Lecture 3 Numerical Analysis. Solution of Non-Linear Equations Chapter 2.

Slides:



Advertisements
Similar presentations
The Rational Zero Theorem
Advertisements

In order to solve this equation, the first item to consider is: “How many solutions are there?” Let’s look at some equations and consider the number of.
Mathematics1 Mathematics 1 Applied Informatics Štefan BEREŽNÝ.
CSE 330: Numerical Methods
Computer Programming (TKK-2144) 13/14 Semester 1 Instructor: Rama Oktavian Office Hr.: M.13-15, W Th , F
4.3 – Location Zeros of Polynomials. At times, finding zeros for certain polynomials may be difficult There are a few rules/properties we can use to help.
Warm Up.
Rational Root Theorem.
Unit-I Roots of Equation
Chapter 4 Roots of Equations
Lectures on Numerical Methods 1 Numerical Methods Charudatt Kadolkar Copyright 2000 © Charudatt Kadolkar.
NUMERICAL METHODS WITH C++ PROGRAMMING
Bracketing Methods Chapter 5 The Islamic University of Gaza
Upper and Lower Bounds for Roots
Zeros of Polynomial Functions
LIAL HORNSBY SCHNEIDER
The Real Zeros of a Polynomial Function
Solution of Polynomial Equations
MATH 175: NUMERICAL ANALYSIS II Lecturer: Jomar Fajardo Rabajante IMSP, UPLB 2 nd Semester AY
The Rational Zero Theorem
The Fundamental Theorem of Algebra And Zeros of Polynomials
Chapter 14: Numerical Methods
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.
Continuity ( Section 1.8) Alex Karassev. Definition A function f is continuous at a number a if Thus, we can use direct substitution to compute the limit.
Chapter 6 Finding the Roots of Equations
Introduction This chapter gives you several methods which can be used to solve complicated equations to given levels of accuracy These are similar to.
Copyright © 2014, 2010 Pearson Education, Inc. Chapter 2 Polynomials and Rational Functions Copyright © 2014, 2010 Pearson Education, Inc.
Zeros of Polynomial Functions Section 2.5 Page 312.
4-5, 4-6 Factor and Remainder Theorems r is an x intercept of the graph of the function If r is a real number that is a zero of a function then x = r.
Lecture 8 Numerical Analysis. Solution of Non-Linear Equations Chapter 2.
Precalculus Complex Zeros V. J. Motto. Introduction We have already seen that an nth-degree polynomial can have at most n real zeros. In the complex number.
Chapter 3 Polynomial and Rational Functions Copyright © 2014, 2010, 2007 Pearson Education, Inc Zeros of Polynomial Functions.
Section 4.3 Zeros of Polynomials. Approximate the Zeros.
Copyright © 2014, 2010, 2006 Pearson Education, Inc. 1 Chapter 3 Quadratic Functions and Equations.
Lecture 6 Numerical Analysis. Solution of Non-Linear Equations Chapter 2.
Chapter 7 Polynomial and Rational Functions with Applications Section 7.2.
7.4 and 7.5 Solving and Zeros of Polynomials
Warm up Write the quadratic f(x) in vertex form..
Lesson 2.5, page 312 Zeros of Polynomial Functions Objective: To find a polynomial with specified zeros, rational zeros, and other zeros, and to use Descartes’
3.4 Zeros of Polynomial Functions. The Fundamental Theorem of Algebra If f(x) is a polynomial of degree n, where n>0, then f has at least one zero in.
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
Numerical Methods.
Copyright © 2009 Pearson Education, Inc. CHAPTER 4: Polynomial and Rational Functions 4.1 Polynomial Functions and Models 4.2 Graphing Polynomial Functions.
Introduction Synthetic division, along with your knowledge of end behavior and turning points, can be used to identify the x-intercepts of a polynomial.
Newton’s Method, Root Finding with MATLAB and Excel
Numerical Methods and Computational Techniques Solution of Transcendental and Polynomial Equations.
Solving Non-Linear Equations (Root Finding)
Numerical Methods Solution of Equation.
Sullivan PreCalculus Section 3.6 Real Zeros of a Polynomial Function Objectives Use the Remainder and Factor Theorems Use Descartes’ Rule of Signs Use.
Sullivan Algebra and Trigonometry: Section 5.2 Objectives Use the Remainder and Factor Theorems Use Descartes’ Rule of Signs Use the Rational Zeros Theorem.
The Rational Zero Theorem The Rational Zero Theorem gives a list of possible rational zeros of a polynomial function. Equivalently, the theorem gives all.
THE FUNDAMENTAL THEOREM OF ALGEBRA. Descartes’ Rule of Signs If f(x) is a polynomial function with real coefficients, then *The number of positive real.
1 © 2010 Pearson Education, Inc. All rights reserved © 2010 Pearson Education, Inc. All rights reserved Chapter 3 Polynomial and Rational Functions.
Chapter 3 Polynomial and Rational Functions Copyright © 2014, 2010, 2007 Pearson Education, Inc Zeros of Polynomial Functions.
SOLVING NONLINEAR EQUATIONS. SECANT METHOD MATH-415 Numerical Analysis 1.
7.5 Roots and Zeros Objectives:
Warm Up  Divide the complex number 3 – 2i 1 + i  Multiply the complex number (3 -2i)(1+i)
LECTURE 2 OF NUMERICAL METHODS 7.2 Solutions of Non-Linear Equations.
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.
Lecture 4 Numerical Analysis. Solution of Non-Linear Equations Chapter 2.
Numerical Analysis Lecture 5.
Numerical Methods and Analysis
Zeros of Polynomial Functions
Copyright © 2014, 2010, 2007 Pearson Education, Inc.
Numerical Analysis Lecture 7.
The Fundamental Theorem of Algebra And Zeros of Polynomials
Continuity Alex Karassev.
Graphical Solutions of Trigonometric Equations
Presentation transcript:

