This document contains a past exam paper for the subject "Design and Analysis of Algorithms". It has 2 parts with a total of 15 questions. Part A covers basic algorithm concepts like recurrence relations, efficiency classes, minimum spanning trees, and more. Part B involves solving algorithm problems using techniques like dynamic programming, Huffman coding, shortest paths, and more. It also tests concepts like P vs NP, approximation algorithms, and analysis of algorithm efficiency.
Cs6402 design and analysis of algorithms may june 2016 answer keyappasami
The document discusses algorithms and complexity analysis. It provides Euclid's algorithm for computing greatest common divisor, compares the orders of growth of n(n-1)/2 and n^2, and describes the general strategy of divide and conquer methods. It also defines problems like the closest pair problem, single source shortest path problem, and assignment problem. Finally, it discusses topics like state space trees, the extreme point theorem, and lower bounds.
QUESTION BANK FOR ANNA UNNIVERISTY SYLLABUSJAMBIKA
first of all i am very happy that the only university that keeps its blog updated. the habit of using algorithm analysis to justify design decisions when you write implement new algorithms and to compare the experimental performance .
This document provides an overview of brute force and divide-and-conquer algorithms. It discusses various brute force algorithms like computing an, string matching, closest pair problem, convex hull problems, and exhaustive search algorithms like the traveling salesman problem and knapsack problem. It also analyzes the time efficiency of these brute force algorithms. The document then discusses the divide-and-conquer approach and provides examples like merge sort, quicksort, and matrix multiplication. It provides pseudocode and analysis for mergesort. In summary, the document covers brute force and divide-and-conquer techniques for solving algorithmic problems.
This document contains exercises, hints, and solutions for Chapter 1 of the book "Introduction to the Design and Analysis of Algorithms." It includes 11 exercises related to algorithms for computing greatest common divisors, square roots, binary representations, and other topics. The document also provides hints for each exercise to help students solve them and includes the solutions.
Random numo Galois Field
Polynomial Arithmetic
Example of Polynomial Arithmetic
bers, its types and usage.
TRNG, PRNG, CHPRNG
Review of BBS
Stream Ciphering
RC4 algorithm
Basic Number Theory
Extended Euclidean Algorithm
Relevance of Extended Euclidean Algorithm
The topic of assignment is a critical problem in mathematics and is further explored in the real
physical world. We try to implement a replacement method during this paper to solve assignment problems with
algorithm and solution steps. By using new method and computing by existing two methods, we analyse a
numerical example, also we compare the optimal solutions between this new method and two current methods. A
standardized technique, simple to use to solve assignment problems, may be the proposed method
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
The document summarizes a paper that presents techniques for using dynamic analysis to discover polynomial and array invariants from program traces. It extends previous work by supporting nonlinear polynomial relations and more complex array invariants. The techniques formulate invariant discovery as a satisfiability problem and use mathematical solvers. An evaluation on test programs found most documented invariants, showing the approach can infer invariants for numeric algorithms. However, it may not scale to large programs and is limited by the traces collected.
The branch-and-bound method is used to solve optimization problems by traversing a state space tree. It computes a bound at each node to determine if the node is promising. Better approaches traverse nodes breadth-first and choose the most promising node using a bounding heuristic. The traveling salesperson problem is solved using branch-and-bound by finding an initial tour, defining a bounding heuristic as the actual cost plus minimum remaining cost, and expanding promising nodes in best-first order until finding the minimal tour.
This document discusses dynamic programming techniques. It covers matrix chain multiplication and all pairs shortest paths problems. Dynamic programming involves breaking down problems into overlapping subproblems and storing the results of already solved subproblems to avoid recomputing them. It has four main steps - defining a mathematical notation for subproblems, proving optimal substructure, deriving a recurrence relation, and developing an algorithm using the relation.
The document provides the MATLAB code to solve systems of linear equations using Gauss elimination with partial pivoting. It takes the matrix A and column matrix B as input from the user. It finds the maximum element in each column of A using partial pivoting to maintain stability. It then performs forward elimination on the augmented matrix [A B]. It displays the updated matrices after each step. Finally, it performs back substitution to calculate the values of the unknown matrix X and displays the result.
Cs6402 design and analysis of algorithms impartant part b questions appasamiappasami
This document outlines important questions from 5 units that could appear on the CS64O2 DESIGN AND ANALYSIS OF ALGORITHMS exam. Unit I covers algorithm problem types, problem solving fundamentals, asymptotic notations, and analyzing recursive/non-recursive algorithms. Unit II discusses closest pair, convex hull, TSP, KP, sorting algorithms, and binary search. Unit III is about dynamic programming, graph algorithms, OBST, and HMT. Unit IV is linear programming problems. Unit V covers complexity classes, backtracking, branch and bound, and approximation algorithms for NP-hard problems like TSP and KP. The document provides guidance on questions to study for the exam.
The document discusses algorithms and their analysis. It begins by defining an algorithm and listing requirements like being unambiguous and finite. It describes writing algorithms using pseudocode or flowcharts and proving their correctness. The document then discusses analyzing algorithms by measuring their time and space efficiency using orders of growth. It explains analyzing best, worst, and average cases and counting basic operations. Finally, it provides examples of analyzing simple algorithms involving if statements and loops.
This document summarizes key concepts in vector tools and geometry. It discusses vector spaces, operations on vectors like linear combinations and dot products, and representations of lines, planes, and polygons using parametric and point normal forms. It also describes algorithms for finding intersections between lines, rays and planes, and clipping lines to convex polygons.
This document discusses functional and imperative models of program design. It covers features of the functional model like recursion, tail recursion, and correctness proofs. The imperative model uses variables, assignments, and control structures. Proof rules are presented for loops, goto statements, functions, and procedures. Problem solving techniques like greedy algorithms are also discussed, applying concepts to problems like the knapsack problem and Huffman coding. Specific algorithms for these problems are outlined.
Secure Linear Transformation Based Cryptosystem using Dynamic Byte SubstitutionCSCJournals
Many classical cryptosystems are developed based on simple substitution. Hybrid cryptosystem using byte substitution and variable length sub key groups is a simple nonlinear algorithm but the cryptanalyst can find the length of each sub key group because the byte substitution is static and if the modulo prime number is small then byte substitution is limited to first few rows of S-box. In this paper an attempt is made to introduce the nonlinearity to the linear transformation based cryptosystem using dynamic byte substitution over GF (28). The secret value is added to the index value to shift the substitution to a new secret location dynamically. This adds extra security in addition to non-linearity, confusion and diffusion. The performance evaluation of the method is also studied and presented.
The document discusses analyzing the running time of algorithms using Big-O notation. It begins by introducing Big-O notation and how it is used to generalize the running time of algorithms as input size grows. It then provides examples of calculating the Big-O running time of simple programs and algorithms with loops but no subprogram calls or recursion. Key concepts covered include analyzing worst-case and average-case running times, and rules for analyzing the running time of programs with basic operations and loops.
The document describes the Regula Falsi method, a numerical method for estimating the roots of a polynomial function. The Regula Falsi method improves on the bisection method by using a value x that replaces the midpoint, serving as a new approximation of a root. An example problem demonstrates applying the Regula Falsi method to find the root of a function between 1 and 2 to within 3 decimal places. Limitations of the method include potential slow convergence, reliance on sign changes to find guesses, and inability to detect multiple roots.
