Summary with Examples for Root finding Methods -Bisection -Newton Raphson -Secant

Similar documents
Root Finding

Lecture 2 Solution of Nonlinear Equations ( Root Finding Problems )

CHAPTER 4d. ROOTS OF EQUATIONS

: Numerical Analysis Topic 2: Solution of Nonlinear Equations Lectures 5-11:

CISE301: Numerical Methods Topic 2: Solution of Nonlinear Equations

Single Variable Optimization

Numerical Methods Solution of Nonlinear Equations

Chapter Newton s Method

Review of Taylor Series. Read Section 1.2

Solution of Linear System of Equations and Matrix Inversion Gauss Seidel Iteration Method

Chapter 3 Differentiation and Integration

OPTIMISATION. Introduction Single Variable Unconstrained Optimisation Multivariable Unconstrained Optimisation Linear Programming

Shuai Dong. Isaac Newton. Gottfried Leibniz

Chapter Newton-Raphson Method of Solving a Nonlinear Equation

36.1 Why is it important to be able to find roots to systems of equations? Up to this point, we have discussed how to find the solution to

Chapter 4: Root Finding

Physics 2A Chapter 3 HW Solutions

Chapter 12. Ordinary Differential Equation Boundary Value (BV) Problems

Lecture 21: Numerical methods for pricing American type derivatives

Math1110 (Spring 2009) Prelim 3 - Solutions

Newton s Method for One - Dimensional Optimization - Theory

Topic 5: Non-Linear Regression

ORDINARY DIFFERENTIAL EQUATIONS EULER S METHOD

6.1 The function can be formulated as a fixed-point iteration as

Transfer Functions. Convenient representation of a linear, dynamic model. A transfer function (TF) relates one input and one output: ( ) system

ME 501A Seminar in Engineering Analysis Page 1

Week3, Chapter 4. Position and Displacement. Motion in Two Dimensions. Instantaneous Velocity. Average Velocity

2. PROBLEM STATEMENT AND SOLUTION STRATEGIES. L q. Suppose that we have a structure with known geometry (b, h, and L) and material properties (EA).

Week 11: Chapter 11. The Vector Product. The Vector Product Defined. The Vector Product and Torque. More About the Vector Product

Chapter Newton-Raphson Method of Solving a Nonlinear Equation

A new Approach for Solving Linear Ordinary Differential Equations

Finite Difference Method

For all questions, answer choice E) NOTA" means none of the above answers is correct.

Lecture 26 Finite Differences and Boundary Value Problems

1 GSW Iterative Techniques for y = Ax

Chapter 7: Conservation of Energy

1 Matrix representations of canonical matrices

So far: simple (planar) geometries

PHYS 1443 Section 004 Lecture #12 Thursday, Oct. 2, 2014

CHAPTER 5 NUMERICAL EVALUATION OF DYNAMIC RESPONSE

CIS526: Machine Learning Lecture 3 (Sept 16, 2003) Linear Regression. Preparation help: Xiaoying Huang. x 1 θ 1 output... θ M x M

An influence line shows how the force in a particular member changes as a concentrated load is moved along the structure.

ACTM State Calculus Competition Saturday April 30, 2011

Spring Force and Power

EEE 241: Linear Systems

Mathematical Economics MEMF e ME. Filomena Garcia. Topic 2 Calculus

Section 8.1 Exercises

Practical Newton s Method

Four Bar Linkages in Two Dimensions. A link has fixed length and is joined to other links and also possibly to a fixed point.

Physics 2A Chapter 9 HW Solutions

Modeling curves. Graphs: y = ax+b, y = sin(x) Implicit ax + by + c = 0, x 2 +y 2 =r 2 Parametric:

NUMERICAL DIFFERENTIATION

(c) (cos θ + i sin θ) 5 = cos 5 θ + 5 cos 4 θ (i sin θ) + 10 cos 3 θ(i sin θ) cos 2 θ(i sin θ) 3 + 5cos θ (i sin θ) 4 + (i sin θ) 5 (A1)

Chapter 3. r r. Position, Velocity, and Acceleration Revisited

Implicit Integration Henyey Method

Chapter 10. Numerical Solution Methods for Engineering Analysis

y i x P vap 10 A T SOLUTION TO HOMEWORK #7 #Problem

Mathematics Intersection of Lines

Period & Frequency. Work and Energy. Methods of Energy Transfer: Energy. Work-KE Theorem 3/4/16. Ranking: Which has the greatest kinetic energy?

