MATH 2140 Numerical Methods

Slides:



Advertisements
Similar presentations
Lecture 5 Newton-Raphson Method
Advertisements

CSE 330: Numerical Methods
Chapter 7 Numerical Differentiation and Integration
Chapter 4 Roots of Equations
Copyright © 2006 The McGraw-Hill Companies, Inc. Permission required for reproduction or display. by Lale Yurttas, Texas A&M University Chapter 51.
ECIV 301 Programming & Graphics Numerical Methods for Engineers Lecture 6 Roots of Equations Bracketing Methods.
Chapter 1 Introduction The solutions of engineering problems can be obtained using analytical methods or numerical methods. Analytical differentiation.
ECIV 301 Programming & Graphics Numerical Methods for Engineers Lecture 20 Solution of Linear System of Equations - Iterative Methods.
ECIV 301 Programming & Graphics Numerical Methods for Engineers Lecture 19 Solution of Linear System of Equations - Iterative Methods.
Revision.
Lectures on Numerical Methods 1 Numerical Methods Charudatt Kadolkar Copyright 2000 © Charudatt Kadolkar.
ECIV 301 Programming & Graphics Numerical Methods for Engineers REVIEW II.
Dr. Marco A. Arocha Aug,  “Roots” problems occur when some function f can be written in terms of one or more dependent variables x, where the.
Fin500J: Mathematical Foundations in Finance Topic 3: Numerical Methods for Solving Non-linear Equations Philip H. Dybvig Reference: Numerical Methods.
Chapter 3 Root Finding.
CISE-301: Numerical Methods Topic 1: Introduction to Numerical Methods and Taylor Series Lectures 1-4: KFUPM.
Professor Walter W. Olson Department of Mechanical, Industrial and Manufacturing Engineering University of Toledo Solving ODE.
MECN 3500 Inter - Bayamon Lecture Numerical Methods for Engineering MECN 3500 Professor: Dr. Omar E. Meza Castillo
Solving Non-Linear Equations (Root Finding)
Copyright © 2006 The McGraw-Hill Companies, Inc. Permission required for reproduction or display. 1 Part 2 Roots of Equations Why? But.
Chapter 6 Finding the Roots of Equations
CISE-301: Numerical Methods Topic 1: Introduction to Numerical Methods and Taylor Series Lectures 1-4: KFUPM CISE301_Topic1.
CISE301_Topic11 CISE-301: Numerical Methods Topic 1: Introduction to Numerical Methods and Taylor Series Lectures 1-4:
EE 3561_Unit_1(c)Al-Dhaifallah EE 3561 : - Computational Methods in Electrical Engineering Unit 1: Introduction to Computational Methods and Taylor.
Lecture Notes Dr. Rakhmad Arief Siregar Universiti Malaysia Perlis
Numerical Methods Applications of Loops: The power of MATLAB Mathematics + Coding 1.
Introduction to Numerical Analysis I MATH/CMPSC 455 Fall 2011 Instructor: Xiaozhe Hu (Shawn)
Application of Differential Applied Optimization Problems.
CSE 541 Rick Parent ELEMENTARY NUMERICAL METHODS Winter 2012.
Applied Numerical Method for Engineers and Scientists
Chapter 3 Roots of Equations. Objectives Understanding what roots problems are and where they occur in engineering and science Knowing how to determine.
Numerical Methods for Engineering MECN 3500
The Islamic University of Gaza Faculty of Engineering Civil Engineering Department Numerical Analysis ECIV 3306 Introduction Course Outline.
Numerical Methods.
CHAPTER 3 NUMERICAL METHODS
Newton’s Method, Root Finding with MATLAB and Excel
MECN 3500 Inter - Bayamon Lecture 6 Numerical Methods for Engineering MECN 3500 Professor: Dr. Omar E. Meza Castillo
BE207 Numerical Analysis using Matlab Lecturer DR Abdullah Awad Faculty of Engineering.
Lecture 5 - Single Variable Problems CVEN 302 June 12, 2002.
The Islamic University of Gaza Faculty of Engineering Civil Engineering Department Numerical Analysis ECIV 3306 Chapter 5 Bracketing Methods.
Solving Non-Linear Equations (Root Finding)
Numerical Methods Solution of Equation.
Today’s class Numerical differentiation Roots of equation Bracketing methods Numerical Methods, Lecture 4 1 Prof. Jinbo Bi CSE, UConn.
SOLVING NONLINEAR EQUATIONS. SECANT METHOD MATH-415 Numerical Analysis 1.
CSE 330: Numerical Methods. What is true error? True error is the difference between the true value (also called the exact value) and the approximate.
1 M 277 (60 h) Mathematics for Computer Sciences Bibliography  Discrete Mathematics and its applications, Kenneth H. Rosen  Numerical Analysis, Richard.
NUMERICAL ANALYSIS I. Introduction Numerical analysis is concerned with the process by which mathematical problems are solved by the operations.
Copyright © The McGraw-Hill Companies, Inc. Permission required for reproduction or display. 1 Part 2 / Chapter 5.
Numerical Integration
Lecture Notes Dr. Rakhmad Arief Siregar Universiti Malaysia Perlis
CHAPTER 3 NUMERICAL METHODS
NUMERICAL DIFFERENTIATION Forward Difference Formula
Bracketing Methods (Bisection Method)
Numerical Methods and Analysis
Read Chapters 5 and 6 of the textbook
MATH 2140 Numerical Methods
Numerical Analysis Lecture 45.
Chapter 10. Numerical Solutions of Nonlinear Systems of Equations
MATH 2140 Numerical Methods
MATH 2140 Numerical Methods
SOLUTION OF NONLINEAR EQUATIONS
MATH-321 In One Slide MATH-321 & MATLAB Command.
SKTN 2393 Numerical Methods for Nuclear Engineers
Copyright © Cengage Learning. All rights reserved.
MATH 1910 Chapter 3 Section 8 Newton’s Method.
1 Newton’s Method.
MATH 2140 Numerical Methods
CISE-301: Numerical Methods Topic 1: Introduction to Numerical Methods and Taylor Series Lectures 1-4: KFUPM CISE301_Topic1.
Presentation transcript:

