Regula-Falsi Method. Type of Algorithm (Equation Solver) The Regula-Falsi Method (sometimes called the False Position Method) is a method used to find.

Slides:



Advertisements
Similar presentations
ROOTS OF EQUATIONS Student Notes ENGR 351 Numerical Methods for Engineers Southern Illinois University Carbondale College of Engineering Dr. L.R. Chevalier.
Advertisements

Bisection Method (Midpoint Method for Equations).
Error Measurement and Iterative Methods
Unit-I Roots of Equation
ECIV 301 Programming & Graphics Numerical Methods for Engineers Lecture 7 Roots of Equations Bracketing Methods.
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.
Second Term 05/061 Roots of Equations Bracketing Methods.
Open Methods (Part 1) Fixed Point Iteration & Newton-Raphson Methods
Roots of Equations Bracketing Methods.
Notes, part 5. L’Hospital Another useful technique for computing limits is L'Hospital's rule: Basic version: If, then provided the latter exists. This.
Lectures on Numerical Methods 1 Numerical Methods Charudatt Kadolkar Copyright 2000 © Charudatt Kadolkar.
Open Methods Chapter 6 The Islamic University of Gaza
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.
NUMERICAL METHODS WITH C++ PROGRAMMING
FP1: Chapter 2 Numerical Solutions of Equations
Fin500J: Mathematical Foundations in Finance Topic 3: Numerical Methods for Solving Non-linear Equations Philip H. Dybvig Reference: Numerical Methods.
Numerical Analysis 1 EE, NCKU Tien-Hao Chang (Darby Chang)
Secant Method Another Recursive Method. Secant Method The secant method is a recursive method used to find the solution to an equation like Newton’s Method.
Scientific Computing Algorithm Convergence and Root Finding Methods.
Solving Non-Linear Equations (Root Finding)
Rolle’s theorem and Mean Value Theorem ( Section 3.2) Alex Karassev.
1 Nonlinear Equations Jyun-Ming Chen. 2 Contents Bisection False Position Newton Quasi-Newton Inverse Interpolation Method Comparison.
Graphics Graphics Korea University kucg.korea.ac.kr 2. Solving Equations of One Variable Korea University Computer Graphics Lab. Lee Seung Ho / Shin.
1 Solution of Nonlinear Equation Dr. Asaf Varol
Newton’s Method Other Recursive Methods Modified Fixed Point Method.
Solving equations numerically The sign - change rule If the function f(x) is continuous for an interval a  x  b of its domain, if f(a) and f(b) have.
Numerical Methods for Engineering MECN 3500
Numerical Methods.
CHAPTER 3 NUMERICAL METHODS
4 Numerical Methods Root Finding Secant Method Modified Secant
Lecture 5 - Single Variable Problems CVEN 302 June 12, 2002.
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.
Note on Information in the Equation of the Tangent Line Knowing the equation of the tangent line to the graph of f(x) at x = a is exactly the same as knowing.
4.1 Extreme Values of Functions
4 Numerical Methods Root Finding Secant Method Modified Secant
Linearization, Newton’s Method
SOLVING NONLINEAR EQUATIONS. SECANT METHOD MATH-415 Numerical Analysis 1.
Solution of Nonlinear Equations ( Root Finding Problems ) Definitions Classification of Methods  Analytical Solutions  Graphical Methods  Numerical.
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.
Modeling of geochemical processes Numeric Mathematics Refreshment
Numerical Solution Of Equations
Solution of Nonlinear Equations ( Root Finding Problems )
Lecture Notes Dr. Rakhmad Arief Siregar Universiti Malaysia Perlis
3.2 Rolle’s Theorem and the
Rolle’s theorem and Mean Value Theorem (Section 4.2)
Numerical Analysis Lecture 5.
Solution of Nonlinear Equations (Root finding Problems
Newton’s Method for Systems of Non Linear Equations
Lecture 4: Numerical Methods
Katherine Han Period Linear Approximation Katherine Han Period
3.2 Rolle’s Theorem and the
Roots of equations Class VII.
Chapter 1: False-Position Method of Solving a Nonlinear Equation
MATH 2140 Numerical Methods
MATH 175: Numerical Analysis II
Intermediate Value Theorem
SOLUTION OF NONLINEAR EQUATIONS
Intermediate Value Theorem
Roots: Bracketing Methods
ROOTS OF EQUATIONS.
Roots: Bracketing Methods
Copyright © Cengage Learning. All rights reserved.
MATH 1910 Chapter 3 Section 8 Newton’s Method.
Presentation transcript:

Regula-Falsi Method

Type of Algorithm (Equation Solver) The Regula-Falsi Method (sometimes called the False Position Method) is a method used to find a numerical estimate of an equation. This method attempts to solve an equation of the form f(x)=0. (This is very common in most numerical analysis applications.) Any equation can be written in this form. Algorithm Requirements This algorithm requires a function f(x) and two points a and b for which f(x) is positive for one of the values and negative for the other. We can write this condition as f(a)  f(b)< 0. If the function f(x) is continuous on the interval [ a,b ] with f ( a )  f ( b )<0, the algorithm will eventually converge to a solution. This algorithm can not be implemented to find a tangential root. That is a root that is tangent to the x -axis and either positive or negative on both side of the root. For example f(x)=(x-3) 2, has a tangential root at x =3.

Regula-Falsi Algorithm The idea for the Regula-Falsi method is to connect the points (a,f(a)) and ( b,f(b )) with a straight line. Since linear equations are the simplest equations to solve for find the regula- falsi point ( x rfp ) which is the solution to the linear equation connecting the endpoints. Look at the sign of f(x rfp ): If sign ( f(x rfp ) ) = 0 then end algorithm else If sign ( f ( x rfp )) = sign ( f(a)) then set a = x rfp else set b = x rfp x -axis ab f(b) f(a) actual root f(x) x rfp equation of line: solving for x rfp

Example Lets look for a solution to the equation x 3 -2 x -3=0. We consider the function f(x)=x 3 -2 x -3 On the interval [0,2] the function is negative at 0 and positive at 2. This means that a =0 and b =2 (i.e. f(0)f(2)=(-3)(1)=-3<0, this means we can apply the algorithm). This is negative and we will make the a =3/2 and b is the same and apply the same thing to the interval [3/2,2]. This is negative and we will make the a =54/29 and b is the same and apply the same thing to the interval [54/29,2].

Stopping Conditions Aside from lucking out and actually hitting the root, the stopping condition is usually fixed to be a certain number of iterations or for the Standard Cauchy Error in computing the Regula-Falsi Point ( x rfp ) to not change more than a prescribed amount (usually denoted  ).