Iterative General Dynamic Model for Serial-Link Manipulators

2.29 Numerical Fluid Mechanics

Anouncements. Multigrid Solvers. Multigrid Solvers. Multigrid Solvers. Multigrid Solvers. Multigrid Solvers

SOLVING NON-LINEAR SYSTEMS BY NEWTON s METHOD USING SPREADSHEET EXCEL Tay Kim Gaik Universiti Tun Hussein Onn Malaysia

One-sided finite-difference approximations suitable for use with Richardson extrapolation

CHAPTER 7 CONSTRAINED OPTIMIZATION 2: SQP AND GRG

Indeterminate pin-jointed frames (trusses)

Physics 5153 Classical Mechanics. Principle of Virtual Work-1

Definition. Measures of Dispersion. Measures of Dispersion. Definition. The Range. Measures of Dispersion 3/24/2014

Chapter 8. Potential Energy and Conservation of Energy

A MODIFIED METHOD FOR SOLVING SYSTEM OF NONLINEAR EQUATIONS

Solution Thermodynamics

Introduction to Vapor/Liquid Equilibrium, part 2. Raoult s Law:

EE 330 Lecture 24. Small Signal Analysis Small Signal Analysis of BJT Amplifier

A Tale of Friction Basic Rollercoaster Physics. Fahrenheit Rollercoaster, Hershey, PA max height = 121 ft max speed = 58 mph

University Physics AI No. 10 The First Law of Thermodynamics

Foldy-Wouthuysen Transformation with Dirac Matrices in Chiral Representation. V.P.Neznamov RFNC-VNIIEF, , Sarov, Nizhniy Novgorod region

E91: Dynamics. E91: Dynamics. Numerical Integration & State Space Representation

Complex Variables. Chapter 18 Integration in the Complex Plane. March 12, 2013 Lecturer: Shih-Yuan Chen

1. Estimation, Approximation and Errors Percentages Polynomials and Formulas Identities and Factorization 52

Common loop optimizations. Example to improve locality. Why Dependence Analysis. Data Dependence in Loops. Goal is to find best schedule:

WINTER 2017 EXAMINATION

Exercise Solutions to Real Analysis

( ) [ ( k) ( k) ( x) ( ) ( ) ( ) [ ] ξ [ ] [ ] [ ] ( )( ) i ( ) ( )( ) 2! ( ) = ( ) 3 Interpolation. Polynomial Approximation.

Name: PHYS 110 Dr. McGovern Spring 2018 Exam 1. Multiple Choice: Circle the answer that best evaluates the statement or completes the statement.

Factoring Using Shor's Quantum Algorithm

Numerical Transient Heat Conduction Experiment

RELIABILITY ASSESSMENT

Lecture 10 Support Vector Machines II

ANALYSIS OF SOURCE LOCATION ALGORITHMS Part II: Iterative methods

PHY2049 Exam 2 solutions Fall 2016 Solution:

Please initial the statement below to show that you have read it

ECEN 5005 Crystals, Nanocrystals and Device Applications Class 19 Group Theory For Crystals

PHYSICS 203-NYA-05 MECHANICS

= 1.23 m/s 2 [W] Required: t. Solution:!t = = 17 m/s [W]! m/s [W] (two extra digits carried) = 2.1 m/s [W]

One Dimensional Axial Deformations

Work is the change in energy of a system (neglecting heat transfer). To examine what could

Calculation of time complexity (3%)

APPENDIX 2 FITTING A STRAIGHT LINE TO OBSERVATIONS

SENSITIVITY APPROACH TO OPTIMAL CONTROL FOR AFFINE NONLINEAR DISCRETE-TIME SYSTEMS

TIME OF COMPLETION NAME SOLUTION DEPARTMENT OF NATURAL SCIENCES. PHYS 2211, Exam 2 Section 1 Version 1 October 18, 2013 Total Weight: 100 points

