sorting and searching; advanced design and analysis techniques such as dynamic programming and greedy algorithms; advanced graph algorithms such as minimum spanning trees and shortest paths; NP-completeness theory; and approximation algorithms. After completing this course you will be able to design efficient and correct algorithms using sophisticated data structures for complex computational tasks.

Course Outcomes:
On completion of the course, student will be able to–

            1.      Formulate the problem.
2.      Design and apply algorithm for given problem.
3.      Analyze the performance of algorithm by asymptotic notation.
            4.      Find optimal solution by applying various algorithmic strategies.

Prerequisites:
Discrete Mathematics (210241),
Data Structures (210243, 210252),
Theory of Computation (310241)

Overview:
1.      Fundamentals

 In this unit, we will learn the significance of algorithms in computer field, various aspects of algorithm development, qualities of good solution, significance of program correctness and efficiency.  Two important aspects of algorithm development viz, correctness and efficiency; are introduced.

      2.      Models and Design

Development of algorithms by successive refinement for both iterative and recursive designs are provided. It also introduces the concepts of specification and prototyping.

      3.      Abstract Algorithms

In this unit meta-algorithms / algorithmic strategies are discussed in detail. We will learn strategies like, ‘divide and conquer’, ‘greedy’, ‘dynamic programming’. Introduction to Genetic algorithms will also be covered.
  
       4.      Complexity Theory

Having gone into laborious detail for evaluation the running time, now we will learn the notations that allow us to characterize the main factors affecting an algorithm’s running time. Concepts like timing analysis, complexity, asymptotic growth rates. Order notation and manipulation rules for the algorithms will be discussed. It will also introduce us to various complexity classes.
       5.      Amortized Analysis

The concept of amortized analysis, which charted a new approach to algorithm efficiency calculation is discussed in this unit. Rather than focusing on each operation separately, it considers the interactions between all the operations by studying the running time of a series of these operations.

       6.      Multithreaded and Distributed Algorithms

In this unit, we shall extend our algorithmic model to encompass parallel algorithms, which can run on a multiprocessor computer that permits multiple instructions to execute concurrently. It introduces the basics of the model, showing hot to quantify parallelism in terms of the measures of work and span. It also explores several interesting Distributed algorithms.

Detail teaching plan is here:

Textbooks

The main textbooks we use are:

1.      Design And Analysis of Algorithms  by Parag Himanshu Dave, Himanshu Bhalchandra Dave

2.      Introduction to Algorithms  by Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest and Clifford Stein

We will also occasionally use:

3.      Fundamentalsof Algorithmics by Gilles Brassard, Paul Bratley
4.      Algorithm Design: Foundations, Analysis and Internet Examples  by Michael T. Goodrich, Roberto Tamassia
5.      Fundamentals ofComputer Algorithms by Horowitz and Sahani
6.      Randomized Algorithms by Rajeev Motwani and Prabhakar Raghavan,
7.      Algorithms on Strings,Trees and Sequences  by Dan Gusfield

Thursday, 6 December 2018

Teaching Plan For Design and Analysis of Algorithm (DAA), TE : CE, Savitribai Phule Pune University (SPPU)

Unit
Topic and sub topics covered
Lecture
I
Fundamentals :
The Role of Algorithms in Computing –
What are algorithms
2
Algorithms as technology
1
Evolution of Algorithms
1
Design of Algorithm
1
Need of Correctness of Algorithm
1
Confirming correctness of Algorithm – sample examples
1
Iterative algorithm design issues
2
II
Models and Design : Functional Model – Features, Recursive processes, Recursive processes,  Recursive processes
1
Scope rules, Tail recursion
1
Checking correctness of Iterative process
1
Imperative Model – Basics
1
Specifications and Prototyping
1
Stepwise Refinement
1
Proof Rules – Basics, For loops, Goto and Exit loops
1
Functions and Procedures
1
Problem Solving using Greedy strategy - Knapsack problem, Huffman code generation algorithm
1
III
Abstract Algorithms: Dynamic Programming
1
Divide and Conquer
1
Greedy strategy
1
Branch-n-Bound
1
Natural Algorithms –Evolutionary Algorithms and Evolutionary Computing
2
Introduction to Genetic Algorithm
1
Simulated Annealing
1
Artificial Neural Network and Tabu Search
1
IV
Complexity Theory: Complexity theory – Counting Dominant operators

