Nonlinearity Root-finding Bisection Fixed Point Iteration Newton s Method Secant Method Conclusion. Nonlinear Systems

Size: px
Start display at page:

Download "Nonlinearity Root-finding Bisection Fixed Point Iteration Newton s Method Secant Method Conclusion. Nonlinear Systems"

Transcription

1 Nonlinear Systems CS 205A: Mathematical Methods for Robotics, Vision, and Graphics Justin Solomon CS 205A: Mathematical Methods Nonlinear Systems 1 / 24

2 Part III: Nonlinear Problems Not all numerical problems can be solved with \ in Matlab. CS 205A: Mathematical Methods Nonlinear Systems 2 / 24

3 Question Have we already seen a nonlinear problem? CS 205A: Mathematical Methods Nonlinear Systems 3 / 24

4 Question Have we already seen a nonlinear problem? minimize A x 2 such that x 2 = 1 nonlinear! CS 205A: Mathematical Methods Nonlinear Systems 3 / 24

5 Root-Finding Problem Given: f : R n R m Find: x with f( x ) = 0 CS 205A: Mathematical Methods Nonlinear Systems 4 / 24

6 Issue: Regularizing Assumptions f(x) = { 1 x 0 1 x > 0 CS 205A: Mathematical Methods Nonlinear Systems 5 / 24

7 Issue: Regularizing Assumptions f(x) = { 1 x 0 1 x > 0 f(x) = { 1 x Q 1 otherwise CS 205A: Mathematical Methods Nonlinear Systems 5 / 24

8 Typical Regularizing Assumptions Continuous f( x) f( y) as x y CS 205A: Mathematical Methods Nonlinear Systems 6 / 24

9 Typical Regularizing Assumptions Continuous f( x) f( y) as x y Lipschitz f( x) f( y) C x y CS 205A: Mathematical Methods Nonlinear Systems 6 / 24

10 Typical Regularizing Assumptions Continuous f( x) f( y) as x y Lipschitz f( x) f( y) C x y Differentiable Df( x) exists for all x CS 205A: Mathematical Methods Nonlinear Systems 6 / 24

11 Typical Regularizing Assumptions Continuous f( x) f( y) as x y Lipschitz f( x) f( y) C x y Differentiable Df( x) exists for all x C k k derivatives exist and are continuous CS 205A: Mathematical Methods Nonlinear Systems 6 / 24

12 Today f : R R CS 205A: Mathematical Methods Nonlinear Systems 7 / 24

13 Property of Continuous Functions Intermediate Value Theorem Suppose f : [a, b] R is continuous. Suppose f(x) < u < f(y). Then, there exists z between x and y such that f(z) = u. CS 205A: Mathematical Methods Nonlinear Systems 8 / 24

14 Property of Continuous Functions Intermediate Value Theorem Suppose f : [a, b] R is continuous. Suppose f(x) < u < f(y). Then, there exists z between x and y such that f(z) = u. Used in Homework 1! CS 205A: Mathematical Methods Nonlinear Systems 8 / 24

15 Reasonable Starting Point f(l) f(r) < 0 CS 205A: Mathematical Methods Nonlinear Systems 9 / 24

16 Bisection Algorithm 1. Compute c = l+r /2. 2. If f(c) = 0, return x = c. 3. If f(l) f(c) < 0, take r c. Otherwise take l c. 4. Return to step?? until r l < ε; then return c. CS 205A: Mathematical Methods Nonlinear Systems 10 / 24

17 Two Important Questions 1. Does it converge? CS 205A: Mathematical Methods Nonlinear Systems 11 / 24

18 Two Important Questions 1. Does it converge? Yes! Unconditionally. CS 205A: Mathematical Methods Nonlinear Systems 11 / 24

19 Two Important Questions 1. Does it converge? Yes! Unconditionally. 2. How quickly? CS 205A: Mathematical Methods Nonlinear Systems 11 / 24