Transcription:

Summary wth Eamples or Root ndng Methods -Bsecton -Newton Raphson -Secant

Nonlnear Equaton Solvers Bracketng Graphcal Open Methods Bsecton False Poston (Regula-Fals) Newton Raphson Secant All Iteratve

Bracketng Methods (Or, two pont methods or ndng roots) Two ntal guesses or the root are requred. These guesses must bracket or be on ether sde o the root. == > Fgure I one root o a real and contnuous uncton, ()=0, s bounded by values = l, = u then ( l ). ( u ) <0. (The uncton changes sgn on opposte sdes o the root)

Bass o Bsecton Method Theorem An equaton ()=0, where () s a real contnuous uncton, has at least one root between l and u ( l ) ( u ) < 0. () u At least one root ests between the two ponts the uncton s real, contnuous, and changes sgn. 4

Algorthm or Bsecton Method Step Choose and u as two guesses or the root such that ( ) ( u ) < 0, or n other words, () changes sgn between and u. Ths was demonstrated n Fgure. () u 5

Step Estmate the root, m o the equaton () = 0 as the md pont between and u as () m = u m u Estmate o m 6

Step Now check the ollowng 0 a) I l m, then the root les between and m ; then = ; u = m. 0 b) I l m, then the root les between m and u ; then = m ; u = u. 0 c) I l m ; then the root s m. Stop the algorthm ths s true. 7

Step 4 Fnd the new estmate o the root m = u Fnd the absolute relatve appromate error where a old m m new new m old m 00 prevousestmateo root new m current estmateo root 8

Step 5 Compare the absolute relatve appromate error error tolerance. s a wth the pre-speced Is a s? Yes No Go to Step usng new upper and lower guesses. Stop the algorthm Note one should also check whether the number o teratons s more than the mamum number o teratons allowed. I so, one needs to termnate the algorthm and noty the user about t. 9

Eample In the dagram shown the loatng ball has a specc gravty o 0.6 and has a radus o 5.5 cm. You are asked to nd the depth to whch the ball s submerged when loatng n water. Dagram o the loatng ball 0

Eample Cont. The equaton that gves the depth to whch the ball s submerged under water s gven by 0.65.990 4 0 a) Use the bsecton method o ndng roots o equatons to nd the depth to whch the ball s submerged under water. Conduct three teratons to estmate the root o the above equaton. b) Fnd the absolute relatve appromate error at the end o each teraton, and the number o sgncant dgts at least correct at the end o each teraton.

Eample Cont. From the physcs o the problem, the ball would be submerged between = 0 and = R, that s where R = radus o the ball, 0 0 0 R 0.055 0. Dagram o the loatng ball

Eample Cont. Soluton To ad n the understandng o how ths method works to nd the root o an equaton, the graph o () s shown to the rght, where -4 065. 99. 0 Graph o the uncton ()

Eample Cont. Let us assume u 0.00 0. Check the uncton changes sgn between and u. Hence 4 4 l 0 0 0.650.990.990 4 4 0. 0. 0.650..990.660 u 4 4 0 0..990.660 0 l u So there s at least on root between and u, that s between 0 and 0. 4

Eample Cont. Graph demonstratng sgn change between ntal lmts 5

Eample Cont. Iteraton The estmate o the root s m 0.055 0.055 0.650.055 m u 0 0..990 0.055 4 5 0 0.055.990 6.6550 0 l m 4 6.6550 5 Hence the root s bracketed between m and u, that s, between 0.055 and 0.. So, the lower and upper lmts o the new bracket are l 0.055, 0. u At ths pont, the absolute relatve appromate error calculated as we do not have a prevous appromaton. a cannot be 6

Eample Cont. Estmate o the root or Iteraton 7

Eample Cont. m 0.085 0.085 0.650.085 m u 0.055 0. 0.085.990 4 5 0.055 (0.085).6 0 6.655 0 0 l Iteraton The estmate o the root s m 4.6 0 4 Hence the root s bracketed between and m, that s, between 0.055 and 0.085. So, the lower and upper lmts o the new bracket are l 0.055, 0.085 u 8