The document discusses the theory of NP-completeness. It begins by defining the complexity classes P, NP, NP-hard, and NP-complete. It then explains the concepts of reduction and how none of the NP-complete problems can be solved in polynomial time deterministically. The document provides examples of NP-complete problems like satisfiability (SAT), vertex cover, and the traveling salesman problem. It shows how nondeterministic algorithms can solve these problems and how they can be transformed into SAT instances. Finally, it proves that SAT is the first NP-complete problem by showing it is in NP and NP-hard.
1. An algorithm is a sequence of unambiguous instructions to solve a problem within a finite amount of time. It takes an input, processes it, and produces an output.
2. Designing an algorithm involves understanding the problem, choosing a computational model and problem-solving approach, designing and proving the algorithm's correctness, analyzing its efficiency, coding it, and testing it.
3. Important algorithm design techniques include brute force, divide and conquer, decrease and conquer, transform and conquer, dynamic programming, and greedy algorithms.
This document provides an introduction to numerical methods and MATLAB programming for engineers. It covers topics such as vectors, functions, plots, and programming in MATLAB. The document is divided into multiple parts that cover various numerical methods topics, including solving equations, linear algebra, functions and data, and differential equations. MATLAB code and examples are provided throughout to demonstrate numerical techniques. The overall goal is to introduce both concepts of numerical methods and MATLAB programming within an engineering context.
Introduction to Algorithms and Asymptotic NotationAmrinder Arora
Asymptotic Notation is a notation used to represent and compare the efficiency of algorithms. It is a concise notation that deliberately omits details, such as constant time improvements, etc. Asymptotic notation consists of 5 commonly used symbols: big oh, small oh, big omega, small omega, and theta.
Skiena algorithm 2007 lecture16 introduction to dynamic programmingzukun
This document summarizes a lecture on dynamic programming. It begins by introducing dynamic programming as a powerful tool for solving optimization problems on ordered items like strings. It then contrasts greedy algorithms, which make locally optimal choices, with dynamic programming, which systematically searches all possibilities while storing results. The document provides examples of computing Fibonacci numbers and binomial coefficients using dynamic programming by storing partial results rather than recomputing them. It outlines three key steps to applying dynamic programming: formulating a recurrence, bounding subproblems, and specifying an evaluation order.
The document summarizes a talk on polynomial identity testing (PIT). PIT is the problem of determining if a polynomial computed by an arithmetic circuit is identical to the zero polynomial. The talk outlines the definition of PIT, its connection to circuit lower bounds, and surveys positive results for restricted circuit classes. It also provides examples of proof techniques for PIT on depth-3 and depth-4 circuits and discusses the relationship between PIT and polynomial factorization.
This document provides an overview of optimization techniques that can be performed using MATLAB. It discusses unconstrained optimization problems where the goal is to minimize or maximize an objective function without any constraints on the variables. Constrained optimization problems are also discussed, where the goal is to optimize the objective function subject to certain equality and inequality constraints. MATLAB functions like fminsearch and fmincon can be used to find the optimal solution for unconstrained and constrained problems respectively. Gradient-based methods for solving constrained optimization problems are also briefly covered.
This document discusses dynamic programming and provides examples to illustrate the technique. It begins by defining dynamic programming as a bottom-up approach to problem solving where solutions to smaller subproblems are stored and built upon to solve larger problems. It then provides examples of dynamic programming algorithms for calculating Fibonacci numbers, binomial coefficients, and finding shortest paths using Floyd's algorithm. The key aspects of dynamic programming like avoiding recomputing solutions and storing intermediate results in tables are emphasized.
Welcome to the Digital Signal Processing (DSP) Lab Manual. This manual is designed to be your comprehensive guide throughout your DSP laboratory sessions. Digital Signal Processing is a fundamental field in electrical engineering and computer science that deals with the manipulation of digital signals to achieve various objectives, such as filtering, transformation, and analysis. In this lab, you will have the opportunity to apply theoretical knowledge to practical, hands-on exercises that will deepen your understanding of DSP concepts.
This manual is structured to provide you with step-by-step instructions, explanations, and insights into the experiments you'll be performing. Each experiment is carefully designed to reinforce your understanding of fundamental DSP principles and help you develop the skills necessary for signal processing applications. Whether you are a student or an instructor, this manual is intended to facilitate a productive and enriching DSP lab experience.
Welcome to the Digital Signal Processing (DSP) Lab Manual. This manual is designed to be your comprehensive guide throughout your DSP laboratory sessions. Digital Signal Processing is a fundamental field in electrical engineering and computer science that deals with the manipulation of digital signals to achieve various objectives, such as filtering, transformation, and analysis. In this lab, you will have the opportunity to apply theoretical knowledge to practical, hands-on exercises that will deepen your understanding of DSP concepts.
This manual is structured to provide you with step-by-step instructions, explanations, and insights into the experiments you'll be performing. Each experiment is carefully designed to reinforce your understanding of fundamental DSP principles and help you develop the skills necessary for signal processing applications. Whether you are a student or an instructor, this manual is intended to facilitate a productive and enriching DSP lab experience.
The document summarizes a paper that presents techniques for using dynamic analysis to discover polynomial and array invariants from program traces. It extends previous work by supporting nonlinear polynomial relations and more complex array invariants. The techniques formulate invariant discovery as a satisfiability problem and use mathematical solvers. An evaluation on test programs found most documented invariants, showing the approach can infer invariants for numeric algorithms. However, it may not scale to large programs and is limited by the traces collected.
The branch-and-bound method is used to solve optimization problems by traversing a state space tree. It computes a bound at each node to determine if the node is promising. Better approaches traverse nodes breadth-first and choose the most promising node using a bounding heuristic. The traveling salesperson problem is solved using branch-and-bound by finding an initial tour, defining a bounding heuristic as the actual cost plus minimum remaining cost, and expanding promising nodes in best-first order until finding the minimal tour.
This document discusses dynamic programming techniques. It covers matrix chain multiplication and all pairs shortest paths problems. Dynamic programming involves breaking down problems into overlapping subproblems and storing the results of already solved subproblems to avoid recomputing them. It has four main steps - defining a mathematical notation for subproblems, proving optimal substructure, deriving a recurrence relation, and developing an algorithm using the relation.
The document provides the MATLAB code to solve systems of linear equations using Gauss elimination with partial pivoting. It takes the matrix A and column matrix B as input from the user. It finds the maximum element in each column of A using partial pivoting to maintain stability. It then performs forward elimination on the augmented matrix [A B]. It displays the updated matrices after each step. Finally, it performs back substitution to calculate the values of the unknown matrix X and displays the result.
Cs6402 design and analysis of algorithms impartant part b questions appasamiappasami
This document outlines important questions from 5 units that could appear on the CS64O2 DESIGN AND ANALYSIS OF ALGORITHMS exam. Unit I covers algorithm problem types, problem solving fundamentals, asymptotic notations, and analyzing recursive/non-recursive algorithms. Unit II discusses closest pair, convex hull, TSP, KP, sorting algorithms, and binary search. Unit III is about dynamic programming, graph algorithms, OBST, and HMT. Unit IV is linear programming problems. Unit V covers complexity classes, backtracking, branch and bound, and approximation algorithms for NP-hard problems like TSP and KP. The document provides guidance on questions to study for the exam.
The document discusses algorithms and their analysis. It begins by defining an algorithm and listing requirements like being unambiguous and finite. It describes writing algorithms using pseudocode or flowcharts and proving their correctness. The document then discusses analyzing algorithms by measuring their time and space efficiency using orders of growth. It explains analyzing best, worst, and average cases and counting basic operations. Finally, it provides examples of analyzing simple algorithms involving if statements and loops.