MATH 2140 Numerical Methods Faculty of Engineering Mechanical Engineering Department MATH 2140 Numerical Methods Instructor: Dr. Phillips Agboola

1.Topics to be covered Topics No of Weeks Contacthours Number Representation and base Number, Error,Sources of Errors 1 3 Nonlinear equations, Simple and Multiple roots of nonlinear equations, numerical methods for simple root (bisection, fixed-point, Newton’s, scant) and multiple (modified) root 2 6 Convergence of iterative methods for nonlinear equations, System of nonlinear equations (Newton’s method) Linear systems, special matrices, direct methods (Gauss-elimination and its variants), LU decomposition) for linear system, norms, iterative methods (Jacobi and Gauss-Seidel), error in linear systems Approximating functions, polynomial interpolation (Lagrange and Newton’s divided differences) formulas, error approximations Numerical differentiations, approximation of first derivative of a function using numerical formulas (two-point and three-point), approximating second derivative of a function using three point formula Numerical integration, using closed Newton’s cotes formula (Trapezoidal and Simpson’s rules) Solution of ordinary differential equations by Taylorand Runge-Kutta method of order 2 Total number of weeks and contact hours per semester 14 42 11/19/2018 Dr. Mohamed Elshazly

Proportio n of Final Assessme nt 5. Schedule of Assessment Tasks for Students During the Semester Assessment Assessment task (eg. essay, test, group project, examination etc.) Week due Proportio n of Final Assessme nt 1 Homework Every Week 10% 2 Quizzes Alternatin g 20% 3 Midterm Exam After 7th week 25% 4 Class Participation 5% 5 Final Exam End of Semester 40% 6 11/19/2018 Dr. Mohamed Elshazly

1. Required Text(s) Numerical Analysis By R.L.Burden, J.D.Faires (7thedition), Thomos Learning 11/19/2018 Dr. Mohamed Elshazly

Introduction 1.1 BACKGROUND Numerical methods are mathematical techniques used for solving mathematical problems that cannot be solved or are difficult to solve analytically. An analytical solution is an exact answer in the form of a mathematical expression in terms of the variables associated with the problem that is being solved. A numerical solution is an approximate numerical value (a number) for the solution. Although numerical solutions are an approximation, they can be very accurate. In many numerical methods, the calculations are executed in an iterative manner until a desired accuracy is achieved. 11/19/2018 Dr. Mohamed Elshazly