Eample Cont. Estmate o the root or Iteraton 9

Eample Cont. The absolute relatve appromate error a at the end o Iteraton s a new m new m old m 00 0.085 0.055 0.085.% 00 None o the sgncant dgts are at least correct n the estmate root o m = 0.085 because the absolute relatve appromate error s greater than 5%. 0

Eample Cont. m 0.06875 0.06875 0.650.06875 m u 0.055 0.085.990 5 5 0.055 0.06875 6.6550 5.560 0 l Iteraton The estmate o the root s m 4 0.06875 5.560 5 Hence the root s bracketed between and m, that s, between 0.055 and 0.06875. So, the lower and upper lmts o the new bracket are l 0.055, 0.06875 u

Eample Cont. Estmate o the root or Iteraton

Eample Cont. The absolute relatve appromate error a at the end o Iteraton s a new m new m old m 00 0.06875 0.085 0.06875 0% 00 Stll none o the sgncant dgts are at least correct n the estmated root o the equaton as the absolute relatve appromate error s greater than 5%. Seven more teratons were conducted and these teratons are shown n Table.

Table Cont. Table Root o ()=0 as uncton o number o teratons or bsecton method. Iteraton u m a % ( m ) 0.00000 0. 0.055 ---------- 6.655 0 5 0.055 0. 0.085..6 0 4 0.055 0.085 0.06875 0.00 5.56 0 5 4 0.055 0.06875 0.0688. 4.484 0 6 5 0.0688 0.06875 0.065 5.6.59 0 5 6 0.0688 0.065 0.0659.70.0804 0 5 7 0.0688 0.0659 0.067.70.76 0 6 8 0.0688 0.067 0.06 0.6897 6.497 0 7 9 0.06 0.067 0.065 0.46.65 0 6 0 0.06 0.065 0.064 0.7.0768 0 7 4

5 Most wdely used method. Based on Taylor seres epanson: ) ( ) ( ) ( 0 Rearrangn g, 0 ) when ( the value o The root s! ) ( ) ( ) ( ) ( ) ( ) ( O Newton-Raphson ormula Solve or Newton-Raphson Method

A convenent method or unctons whose dervatves can be evaluated analytcally. It may not be convenent or unctons whose dervatves cannot be evaluated analytcally. 6

Newton-Raphson Method () ( ), = - ( ) ( ) ( - ) + + X 7 Geometrcal llustraton o the Newton-Raphson method. http://numercalmethods.eng.us.edu

Dervaton () ( ) B tan( AB AC '( ) ( ) C + A X ( ) ( ) Dervaton o the Newton-Raphson method. http://numercalmethods.eng.us.edu 8

Algorthm or Newton - Raphson Method Step Evaluate () symbolcally. Step Use an ntal guess o the root,, as = -, to estmate the new value o the root, Step Fnd the absolute relatve appromate error a as - a = 00 9 http://numercalmethods.eng.us.edu

Step 4 Compare the absolute relatve appromate error wth the pre-speced relatve error tolerance. s Is a s? Yes No Go to Step usng new estmate o the root. Stop the algorthm Also, check the number o teratons has eceeded the mamum number o teratons allowed. I so, one needs to termnate the algorthm and noty the user. 0 http://numercalmethods.eng.us.edu

Step Eample ' Solve Eample usng Newton-Raphson Methods. Solve or ' -065. -0. + 99. 0 Let us assume the ntal guess o the root o s. Ths s a reasonable guess! and 0 0.m are not good choces) as the etreme values o the depth would be 0 and the dameter (0. m) o the ball. -4 0 0.05 0 m

Eample Cont. Step Iteraton The estmate o the root s 0 0.05 0 ' 0 0.05 0.650.05 0.05 0.0.05.80 0.05 90 0.05 0.04 0.064 4.990 4

Eample Cont. Estmate o the root or the rst teraton.