This document summarizes key concepts in vector tools and geometry. It discusses vector spaces, operations on vectors like linear combinations and dot products, and representations of lines, planes, and polygons using parametric and point normal forms. It also describes algorithms for finding intersections between lines, rays and planes, and clipping lines to convex polygons.
This document discusses functional and imperative models of program design. It covers features of the functional model like recursion, tail recursion, and correctness proofs. The imperative model uses variables, assignments, and control structures. Proof rules are presented for loops, goto statements, functions, and procedures. Problem solving techniques like greedy algorithms are also discussed, applying concepts to problems like the knapsack problem and Huffman coding. Specific algorithms for these problems are outlined.
Secure Linear Transformation Based Cryptosystem using Dynamic Byte SubstitutionCSCJournals
Many classical cryptosystems are developed based on simple substitution. Hybrid cryptosystem using byte substitution and variable length sub key groups is a simple nonlinear algorithm but the cryptanalyst can find the length of each sub key group because the byte substitution is static and if the modulo prime number is small then byte substitution is limited to first few rows of S-box. In this paper an attempt is made to introduce the nonlinearity to the linear transformation based cryptosystem using dynamic byte substitution over GF (28). The secret value is added to the index value to shift the substitution to a new secret location dynamically. This adds extra security in addition to non-linearity, confusion and diffusion. The performance evaluation of the method is also studied and presented.
The document discusses analyzing the running time of algorithms using Big-O notation. It begins by introducing Big-O notation and how it is used to generalize the running time of algorithms as input size grows. It then provides examples of calculating the Big-O running time of simple programs and algorithms with loops but no subprogram calls or recursion. Key concepts covered include analyzing worst-case and average-case running times, and rules for analyzing the running time of programs with basic operations and loops.
The document describes the Regula Falsi method, a numerical method for estimating the roots of a polynomial function. The Regula Falsi method improves on the bisection method by using a value x that replaces the midpoint, serving as a new approximation of a root. An example problem demonstrates applying the Regula Falsi method to find the root of a function between 1 and 2 to within 3 decimal places. Limitations of the method include potential slow convergence, reliance on sign changes to find guesses, and inability to detect multiple roots.
The document discusses the theory of NP-completeness. It begins by defining the complexity classes P, NP, NP-hard, and NP-complete. It then explains the concepts of reduction and how none of the NP-complete problems can be solved in polynomial time deterministically. The document provides examples of NP-complete problems like satisfiability (SAT), vertex cover, and the traveling salesman problem. It shows how nondeterministic algorithms can solve these problems and how they can be transformed into SAT instances. Finally, it proves that SAT is the first NP-complete problem by showing it is in NP and NP-hard.
1. An algorithm is a sequence of unambiguous instructions to solve a problem within a finite amount of time. It takes an input, processes it, and produces an output.
2. Designing an algorithm involves understanding the problem, choosing a computational model and problem-solving approach, designing and proving the algorithm's correctness, analyzing its efficiency, coding it, and testing it.
3. Important algorithm design techniques include brute force, divide and conquer, decrease and conquer, transform and conquer, dynamic programming, and greedy algorithms.
This document provides an introduction to numerical methods and MATLAB programming for engineers. It covers topics such as vectors, functions, plots, and programming in MATLAB. The document is divided into multiple parts that cover various numerical methods topics, including solving equations, linear algebra, functions and data, and differential equations. MATLAB code and examples are provided throughout to demonstrate numerical techniques. The overall goal is to introduce both concepts of numerical methods and MATLAB programming within an engineering context.
Introduction to Algorithms and Asymptotic NotationAmrinder Arora
Asymptotic Notation is a notation used to represent and compare the efficiency of algorithms. It is a concise notation that deliberately omits details, such as constant time improvements, etc. Asymptotic notation consists of 5 commonly used symbols: big oh, small oh, big omega, small omega, and theta.
Skiena algorithm 2007 lecture16 introduction to dynamic programmingzukun
This document summarizes a lecture on dynamic programming. It begins by introducing dynamic programming as a powerful tool for solving optimization problems on ordered items like strings. It then contrasts greedy algorithms, which make locally optimal choices, with dynamic programming, which systematically searches all possibilities while storing results. The document provides examples of computing Fibonacci numbers and binomial coefficients using dynamic programming by storing partial results rather than recomputing them. It outlines three key steps to applying dynamic programming: formulating a recurrence, bounding subproblems, and specifying an evaluation order.
The document summarizes a talk on polynomial identity testing (PIT). PIT is the problem of determining if a polynomial computed by an arithmetic circuit is identical to the zero polynomial. The talk outlines the definition of PIT, its connection to circuit lower bounds, and surveys positive results for restricted circuit classes. It also provides examples of proof techniques for PIT on depth-3 and depth-4 circuits and discusses the relationship between PIT and polynomial factorization.
This document provides an overview of optimization techniques that can be performed using MATLAB. It discusses unconstrained optimization problems where the goal is to minimize or maximize an objective function without any constraints on the variables. Constrained optimization problems are also discussed, where the goal is to optimize the objective function subject to certain equality and inequality constraints. MATLAB functions like fminsearch and fmincon can be used to find the optimal solution for unconstrained and constrained problems respectively. Gradient-based methods for solving constrained optimization problems are also briefly covered.
This document discusses dynamic programming and provides examples to illustrate the technique. It begins by defining dynamic programming as a bottom-up approach to problem solving where solutions to smaller subproblems are stored and built upon to solve larger problems. It then provides examples of dynamic programming algorithms for calculating Fibonacci numbers, binomial coefficients, and finding shortest paths using Floyd's algorithm. The key aspects of dynamic programming like avoiding recomputing solutions and storing intermediate results in tables are emphasized.
Welcome to the Digital Signal Processing (DSP) Lab Manual. This manual is designed to be your comprehensive guide throughout your DSP laboratory sessions. Digital Signal Processing is a fundamental field in electrical engineering and computer science that deals with the manipulation of digital signals to achieve various objectives, such as filtering, transformation, and analysis. In this lab, you will have the opportunity to apply theoretical knowledge to practical, hands-on exercises that will deepen your understanding of DSP concepts.
This manual is structured to provide you with step-by-step instructions, explanations, and insights into the experiments you'll be performing. Each experiment is carefully designed to reinforce your understanding of fundamental DSP principles and help you develop the skills necessary for signal processing applications. Whether you are a student or an instructor, this manual is intended to facilitate a productive and enriching DSP lab experience.
Welcome to the Digital Signal Processing (DSP) Lab Manual. This manual is designed to be your comprehensive guide throughout your DSP laboratory sessions. Digital Signal Processing is a fundamental field in electrical engineering and computer science that deals with the manipulation of digital signals to achieve various objectives, such as filtering, transformation, and analysis. In this lab, you will have the opportunity to apply theoretical knowledge to practical, hands-on exercises that will deepen your understanding of DSP concepts.
This manual is structured to provide you with step-by-step instructions, explanations, and insights into the experiments you'll be performing. Each experiment is carefully designed to reinforce your understanding of fundamental DSP principles and help you develop the skills necessary for signal processing applications. Whether you are a student or an instructor, this manual is intended to facilitate a productive and enriching DSP lab experience.
This document contains information about the Digital Signal Processing lab at Shadan College of Engineering & Technology. It includes:
1. A list of 12 experiments to be conducted in the lab, related to topics like generating signals, implementing filters, and analyzing system responses.
2. An introduction to MATLAB, describing its basic functions and capabilities for numerical computation and signal processing.