For example, Fig. 1-1 shows a block of mass m being pulled by a force F applied at an angle θ By applying equations of equilibrium, the relationship between the force and the angle is given by: 11/19/2018 Dr. Mohamed Elshazly

Solving Nonlinear Equations 3.1 BACKGROUND Equations need to be solved in all areas of science and engineering. An equation of one variable can be written in the form: A solution to the equation (also called a root of the equation) is a numerical value of x that satisfies the equation. Graphically, as shown in Fig. 3-1, the solution is the point where the function f(x) crosses or touches the x-axis. An equation might have no solution or can have one or several (possibly many) roots. 11/19/2018 Dr. Mohamed Elshazly

To determine the angle θ if As and r are given, Eq. (3.2) has to be For example, the area of a segment As of a circle with radius r (shaded area in Fig. 3-2) is given by: To determine the angle θ if As and r are given, Eq. (3.2) has to be solved for θ. Obviously, θ cannot be written explicitly in terms of As and r, and the equation cannot be solved analytically. 11/19/2018 Dr. Mohamed Elshazly

A numerical solution of an equation f(x) = 0 is a value of x that satisfies the equation approximately. This means that when x is substituted in the equation, the value of f(x) is close to zero, but not exactly zero. For example, to determine the angle θ for a circle with r = 3 m and As = 8 m2, Eq. (3.2) can be written in the form: 11/19/2018 Dr. Mohamed Elshazly

A plot of f(θ) (Fig. 3-3) shows that the solution is between 2 and 3. Substituting θ = 2.4 rad in Eq. (3.3) gives f(θ) = 0.2396, and the solution θ= 2.43 rad gives f(θ) = 0.003683. Obviously, the latter is a more accurate, but not an exact, solution. It is possible to determine values of θ that give values of f(θ) that are closer to zero, but it is impossible to determine a numerical value of e for which f(θ) is exactly zero. When solving an equation numerically, one has to select the desired accuracy of the solution. 11/19/2018 Dr. Mohamed Elshazly

3.3 BISECTION METHOD The bisection method is a bracketing method for finding a numerical solution of an equation of the form f(x) = 0 when it is known that within a given interval [a, b], f(x) is continuous and the equation has a solution. When this is the case, f(x) will have opposite signs at the endpoints of the interval. As shown in Fig. 3-6, if f(x) is continuous and has a solution between the points x = a and x = b , then either f(a) > 0 and f(b) < 0 or f(a) < 0 and f(b) > 0. In other words, if there is a solution between x =a and x = b, then f(a)f(b) < 0 11/19/2018 Dr. Mohamed Elshazly

11/19/2018 Dr. Mohamed Elshazly

Algorithm for the bisection method 1. Choose the first interval by finding points a and b such that a solution exists between them. This means that f(a) and f(b) have different signs such that f(a)f(b) < 0. The points can be determined by examining the plot of f(x) versus x. 2. Calculate the first estimate of the numerical solution xNs1 by: 3. Determine whether the true solution is between a and xNS1, or between xNs1 and b. This is done by checking the sign of the product f(a) · f(xNsi) : If f(a) · f(xNsi) < 0, the true solution is between a and xNsi· If f(a) · f(xNsi) > 0, the true solution is between xNsi and b. 4. Select the subinterval that contains the true solution (a to x NSI, or xNsi to b) as the new interval [a, b], and go back to step 2. Steps 2 through 4 are repeated until a specified tolerance or error bound is attained. 11/19/2018 Dr. Mohamed Elshazly

11/19/2018 Dr. Mohamed Elshazly

SOLUTION To find the approximate location of the solution, a plot of the function: f(x) = 8-4.5(x- sinx) is made by using the fplot command of MATLAB. The plot (Fig. 3-8), shows that the solution is between x = 2 and x = 3. The initial interval is chosen as a = 2 and b = 3 . 11/19/2018 Dr. Mohamed Elshazly

11/19/2018 Dr. Mohamed Elshazly

11/19/2018 Example-1 Find the root of: CISE301_Topic2

11/19/2018 Example-1 Iteration a b c= (a+b) 2 f(c) (b-a) 1 0.5 -0.375 0.25 0.266 3 .375 -7.23E-3 0.125 4 0.375 0.3125 9.30E-2 0.0625 5 0.34375 9.37E-3 0.03125 CISE301_Topic2

EXAMPLE-2

EXAMPLE -3

EXAMPLE-3