20 Convergence Analysis Examine E k with x k x < E k. CS 205A: Mathematical Methods Nonlinear Systems 12 / 24

21 Bisection: Linear Convergence E k E k for E k r k l k CS 205A: Mathematical Methods Nonlinear Systems 13 / 24

22 Fixed Points g(x ) = x CS 205A: Mathematical Methods Nonlinear Systems 14 / 24

23 Fixed Points g(x ) = x Question: Same as root-finding? CS 205A: Mathematical Methods Nonlinear Systems 14 / 24

24 Simple Strategy x k+1 = g(x k ) CS 205A: Mathematical Methods Nonlinear Systems 15 / 24

25 Convergence Criterion E k x k x = g(x k 1 ) g(x ) CS 205A: Mathematical Methods Nonlinear Systems 16 / 24

26 Convergence Criterion E k x k x = g(x k 1 ) g(x ) C x k 1 x if g is Lipschitz = CE k 1 CS 205A: Mathematical Methods Nonlinear Systems 16 / 24

27 Alternative Criterion Lipschitz near x with good starting point. CS 205A: Mathematical Methods Nonlinear Systems 17 / 24

28 Alternative Criterion Lipschitz near x with good starting point. e.g. C 1 with g (x ) < 1 CS 205A: Mathematical Methods Nonlinear Systems 17 / 24

29 Convergence Rate of Fixed Point When it converges... Always linear (why?) CS 205A: Mathematical Methods Nonlinear Systems 18 / 24

30 Convergence Rate of Fixed Point When it converges... Always linear (why?) Often quadratic! CS 205A: Mathematical Methods Nonlinear Systems 18 / 24

31 Newton s Method x k+1 = x k f(x k) f (x k ) CS 205A: Mathematical Methods Nonlinear Systems 19 / 24

32 Newton s Method x k+1 = x k f(x k) f (x k ) Fixed point iteration on g(x) x f(x) f (x) CS 205A: Mathematical Methods Nonlinear Systems 19 / 24

33 Convergence of Newton Simple Root A root x with f (x ) 0. CS 205A: Mathematical Methods Nonlinear Systems 20 / 24

34 Convergence of Newton Simple Root A root x with f (x ) 0. Quadratic convergence in this case! CS 205A: Mathematical Methods Nonlinear Systems 20 / 24

35 Convergence of Newton Simple Root A root x with f (x ) 0. Quadratic convergence in this case! Higher-order approximations? CS 205A: Mathematical Methods Nonlinear Systems 20 / 24

36 Issue Differentiation is hard! CS 205A: Mathematical Methods Nonlinear Systems 21 / 24

37 Secant Method x k+1 = x k f(x k)(x k x k 1 ) f(x k ) f(x k 1 ) CS 205A: Mathematical Methods Nonlinear Systems 22 / 24

38 Secant Method x k+1 = x k f(x k)(x k x k 1 ) f(x k ) f(x k 1 ) Trivia: Converges at rate CS 205A: Mathematical Methods Nonlinear Systems 22 / 24

39 Secant Method x k+1 = x k f(x k)(x k x k 1 ) f(x k ) f(x k 1 ) Trivia: Converges at rate ( Golden Ratio ) CS 205A: Mathematical Methods Nonlinear Systems 22 / 24

40 Hybrid Methods Want: Convergence rate of secant/newton with convergence guarantees of bisection CS 205A: Mathematical Methods Nonlinear Systems 23 / 24

41 Hybrid Methods Want: Convergence rate of secant/newton with convergence guarantees of bisection e.g. Dekker s Method: Take secant step if it is in the bracket, bisection step otherwise CS 205A: Mathematical Methods Nonlinear Systems 23 / 24