3. Programs and instructions for carrying out specific DSP experiments in MATLAB, including generating basic signals, computing the DFT/IDFT of sequences, and determining the impulse/frequency responses of systems defined by difference equations.
The document provides students with an overview of the lab activities and teaches them how to use MATLAB for digital signal
The number theory is a branch of mathematics which is primarily dedicated to the
study of integers. The number theory, as such, is less applied in engineering compared to
calculus, geometry, etc. The problem was that it could not be used directly in any
application. But, the number theory, combined with the computational power of modern
computers, gives interesting solutions to real-life problems. It has many uses in various
fields such as cryptography, computing, numerical analysis and so on. Here, we focus on
the applications of the number theory about engineering challenges.
Problem 2.19. How much time is required to add or subtract two large.pdfJUSTSTYLISH3B2MOHALI
Problem 2.19. How much time is required to add or subtract two large integers of size m and n
respectively? Sketch the appropriate algorithm.
Solution
For the large integers, they can not be accomodated in the permitted range of 32 bits of standard
integer(Exact size depends on the system and environment).
So to add two very large numbers, we can keep them in form of string and then process them
from right side, one character at a time.
Below is the algorithm:
Suppose we have got two nos 125372 432
1. Take two strings s1 and s2.
2. copy 125372 in s1, and 432 in s2
3. Compute the length diff: abs(length(s1) - length(s2)). Left pad the small string with zeroes so
that size of both the strings are same. Lets assume length is n now.
4. Create a result string of (n+1) character, as the addition might produce a carry on the last.
5. Now start from the right most digit of both nos. Compute the int value of single digit in both
nos as shown below. To get the int value of a digit, we subtract \'0\' in from of character from the
String chacter, so that we get exact digit.
6. Now sum both these digits and put the result\'s unit bit in resultant string at ith position. You
might get an carry in this step, which you need to remember, when you go to process the (i-1)th
digit of the nos.
For( i=n; i >= 1; i-- ) {
// TO find out the digit at iTh pos in s1 in number format from character format
int n1 = s1.charAt(i) - \'0\'
int n2 = s2.charAt(i) - \'0\'
int carry = ((n1+n2)>=10) ? 1: 0;
result[i+1] = \'0\' + ((n1+n2)%10); //only put unit digit in result string, that too in form of
character
}
7. Keep doing the above steps for all the n digits. Now you may get a overall carry at the last
step, which you need to keep on result[0] pos. And your reslt string is prepared with the sum.
Similary can be done for subtraction also..
Traveling Salesman Problem in Distributed Environmentcsandit
In this paper, we focus on developing parallel algorithms for solving the traveling salesman problem (TSP) based on Nicos Christofides algorithm released in 1976. The parallel algorithm
is built in the distributed environment with multi-processors (Master-Slave). The algorithm is installed on the computer cluster system of National University of Education in Hanoi,
Vietnam (ccs1.hnue.edu.vn) and uses the library PJ (Parallel Java). The results are evaluated and compared with other works.
TRAVELING SALESMAN PROBLEM IN DISTRIBUTED ENVIRONMENTcscpconf
The document describes developing a parallel algorithm for solving the traveling salesman problem (TSP) based on Christofides' algorithm. It discusses implementing Christofides' algorithm in a distributed environment using multiple processors. The parallel algorithm divides the graph vertices and distance matrix across slave processors, which calculate the minimum spanning tree in parallel. The master processor then finds odd-degree vertices, performs matching, and finds the Hamiltonian cycle to solve TSP. The algorithm is tested on a computer cluster using graphs of 20,000 and 30,000 nodes, showing improved runtime over the sequential algorithm.
The document describes implementing Lagrange interpolation and least squares polynomial fitting in MATLAB. It includes:
1) An M-file to calculate interpolated values using Lagrange basis polynomials for given x and y data and test point xx.
2) Using polyfit to find the coefficients of a polynomial of degree k that best fits the given x and y data in a least squares sense.
3) Plotting the original data points and polynomial curves for different values of k to visualize the fitting.
MATLAB DOCUMENTATION ON SOME OF THE MODULES
A.Generate videos in which a skeleton of a person doing the following Gestures.
1.Tilting his head to right and left
2.Tilting his hand to right and left
3.Walking
in matlab.
B. Write a MATLAB program that converts a decimal number to Roman number and vice versa.
C.Using EZ plot & anonymous functions plot the following:
· Y=Sqrt(X)
· Y= X^2
· Y=e^(-XY)
D.Take your picture and
· Show R, G, B channels along with RGB Image in same figure using sub figure.
· Convert into HSV( Hue, saturation and value) and show the H,S,V channels along with HSV image
E.Record your name pronounced by yourself. Try to display the signal(name) in a plot vs Time, using matlab.
F.Write a script to open a new figure and plot five circles, all centered at the origin and with increasing radii. Set the line width for each circle to something thick (at least 2 points), and use the colors from a 5-color jet colormap (jet).
G. NEWTON RAPHSON AND SECANT METHOD
H.Write any one of the program to do following things using file concept.
1.Create or Open a file
2. Read data from the file and write data to another file
3. Append some text to already existed file
4. Close the file
I.Write a function to perform following set operations
1.Union of A and B
2. Intersection of A and B
3. Complement of A and B
(Assume A= {1, 2, 3, 4, 5, 6}, B= {2, 4, 6})
The document provides a lab manual for the course GE3171 - Problem Solving and Python Programming Laboratory. It includes the course objectives, list of experiments, syllabus, and programs for various experiments involving Python programming concepts like lists, tuples, conditionals, loops, functions etc. The experiments cover problems on real-life applications such as electricity billing, library management, vehicle components, building materials etc. The document demonstrates how to write Python programs to solve such problems and validate the output.
The document contains details about experiments performed in a Digital Signal Processing practical course. It includes the aims, apparatus required, theory, source code and results for experiments involving MATLAB programs to generate basic signals like impulse, step, ramp and exponential signals; sine and cosine signals; quantization; sampling theorem; linear convolution; autocorrelation; and cross-correlation. Programs were written in MATLAB to perform the various digital signal processing tasks and the output was verified.
This document provides an introduction to computational finance using MATLAB. It discusses MATLAB basics like matrices, vectors, solving linear equations, and generating random numbers. Key points covered include:
- MATLAB is well-suited for numerical linear algebra operations on matrices and vectors.
- Functions like rand and randn are used to generate uniformly distributed and Gaussian/normal distributed random numbers, which are important in finance.
- Histograms can be used to visualize the distributions of random numbers and converge to the probability density function as the number of samples increases.
dynamic programming complete by Mumtaz Ali (03154103173)Mumtaz Ali
The document discusses dynamic programming, including its meaning, definition, uses, techniques, and examples. Dynamic programming refers to breaking large problems down into smaller subproblems, solving each subproblem only once, and storing the results for future use. This avoids recomputing the same subproblems repeatedly. Examples covered include matrix chain multiplication, the Fibonacci sequence, and optimal substructure. The document provides details on formulating and solving dynamic programming problems through recursive definitions and storing results in tables.
TOPIC:-
SCIENTIFIC CALCULATOR
PURPOSE:-
To create scientific calculator class to perform the task related to scientific calculations. Which include finding square or cube etc.
The scientific calculator perform following task:-
1. Determine the square of the number.
2. Determine the square root of the number
3. Determine the first number power of the second number
4. Determine the factorial of a number
5. Determine the sin, cos and tan value of the number.
6. Determine the logarithm, natural logarithm and exponential of the number.
The program demonstrates linear and circular convolution of sequences using MATLAB. For linear convolution, the conv function is used to convolve two input sequences and plot the results. For circular convolution, the FFT of each sequence is taken, multiplied together and inverse FFT applied to obtain the output, which is also plotted. The program thus allows generation and visualization of linear and circular convolution.