Growth rate, upper bounds, asymptotic growth, O, Ω, Ɵ, o and ω notations
2
polynomial and non-polynomial problems
1
deterministic and non-deterministic algorithms
1
P-class problems, NP-class of problems
2
Polynomial problem reduction NP complete problems- vertex cover
1
Polynomial problem reduction NP complete problems-   3-SAT
1
NP hard problem - Hamiltonian cycle.
1
V
Amortized Analysis: Amortized Analysis – Binary, Binomial and Fibonacci heap
2
Dijkstra‘s Shortest path algorithm, Splay Trees, Time-Space tradeoff
2
Introduction to Tractable and Non-tractable Problems
1
Introduction to Randomized and Approximate algorithms
1
Embedded Algorithms: Embedded system scheduling (power optimized scheduling algorithm)
2
sorting algorithm for embedded systems

1
VI
Multithreaded and Distributed Algorithms: Multithreaded Algorithms - Introduction, Performance measures
1
Analyzing multithreaded algorithms
1
Parallel loops, Race conditions
1
Problem Solving using Multithreaded Algorithms - Multithreaded matrix multiplication,
1
Multithreaded merge sort
1
Distributed Algorithms - Introduction, Distributed breadth first search
1
Distributed Minimum Spanning Tree
1
String Matching- Introduction, The Naive string matching algorithm
1
The Rabin-Karp algorithm
1

Text books: (T)

T1. Parag Himanshu Dave, Himanshu Bhalchandra Dave, ―Design And Analysis of Algorithms‖, Pearson Education, ISBN 81-7758-595-9
T2. Gilles Brassard, Paul Bratley, ―Fundamentalsof Algorithmics‖, PHI, ISBN 978-81-203-1131-2

References Books: (R)

R1. Michael T. Goodrich, Roberto Tamassia , ―Algorithm Design: Foundations, Analysis and Internet Examples‖, Wiley, ISBN 978-81-265-0986-7
R2. Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest and Clifford Stein, ―Introduction to Algorithms‖, MIT Press; ISBN 978-0-262-03384-8
R3. Horowitz and Sahani, "Fundamentals ofComputer Algorithms", University Press, ISBN: 978 81 7371 6126, 81 7371 61262
R4. Rajeev Motwani and Prabhakar Raghavan, ―Randomized Algorithms‖, Cambridge University Press, ISBN: 978-0-521-61390-3
R5. Dan Gusfield, ―Algorithms on Strings,Trees and Sequences‖, Cambridge University Press,ISBN:0-521-67035-7






Thursday, 16 August 2018

Assignment 1 & 2

Since Assignment -I and II  is not properly visible on mobile devices, I am sharing these snapshots.



Friday, 10 August 2018

Rules of Inference


An argument (in propositional logic) is a sequence of propositions. All but the final proposition are called premises. The last proposition is the conclusion. The argument is valid iff the truth of all premises implies the conclusion is true.

In this discussion, we are using a notation where, hypotheses are written in a column, followed by a horizontal bar, followed by a line that begins with the therefore symbol with the conclusion.


Addition

P
----------------
PQ


Disjunctive Syllogism

PQ
¬P
----------------

Q


Conjunction

P
Q
----------------
PQ

Hypothetical Syllogism

P→Q
Q→R
----------------
P→R

Simplification

PQ
----------------
P

Constructive Dilemma

(P→Q)(R→S)
PR
----------------
QS

Modus Ponens

P→Q
P
----------------
Q

Destructive Dilemma

(P→Q)(R→S)
¬Q¬S
----------------
¬P¬R

Modus Tollens

P→Q
¬Q
----------------
¬P

Resolution
PQ
¬PR
----------------
QR


How to build arguments using the rules of inference

1. I t is not sunny this afternoon and it is colder than yesterday.
2. If we go swimming it is sunny.
3. If we do not go swimming then we will take a canoe trip.
4. If we take a canoe trip then we will be home by sunset.
5. We will be home by sunset