Eample Cont. Step The absolute relatve appromate error a at the end o Iteraton s a 0 00 0.064 0.05 0.064 9.90% 00 The number o sgncant dgts at least correct s 0, as you need an absolute relatve appromate error o 5% or less or at least one sgncant dgts to be correct n your result. 4

Eample Cont. Iteraton The estmate o the root s ' 0.068 0.064 0.650.064 0.064 0.0.064 0.064 0.064 0.064.97780 8.90970 5 4.46460 7.990 4 5

Eample Cont. Estmate o the root or the Iteraton. 6

Eample Cont. The absolute relatve appromate error a at the end o Iteraton s a 00 0.068 0.064 0.068 0.076% 00 m The mamum value o m or whch a 0.50 s.844. Hence, the number o sgncant dgts at least correct n the answer s. 7

Eample Cont. Iteraton The estmate o the root s ' 0.068 0.068 0.068 0.068 0.650.068 0.068 0.0.068 0.068 4.440 8.970 4.980 9.990 4 8

Eample Cont. Estmate o the root or the Iteraton. 9

Eample Cont. The absolute relatve appromate error a at the end o Iteraton s a 00 0.068 0.068 0.068 00 0% The number o sgncant dgts at least correct s 4, as only 4 sgncant dgts are carred through all the calculatons. 40

Secant Method-Dervaton () ( ) ( - ) + + Geometrcal llustraton o Newton-Raphson method., the X Newton s Method ( ) = - ( ) ( ) ( ) ( ( ( )( ) ) ( () Appromate the dervatve () Substtutng Equaton () nto Equaton () gves the Secant method ) ) 4

Secant Method Dervaton The secant method can also be derved rom geometry: () ( ) ( - ) C E D A + - B X The Geometrc Smlar Trangles AB DC AE DE can be wrtten as ( ) ( ) On rearrangng, the secant method s gven as Geometrcal representaton o Secant method. the ( ( )( ) ( ) ) 4

4 Step 00 - = a Calculate the net estmate o the root rom two ntal guesses Fnd the absolute relatve appromate error ) ( ) ( ) )( ( Algorthm or Secant Method

Step Fnd the absolute relatve appromate error s greater than the prespeced relatve error tolerance. I so, go back to step, else stop the algorthm. Also check the number o teratons has eceeded the mamum number o teratons. 44

Eample In Eample, the equaton that gves the depth to whch the ball s submerged under water s gven by -4-065. + 99. 0 Use the Secant method o ndng roots o equatons to nd the depth to whch the ball s submerged under water. Conduct three teratons to estmate the root o the above equaton. Fnd the absolute relatve appromate error and the number o sgncant dgts at least correct at the end o each teraton. 45

Eample Cont. Step Let us assume the ntal guesses o the root o as 0.0and 0.05. 0 0 0 Iteraton The estmate o the root s 0.0646 0 0 0 4 0.05 0.650.05.990 0.05 0.0 4 0.05 0.650.05.990 0.0 0.65 0.0 0.05.990 4 46

Eample Cont. Step The absolute relatve appromate error Iteraton s a 0 00 0.0646 0.05 0.0646.6% 00 a at the end o The number o sgncant dgts at least correct s 0, as you need an absolute relatve appromate error o 5% or less or one sgncant dgts to be correct n your result. 47

Eample Cont. Graph o results o Iteraton. 48

Eample Cont. Step Iteraton The estmate o the root s 0.064 0 0 4 0.0646 0.650.0646.990 0.0646 0.05 4 0.650.0646.990 0.05 0.65 0.05 0.0646 0.0646.990 4 49

Eample Cont. The absolute relatve appromate error Iteraton s a 00 0.064 0.0646 0.064.55% 00 a at the end o The number o sgncant dgts at least correct s, as you need an absolute relatve appromate error o 5% or less. 50

Eample Cont. Graph o results o Iteraton. 5

Eample Cont. Iteraton The estmate o the root s 0.068 4 0.064 0.650.064.990 0.064 0.0646 4 0.650.064.990 0.05 0.65 0.0646 0.064 0.064.990 4 5

Eample Cont. The absolute relatve appromate error Iteraton s a 00 0.068 0.064 0.068 0.0595% 00 a at the end o The number o sgncant dgts at least correct s 5, as you need an absolute relatve appromate error o 0.5% or less. 5