1. Various common signals were generated using MATLAB, including unit impulse, unit step, ramp, sinc, sine, sawtooth, square, and triangular signals. Both continuous and discrete forms were produced.
2. Operations on the generated signals included plotting their amplitude over time or index, adding titles and labels to figures, and displaying the results in different subplot configurations for comparison.
3. Common periodic signals like sine and square waves were generated along with aperiodic signals such as ramp, impulse and step functions to demonstrate the creation of basic continuous and discrete time signals in MATLAB for analysis and simulation.
A New Deterministic RSA-Factoring AlgorithmJim Jimenez
This document proposes a new deterministic algorithm for factoring RSA numbers (n = p * q) and describes how it works. The algorithm uses schoolboy multiplication and counting/probability concepts to sequentially produce possible values for the prime factors p and q in a way that their product equals the original RSA number n. It has two main procedures: 1) A Producer procedure that sequentially generates values for the digits of p and q to match the first half of the digits in n. 2) An Eliminator procedure that eliminates combinations of p and q that do not match the second half of digits in n, leaving the correct factors. Pseudocode is provided to demonstrate how it works on a sample number. The document concludes by analyzing the running
This document provides steps to enable SSL/HTTPS for an Elasticsearch server. It involves generating certificates, configuring Elasticsearch, and enabling TLS for both transport and HTTP layers. The process includes generating a CA certificate, creating node certificates signed by the CA, editing the Elasticsearch configuration file, and restarting Elasticsearch to enable HTTPS.
Anti lock braking (ABS) Model based Design in MATLAB-SimulinkOmkar Rane
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This document discusses bootloaders for embedded systems. It defines a bootloader as the first code executed after a system powers on or resets that is responsible for loading the operating system kernel. The document then describes the tasks of a bootloader like initializing hardware, loading binaries from storage, and providing a shell. It outlines the booting process differences between desktops and embedded systems. Finally, it focuses on the universal bootloader U-Boot, describing its directory structure, configuration, building process, and commands.
Concept of Diversity & Fading (wireless communication)Omkar Rane
This document discusses concepts related to fading and diversity in wireless communication systems. It introduces fading as signal variations caused by multipath interference from multiple signal propagation paths. It describes two types of fading: large-scale fading due to path loss and shadowing, and small-scale fading which includes fast fading due to mobility and slow fading due to shadowing. It also discusses different diversity techniques that can be used to combat fading, including space, polarization, frequency and time diversity.
The document describes several projects completed as part of a semester-long internship at Tata Motors. The projects include:
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Machine Learning Model for M.S admissionsOmkar Rane
The document describes building a machine learning model to predict admissions for a Master's program. It loads student data, preprocesses it by imputing missing values, splits it into training and test sets, trains several models and evaluates their accuracy via cross-validation. Logistic regression achieved the best results with 77.5% accuracy. The trained logistic regression model is used to make predictions on new student data.
The document defines a function called DelayMs that uses Timer0 to create delays in milliseconds. It initializes Timer0 to increment every 12,000 clock cycles, which at a clock speed of 12MHz equals 1ms. The main function blinks an LED by turning it on for 500ms, then off for 500ms in a continuous loop using the DelayMs function.
ADC (Analog to Digital conversion) using LPC 1768Omkar Rane
This document contains code for an interfacing diagram that converts analog to digital values and displays the output in hexadecimal on an LCD screen. The code configures the ADC and UART ports, performs an analog to digital conversion on an input voltage, converts the output to hexadecimal, and transmits the values over UART. It also contains calculations for the step size of the ADC based on the reference voltage and bits. An observation table shows theoretical and measured output values for different input voltages.
PWM based motor speed control using LPC 1768Omkar Rane
This code configures the LPC1768 microcontroller's PWM peripheral to generate a pulse width modulated signal on pin PWM1.5. It initializes the PWM timer and match registers, enables the PWM output, and then uses a for loop to incrementally change the duty cycle of the PWM signal from 10% to 100% over 10 steps, pausing 1 second between each step. This allows the user to see the PWM output change frequency smoothly over time.
UART interfacing on LPC1768 (Cortex M3 micro controller)Omkar Rane
This code configures the UART0 port on an LPC1768 microcontroller to communicate at 9600 baud. It sets the pin configuration for transmit and receive, enables the FIFO and clears flags. It then configures the baud rate divisor, character length, parity and stop bits. The code enters a loop to continuously read incoming serial data and echo it back on the transmit line.
This document provides an interfacing diagram and code for an LPC1768 microcontroller to toggle a set of GPIO pins. The code uses PINSEL4 and FIO2DIR registers to configure pins P0.2 to P0.7 as GPIO outputs. It then uses a for loop to toggle all the pins high and low by setting and clearing the FIO2SET and FIO2CLR registers respectively.
CAN interfacing on LPC1768 (ARM Cortex M3 based Micro controller)Omkar Rane
The document describes code for initializing and transmitting messages over a CAN bus on an LPC1768 microcontroller, including initializing the CAN controller, setting an acceptance filter to define which message IDs will be received, and sending a test message over CAN1 while monitoring reception on CAN2. Functions are defined for CAN initialization, setting the acceptance filter, and transmitting messages with given IDs and data over CAN1.
Vlisi Course project presentation:Keypad ScannerOmkar Rane
This document describes a keypad scanner project implemented on an FPGA board. It includes the truth table and equations for scanning the keys of a 3x4 keypad to drive 4 LEDs. The Verilog code implements this design using logic gates and assigns statements. The code was submitted by 3 students for their VLSI design course, overseen by instructor Dr. Dipti Sakhare. References on Verilog, digital design, and the FPGA board used are also listed.
VlSI course project report : Keypad Scanner Omkar Rane
This project report describes the design, simulation, and implementation of a keypad scanner on an FPGA board. A Verilog code was written using a dataflow modeling style to scan a 3x4 keypad and output a 5-bit binary number corresponding to the pressed key. The code was simulated using Xilinx Vivado software. Finally, the design was implemented on a Digilent Nexys ddr FPGA board and tested successfully.
LPC 1768 A study on Real Time clock featuresOmkar Rane
The document discusses the real-time clock (RTC) and backup registers for the LPC176x/5x microcontroller. It describes the RTC oscillator, clock control register, time counter registers, consolidated time registers, and steps for using the RTC including initializing, setting date and time, and reading date and time. Potential applications mentioned include a digital clock, timer controller, and data logging systems.
This document provides an overview and reference manual for the Nexys4 DDR FPGA board. It describes the board's features such as the Artix-7 FPGA, memory resources, ports, and peripherals. It also provides details on power supplies, FPGA configuration options, and migrating designs from the original Nexys4 board. The manual is intended to help users understand and utilize the capabilities of the Nexys4 DDR board.
The document discusses linear regression for predicting salary based on years of experience. It introduces gradient descent for linear regression, which iteratively updates the slope and intercept parameters (θ1 and θ2) to minimize cost and improve predictions. Gradient descent takes steps in the direction of the steepest descent down the cost function landscape. The learning rate determines step sizes and must be optimized for accurate predictions within a reasonable time.
Construction Materials (Paints) in Civil EngineeringLavish Kashyap
This file will provide you information about various types of Paints in Civil Engineering field under Construction Materials.