Lecture 3 Numerical Analysis

Solution of Non-Linear Equations Chapter 2

Introduction Bisection Method Regula-Falsi Method Method of iteration Newton - Raphson Method Muller’s Method Graeffe’s Root Squaring Method

Definition: The equation f (x) = 0 is called an algebraic, if it is purely a polynomial in x; it is a transcendental if f (x) contains trigonometric, exponential or logarithmic functions

For example, is an algebraic equation, Whereasand are transcendental equations.

To find the solution of an equation f (x) = 0, we find those values of x for which f (x) = 0 is satisfied. Such values of x are called the roots of f (x) = 0. Thus a is a root of an equation f (x) = 0, if and only if, f (a) = 0.

Properties of an algebraic equation Every algebraic equation of n-th degree, where n is a positive integer, has n and only n roots.

Complex roots occur in pairs. That is, if (a + ib) is a root of f (x) = 0, then (a – ib) is also a root of this equation. If x = a is a root of f (x) = 0, a polynomial of degree n, then (x – a) is a factor of f (x). On dividing f (x) by (x – a) we obtain a polynomial of degree (n – 1).

Descartes rule of signs: The number of positive roots of an algebraic equation f (x) = 0 with real coefficients cannot exceed the number of changes in sign of the coefficients in the polynomial f (x) = 0. Similarly, the number of negative roots of f (x) = 0 cannot exceed the number of changes in the sign of the coefficients of f (-x) = 0.

For example, consider an equation As there are three changes in sign, so, the degree of the equation is three, and hence the given equation will have all the three positive roots.

Intermediate value property: If f (x) is a real valued continuous function in the closed interval If f (a) and f (b) have opposite once; that is f (x) = 0 has at least one root such that

Numerical methods for solving either a transcendental equation or an algebraic equation are classified into two groups: Direct methods, which require no knowledge of the initial approximation of a root of the equation f (x) = 0. Iterative methods, require first approximation to initiate iteration.

How to get the first approximation? We can find the approximate value of the root of f (x) = 0 either by graphical method or by an analytical method

Graphical method The equation f (x) = 0 can be rewritten as f 1 (x) = f 2 (x) and the first approximation to a root of f (x) = 0 can be taken as the abscissa of the point of intersection of the graphs of y = f 1 (x) and y = f 2 (x). For example, consider,

It can be written as x – 1 = sin x. Now, we shall draw the graphs of y =(x -1) and y = sin x

Answer ! The approximate value of the root is found to be 1.9

Analytical method This method is based on ‘intermediate value property’.

Here f (0) and f (1) are of opposite signs. Therefore, using intermediate value property we infer that there is at least one root between x = 0 and x = 1. This method is often used to find the first approximation to a root of either transcendental equation or algebraic equation.

Hence, in analytical method, we must always start with an initial interval (a, b), so that f (a) and f (b) have opposite signs.

Bisection Method (Bolzano)

Suppose, we wish to locate the root of an equation f (x) = 0 in an interval, say (x 0, x 1 ). Let f (x 0 ) and f (x 1 ) are of opposite signs, such that f (x 0 ) f (x 1 ) < 0. Then the graph of the function crosses the x-axis between x 0 and x 1, which guarantees the existence of at least one root in the interval (x 0, x 1 ).

The desired root is approximately defined by the midpoint If f (x 2 ) = 0, then x 2 is the desired root of f (x) = 0. However, if f (x 2 ) 0 then the root may be between x 0 and x 2 or x 2 and x 1.

Now, we define the next approximation by provided f (x 0 ) f (x 2 ) < 0, then the root may be found between x 0 and x 2 or by provided f (x 1 ) f (x 2 ) < 0, then the root lies between x 1 and x 2 etc.

Thus, at each step, we either find the desired root to the required accuracy or narrow the range to half the previous interval. This process of halving the intervals is continued to determine a smaller and smaller interval within which the desired root lies. Continuation of this process eventually gives us the desired root.

Example Solve x 3 – 9x + 1 = 0 for the root between x = 2 and x = 4 by the bisection method.

Solution Given f (x) = x 3 – 9x + 1. Here f (2) = -9, f (4) = 29. Therefore, f (2) f (4) < 0 and hence the root lies between 2 and 4. Let x 0 = 2, x 1 = 4. Now, we define as a first approximation to a root of f (x) = 0 and note that f (3) = 1, so that f (2) f (3) < 0. Thus the root lies between 2 and 3

We further define, and note that f (x 3 ) = f (2.5) < 0, so that f (2.5) f (3) < 0. Therefore, we define the mid-point, Similarly, x 5 = and x 6 = and the process can be continued until the root is obtained to the desired accuracy.

These results are presented in the table. n xnxnxnxn f ( x n )

Lecture 3 Numerical Analysis