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Welcome to the May 2025 edition of WIPAC Monthly celebrating the 14th anniversary of the WIPAC Group and WIPAC monthly.
In this edition along with the usual news from around the industry we have three great articles for your contemplation
Firstly from Michael Dooley we have a feature article about ammonia ion selective electrodes and their online applications
Secondly we have an article from myself which highlights the increasing amount of wastewater monitoring and asks "what is the overall" strategy or are we installing monitoring for the sake of monitoring
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This research is oriented towards exploring mode-wise corridor level travel-time estimation using Machine learning techniques such as Artificial Neural Network (ANN) and Support Vector Machine (SVM). Authors have considered buses (equipped with in-vehicle GPS) as the probe vehicles and attempted to calculate the travel-time of other modes such as cars along a stretch of arterial roads. The proposed study considers various influential factors that affect travel time such as road geometry, traffic parameters, location information from the GPS receiver and other spatiotemporal parameters that affect the travel-time. The study used a segment modeling method for segregating the data based on identified bus stop locations. A k-fold cross-validation technique was used for determining the optimum model parameters to be used in the ANN and SVM models. The developed models were tested on a study corridor of 59.48 km stretch in Mumbai, India. The data for this study were collected for a period of five days (Monday-Friday) during the morning peak period (from 8.00 am to 11.00 am). Evaluation scores such as MAPE (mean absolute percentage error), MAD (mean absolute deviation) and RMSE (root mean square error) were used for testing the performance of the models. The MAPE values for ANN and SVM models are 11.65 and 10.78 respectively. The developed model is further statistically validated using the Kolmogorov-Smirnov test. The results obtained from these tests proved that the proposed model is statistically valid.
Jacob Murphy Australia - Excels In Optimizing Software ApplicationsJacob Murphy Australia
In the world of technology, Jacob Murphy Australia stands out as a Junior Software Engineer with a passion for innovation. Holding a Bachelor of Science in Computer Science from Columbia University, Jacob's forte lies in software engineering and object-oriented programming. As a Freelance Software Engineer, he excels in optimizing software applications to deliver exceptional user experiences and operational efficiency. Jacob thrives in collaborative environments, actively engaging in design and code reviews to ensure top-notch solutions. With a diverse skill set encompassing Java, C++, Python, and Agile methodologies, Jacob is poised to be a valuable asset to any software development team.
Welcome to MIND UP: a special presentation for Cloudvirga, a Stewart Title company. In this session, we’ll explore how you can “mind up” and unlock your potential by using generative AI chatbot tools at work.
Curious about the rise of AI chatbots? Unsure how to use them-or how to use them safely and effectively in your workplace? You’re not alone. This presentation will walk you through the practical benefits of generative AI chatbots, highlight best practices for safe and responsible use, and show how these tools can help boost your productivity, streamline tasks, and enhance your workday.
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OPTIMIZING DATA INTEROPERABILITY IN AGILE ORGANIZATIONS: INTEGRATING NONAKA’S...ijdmsjournal
Agile methodologies have transformed organizational management by prioritizing team autonomy and
iterative learning cycles. However, these approaches often lack structured mechanisms for knowledge
retention and interoperability, leading to fragmented decision-making, information silos, and strategic
misalignment. This study proposes an alternative approach to knowledge management in Agile
environments by integrating Ikujiro Nonaka and Hirotaka Takeuchi’s theory of knowledge creation—
specifically the concept of Ba, a shared space where knowledge is created and validated—with Jürgen
Habermas’s Theory of Communicative Action, which emphasizes deliberation as the foundation for trust
and legitimacy in organizational decision-making. To operationalize this integration, we propose the
Deliberative Permeability Metric (DPM), a diagnostic tool that evaluates knowledge flow and the
deliberative foundation of organizational decisions, and the Communicative Rationality Cycle (CRC), a
structured feedback model that extends the DPM, ensuring long-term adaptability and data governance.
This model was applied at Livelo, a Brazilian loyalty program company, demonstrating that structured
deliberation improves operational efficiency and reduces knowledge fragmentation. The findings indicate
that institutionalizing deliberative processes strengthens knowledge interoperability, fostering a more
resilient and adaptive approach to data governance in complex organizations.
Newly poured concrete opposing hot and windy conditions is considerably susceptible to plastic shrinkage cracking. Crack-free concrete structures are essential in ensuring high level of durability and functionality as cracks allow harmful instances or water to penetrate in the concrete resulting in structural damages, e.g. reinforcement corrosion or pressure application on the crack sides due to water freezing effect. Among other factors influencing plastic shrinkage, an important one is the concrete surface humidity evaporation rate. The evaporation rate is currently calculated in practice by using a quite complex Nomograph, a process rather tedious, time consuming and prone to inaccuracies. In response to such limitations, three analytical models for estimating the evaporation rate are developed and evaluated in this paper on the basis of the ACI 305R-10 Nomograph for “Hot Weather Concreting”. In this direction, several methods and techniques are employed including curve fitting via Genetic Algorithm optimization and Artificial Neural Networks techniques. The models are developed and tested upon datasets from two different countries and compared to the results of a previous similar study. The outcomes of this study indicate that such models can effectively re-develop the Nomograph output and estimate the concrete evaporation rate with high accuracy compared to typical curve-fitting statistical models or models from the literature. Among the proposed methods, the optimization via Genetic Algorithms, individually applied at each estimation process step, provides the best fitting result.
Digit Factorial Chains .(Euler Problem -74) (Matlab Programming Solution)
1. A Mini Project Report on
‘Digit Factorial Chains’
Submitted By
Abhishek Sainkar S177089
Akash Nimbalkar S177069
Omkar Rane S177086
A Mini Project report submitted as a partial fulfillment towards Practical of Applied
Mathematics for semester IV of S. Y. B.Tech., Electronics & Telecommunication
Engineering
2017-18
Under the guidance of
Mrs. Usha Verma
Department of Electronics & Telecommunication Engineering
MIT Academy of Engineering, Alandi (D), Pune-412 105
2. MIT Academy of Engineering, Alandi (D)
S. Y. B. Tech. Sem IV Applied Mathematics Mini Project 2
C E R T I F I C A T E
This is to certify that Abhishek Sainkar(S177089),Akash
Nimbalkar(S177069),Omkar Rane (S177086) of Department of
Electronics & Telecommunication Engineering, MIT Academy of
Engineering, Alandi (D), Pune have submitted Mini Project report on
“Digit Factorial Chain” as a partial fulfillment of Semester-IV S. Y. B.
Tech. for completion of Applied Mathematics Practical work during the
academic year 2017-18.
Mrs. Usha Verma Prof. Satish Gajbhiv
(Guide) (Head, Applied Sciences)
External Examiner
3. MIT Academy of Engineering, Alandi (D)
S. Y. B. Tech. Sem IV Applied Mathematics Mini Project 3
ACKNOWLEDGEMENT
I take this opportunity to record my profound gratitude and indebtedness to Mrs. Usha Verma,
Assistant Professor, Department of Electronics Engineering for her inspiring guidance, valuable
advices, constant encouragement and untiring supervision throughout my research work.
I express my deep sense of gratitude to S. G. Gajbhiv, Head, Dept. of Applied Sciences, for
his continuous inspiration and encouragement.
Finally, I would like to acknowledge and express my special thanks to my family, friends
and classmates for their patience, encouragement, support they have made during the period of this
work.
Abhishek Sainkar S177089
Akash Nimbalkar S177000
Omkar Rane S177086
4. MIT Academy of Engineering, Alandi (D)
S. Y. B. Tech. Sem IV Applied Mathematics Mini Project 4
List of Figures
Sr. No. Figure
No.
Title of Figure Page
No.
1 3.1(a)
Flowchart of Project
14
2 3.1(b)
Flowchart of project (contd.)
15
3 5.1(a)
Loop length 1000000, output of code
17
4 5.1(b)
Result validation by Interactive test for loop length 1000000
17
5 5.2(a)
loop length- 1000, output of code
18
6 5.2(b)
Result validation by Interactive test for loop length 1000
18
7 5.3(a)
loop length- 4500, output of code
19
8 5.2(b)
Result validation by Interactive test for loop length 4500
19
9 5.4(a)
loop length-9893, output of code
20
10 5.2(b)
Result validation by Interactive test for loop length 9893
20
5. MIT Academy of Engineering, Alandi (D)
S. Y. B. Tech. Sem IV Applied Mathematics Mini Project 5
INDEX
List of Figures …………………………………….……………………………………….. 5
Abstract ……………………………………………………………………………………..7
CHAPTER 1 INTRODUCTION ……………………………………………………..8
1.1 Problem Definition
1.2 Theoretical Background
CHAPTER 2 METHODOLOGY …………………………………………………….9
CHAPTER 3 ALGORITHM AND FLOWCHART……….……...……………......10
CHAPTER 4 IMPLEMENTATION IN MATLAB ………………………………..14
CHAPTER 5 RESULTS …………………….………………………………………15
CHAPTER 6 CONCLUSION ………………………………………………………20
REFERENCES 21
6. MIT Academy of Engineering, Alandi (D)
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ABSTRACT
In mathematics, the factorial of a non-negative integer n, denoted by n!. The factorial operation is
used in many areas of mathematics, mainly in combinatorics, algebra, and mathematical analysis.
Its most basic occurrence is the fact that there is n! ways to arrange n distinct objects into a sequence.
This problem is counting digit factorial chains of non-repeating number between 69 to one million.
The longest non-repeating chain below one million contain sixty non-repeating terms. For that, it is
calculated that how many chains below one million contain exactly sixty non-repeating terms.
Its seen that there is wide range application of factorials in mathematical formulations like sine,
cosine and exponential expansion series. This series are used programmed in the form of code such
that we can calculate values of sine and cosine angles using a computer program. Digit factorial sum
is used in Cunningham number implementation.
7. MIT Academy of Engineering, Alandi (D)
S. Y. B. Tech. Sem IV Applied Mathematics Mini Project 7
CHAPTER 1: INTRODUCTION
1.1 Problem Definition:
The number is well known for the property that the sum of the factorial of its digits is equal to 145:
1! +4! +5! = 1+24+120=145
Perhaps less well known is 169, in that it produces the longest chain of numbers that link back to
169; it turns out that there are only three such loops that exist:
169 -363601-1454-169
871-45361-871
872-45362-872
It is not difficult to prove that EVERY starting number will eventually get stuck in a loop. For
example,
69-363600-1454-169-363601(-1454)
78-45360-871-45361(-871)
540-145(-145)
Starting with 69 produces a chain of five non-repeating terms, but the longest non-repeating chain
with a starting number below one million is sixty terms.
For a given length and limit print all the integers which have chain length.
8. MIT Academy of Engineering, Alandi (D)
S. Y. B. Tech. Sem IV Applied Mathematics Mini Project 8
1.2 Theoretical Background:
Factorial of a number n can be defined as product of all positive numbers less than or equal to n. It
the multiplying sequence of numbers in a descending order[11].
1!=11!=1
2!=2×12!=2×1
3!=3×2×13!=3×2×1
4!=4×3×2×14!=4×3×2×1
..
n!n! = n×(n−1) ×(n−2) ...×2×1
In mathematics, the factorial of a non-negative integer n, denoted by n!, is the product of all
positive integers less than or equal to n. he value of 0! is 1, according to the convention for an empty
product. The factorial operation is encountered in many areas of mathematics, notably
in combinatorics, algebra, and mathematical analysis. Its most basic occurrence is the fact that there
are n! ways to arrange n distinct objects into a sequence (i.e., permutations of the set of objects).
This fact was known at least as early as the 12th century, to Indian scholars. Fabian, in 1677,
described factorials as applied to change ringing. After describing a recursive approach, Stedman
gives a statement of a factorial (using the language of the original): Now the nature of these methods
is such, that the changes on one number comprehends [includes] the changes on all lesser numbers,
... insomuch that a complete Peal of changes on one number seemed to be formed by uniting of the
complete Peals on all lesser numbers into one entire body. The notation n! was introduced by the
French mathematician Christian Kramp in 1808. The definition of the factorial function can also
be extended to non-integer arguments, while retaining its most important properties; this involves
more advanced mathematics, notably techniques from mathematical analysis. He further writes that
“about giving the name factorial I have given it the name 'faculty'. Arbogast has substituted the name
'factorial' which is clearer and more French[10]. In adopting his idea, I congratulate myself on paying
homage to the memory of my friend. ... I use the very simple notation n! to designate the product of
numbers decreasing from n to unity, i.e. n (n - 1) (n - 2) ... 3 . 2 . 1. The constant use in combinatorial
analysis, in most of my proofs, that I make of this idea, has made this notation necessary.”
In digit factorial chain, individual digits factorial is taken and by doing sum of each digit’s factorial
we get another number then, again taking individual factorial sum the process is continued till we
get the same starting number and the chain of these numbers is calculated.
9. MIT Academy of Engineering, Alandi (D)
S. Y. B. Tech. Sem IV Applied Mathematics Mini Project 9
CHAPTER 2: METHODOLOGY
Various functions used in MATLAB for implementation of code are as follows:
1) factorial() : f = factorial(n) returns the product of all positive integers less than
or equal to n, where n is a nonnegative integer value. If n is an array,
then f contains the factorial of each value of n. The data type and size of f is the
same as that of factorial of individual number that is separated then using
factorial is calculated.
2) sum():sum (A) returns the sum along different dimensions of the fi array A.
If A is a vector, sum(A) returns the sum of the elements.
If A is a matrix, sum(A) treats the columns of A as vectors, returning a row
vector of the sums of each column.
If A is a multidimensional array, sum(A) treats the values along the first non-
singleton dimension as vectors, returning an array of row vectors. Every
factorial of individual number is added or summed using this function. All the
process is done with the help of looping statement.
3) dec2base(): str = dec2base(d, base) converts the nonnegative integer d to the
specified base. d must be a nonnegative integer smaller than the value returned
by flintmax, and base must be an integer between 2 and 36. The returned
argument str is a character vector.str = dec2base(d, base, n) produces a
representation with at least n digits. This function converts decimal tom base
value it is used for digit separation with singleton attribute of function.
4) mod():mod(a,m) returns the remainder after division of a by m, where a is the
dividend and m is the divisor. This function is often called the modulo operation,
which can be expressed as b = a - m.*floor(a./m). The mod function follows the
convention that mod(a,0) returns a.
5) floor():floor rounds a number to the next smaller integer. For complex
arguments, floor rounds the real and the imaginary parts separately. For real
numbers and exact expressions representing real numbers, floor returns integers.
For arguments that contain symbolic identifiers, floor returns unevaluated
function calls. For floating-point intervals, floor returns floating-point intervals
containing all the results of applying floor to the real or complex numbers inside
the interval.
10. MIT Academy of Engineering, Alandi (D)
S. Y. B. Tech. Sem IV Applied Mathematics Mini Project 10
6) While loop: while expression, statements, end evaluates an expression, and
repeats the execution of a group of statements in a loop while the expression is
true. An expression is true when its result is nonempty and contains only
nonzero elements (logical or real numeric). Otherwise, the expression is false.
7) For loop: The "for" loop evaluates the entire expression at the time that the
flow of control enters the "for" loop from above. The entire set of values is
recorded at the time the loop is entered from above. No calculations within the
body of the loop can change the set of values: if a variable is mentioned in the
"for" and that variable's value is changed within the loop, then the "for" loop's
recorded expression list will not change.
11. MIT Academy of Engineering, Alandi (D)
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CHAPTER 3: ALGORITHM AND FLOWCHART
Step 1: Start
Step 2: take input q=Largest number limit for loop length, w= number of terms in chain
Step 3: Declare variables Final=0 n=I, x=i, l=0, c=0, flag=0, a=i
Step4: Loop
for i=69: q
while(n>0)
d=mod(n,10)
c=c+factorial(d)
n=floor(n./10)
if n<=0
if c==x or length(a)==w
for i=1: w
if (c==a(i))
flag=1;
break
a1=unique(a)
if flag==1 && length(a1) =length(a) & length(a)=w
ans12=a
final=final+1
if length(a)>w
break
a=[a,c]
n=c
c=0
Step 5: Declare output=final
Step 6: Display Final
Step 7: End
12. MIT Academy of Engineering, Alandi (D)
S. Y. B. Tech. Sem IV Applied Mathematics Mini Project 12
FLOWCHART
Figure 3.5(a) Flowchart of Project
13. MIT Academy of Engineering, Alandi (D)
S. Y. B. Tech. Sem IV Applied Mathematics Mini Project 13
Figure 3.1(b) Flowchart of project (contd.)
14. MIT Academy of Engineering, Alandi (D)
S. Y. B. Tech. Sem IV Applied Mathematics Mini Project 14
CHAPTER 4: IMPLEMENTATION IN MATLAB
% Applied Mathematics Practical Project.
%Title:-Digit Factorial Chains
%Batch-1 Block-1 ENTC S.Y B.Tech Cycle-2 Sem-4
%MIT Academy Of Engineering.
%Team-Members:
%1) Abhishek Sainkar
%2)Akash Nimbalkar
%3)Omkar Rane
%_______________________________________________________________________%
q=input('Enter largest number Limit for loop length :'); % user defined loop
limit
w=input('Enter Number of terms in chain :');% number of terms to enter for
length
final=0;
for i=69:q
n=i;
x=i;
l=0;
c=0;
flag=0;
a=i
while(n>0)
d=mod(n,10); % return remainder of value n [3].
c=c+factorial(d);% factorial function[8]
n=floor(n./10);% returns quotient of floor [2].
if n<=0
if c==x || length(a)==w
for i=1:w
if (c==a(i))
flag=1;
break
end
end
a1=unique(a);%it returns set of values without repition of anynumber.[9]
if flag==1 && length(a1)==length(a) && length(a)==w
ans12=a;
final=final+1;
end
else
if length(a)>w
break
end
a=[a,c];
n=c;
c=0;
end
end
end
end
ouput=final;
display(final); % displaying number of chains
15. MIT Academy of Engineering, Alandi (D)
S. Y. B. Tech. Sem IV Applied Mathematics Mini Project 15
CHAPTER 5: RESULTS
The Project is tested with various cases, considered different loop length for calculating the number
of chains having sixty non-repeating terms. For the evaluation of project, there is Interactive test
available online on website. To validate result, we used the Interactive test and validated number of
chains by calculating numbers. The output of code in MATLAB and by Interactive test result is
mentioned in table 5.1[1]
Table 5.1 Result Table [1]
Accuracy:
Accuracy Percentage=
𝑻𝒐𝒕𝒂𝒍 𝑵𝒖𝒎𝒃𝒆𝒓 𝒐𝒇 𝒄𝒐𝒓𝒓𝒆𝒄𝒕 𝒄𝒂𝒔𝒆𝒔
𝑻𝒐𝒕𝒂𝒍 𝒏𝒖𝒎𝒃𝒆𝒓 𝒐𝒇 𝑻𝒆𝒕𝒔𝒕𝒆𝒅 𝒄𝒂𝒔𝒆𝒔.
𝑿𝟏𝟎𝟎
Accuracy Percentage=
𝟔
𝟕
𝑿𝟏𝟎𝟎
Accuracy Percentage= 𝟖𝟓. 𝟕𝟏𝟒 %
Sr. No Loop
Length
Terms Number of chains
by Project code
Number of chains
by Interactive test
Correctness
of Code
1 1000000 60 402 402 Correct
2 1000 60 0 -1 Wrong
3 1500 60 2 2 Correct
4 1998 60 6 6 Correct
5 2500 60 6 6 Correct
5 4500 60 10 10 Correct
6 8998 60 30 30 Correct
7 9893 60 42 42 Correct
16. MIT Academy of Engineering, Alandi (D)
S. Y. B. Tech. Sem IV Applied Mathematics Mini Project 16
Case 1: The Project was Validated in Various cases. From 69 to 1000000, the number of chains
containing exactly 60 non-repeating chain is calculated in MATLAB as shown in figure 5.1(a).
Figure 5.1(a) Loop length 1000000, output of code
For 1000000 loop length the output of code validated from online Interactive test showing those
numbers who contain exactly sixty non-repeating terms is shown in figure 5.1(b)
Figure 5.1(b) Result validation by Interactive test for loop length 1000000[1]
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Case 2: For the loop length 1000 number of chains containing 60 non-repeating chains is calculated
in MATLAB as shown in figure 5.2 (a)
Figure 5.6(a) loop length- 1000, output of code
For 1000 loop length the output of code validated from online Interactive test showing those numbers
who contain exactly sixty non-repeating terms is shown in figure 5.2(b)
Figure 5.2(b) Result validation by Interactive test for loop length 1000[1]
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Case 3: For the loop length 1000 number of chains containing 60 non-repeating chains is
calculated in MATLAB as shown in figure 5.3 (a)
Figure 5.7(a) loop length-4500, output of code
For 4500 loop length the output of code validated from online Interactive test showing those numbers
who contain exactly sixty non-repeating terms is shown in figure 5.3(b)
Figure 8 Result validation by Interactive test for loop length 4500 [1]
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Case 4: For the loop length 9893, number of chains containing 60 non-repeating chains is calculated
in MATLAB as shown in figure 5.4 (a)
Figure 9.4(a) Loop length-9893, output of code
For 4500 loop length the output of code validated from online Interactive test showing those numbers
who contain exactly sixty non-repeating terms is shown in figure 5.4(b)
Figure 5.4(b) Result validation by Interactive test for loop length-9893 [1]
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CHAPTER 6: CONCLUSION
Digit factorial chains is mathematical computation based on large numerical factorial and summation
to find out non-repeating chains of numbers below 1 million. It was successfully completed and
implementation of code based on digit factorial sum principle in this coding process we had gone
through various brain storming session to develop logic and algorithm for this code. The problem
statement itself provided us hint that below million there are 60 terms. It is also seen that computation
results in MATLAB will depend upon hardware configuration of computer based upon time of
calculation to give output. There is indirect application of digit factorial chains in Cunningham
number and factorial chains [10]. All kinds of expansion series like sine, cosine and exponential are
applications of factorial and their summations [11]. In digit factorial sum we have learnt new
function for making implementation simpler for complex problems. We came to know various
different functions in MATLAB.
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