42 Single-Variable Conclusion Unlikely to solve exactly, so we settle for iterative methods Must check that method converges at all Convergence rates: Linear: Ek+1 CE k for some 0 C < 1 Superlinear: E k+1 CEk r for some r > 1 Quadratic: r = 2 Cubic: r = 3 Time per iteration also important Next CS 205A: Mathematical Methods Nonlinear Systems 24 / 24

Nonlinearity Root-finding Bisection Fixed Point Iteration Newton s Method Secant Method Conclusion. Nonlinear Systems

Nonlinearity Root-finding Bisection Fixed Point Iteration Newton s Method Secant Method Conclusion. Nonlinear Systems Nonlinear Systems CS 205A: Mathematical Methods for Robotics, Vision, and Graphics Doug James (and Justin Solomon) CS 205A: Mathematical Methods Nonlinear Systems 1 / 27 Part III: Nonlinear Problems Not

More information

CS 323: Numerical Analysis and Computing

CS 323: Numerical Analysis and Computing CS 323: Numerical Analysis and Computing MIDTERM #2 Instructions: This is an open notes exam, i.e., you are allowed to consult any textbook, your class notes, homeworks, or any of the handouts from us.

More information

CS 323: Numerical Analysis and Computing

CS 323: Numerical Analysis and Computing CS 323: Numerical Analysis and Computing MIDTERM #2 Instructions: This is an open notes exam, i.e., you are allowed to consult any textbook, your class notes, homeworks, or any of the handouts from us.

More information

Computational Methods. Solving Equations

Computational Methods. Solving Equations Computational Methods Solving Equations Manfred Huber 2010 1 Solving Equations Solving scalar equations is an elemental task that arises in a wide range of applications Corresponds to finding parameters

More information

STOP, a i+ 1 is the desired root. )f(a i) > 0. Else If f(a i+ 1. Set a i+1 = a i+ 1 and b i+1 = b Else Set a i+1 = a i and b i+1 = a i+ 1

STOP, a i+ 1 is the desired root. )f(a i) > 0. Else If f(a i+ 1. Set a i+1 = a i+ 1 and b i+1 = b Else Set a i+1 = a i and b i+1 = a i+ 1 53 17. Lecture 17 Nonlinear Equations Essentially, the only way that one can solve nonlinear equations is by iteration. The quadratic formula enables one to compute the roots of p(x) = 0 when p P. Formulas

More information

Nonlinear Equations and Continuous Optimization

Nonlinear Equations and Continuous Optimization Nonlinear Equations and Continuous Optimization Sanzheng Qiao Department of Computing and Software McMaster University March, 2014 Outline 1 Introduction 2 Bisection Method 3 Newton s Method 4 Systems

More information

Ordinary Differential Equations I

Ordinary Differential Equations I Ordinary Differential Equations I CS 205A: Mathematical Methods for Robotics, Vision, and Graphics Justin Solomon CS 205A: Mathematical Methods Ordinary Differential Equations I 1 / 27 Theme of Last Three

More information

Computational Methods CMSC/AMSC/MAPL 460. Solving nonlinear equations and zero finding. Finding zeroes of functions

Computational Methods CMSC/AMSC/MAPL 460. Solving nonlinear equations and zero finding. Finding zeroes of functions Computational Methods CMSC/AMSC/MAPL 460 Solving nonlinear equations and zero finding Ramani Duraiswami, Dept. of Computer Science Where does it arise? Finding zeroes of functions Solving functional equations

More information

Variable. Peter W. White Fall 2018 / Numerical Analysis. Department of Mathematics Tarleton State University

Variable. Peter W. White Fall 2018 / Numerical Analysis. Department of Mathematics Tarleton State University Newton s Iterative s Peter W. White white@tarleton.edu Department of Mathematics Tarleton State University Fall 2018 / Numerical Analysis Overview Newton s Iterative s Newton s Iterative s Newton s Iterative

More information

Scientific Computing: An Introductory Survey

Scientific Computing: An Introductory Survey Scientific Computing: An Introductory Survey Chapter 5 Nonlinear Equations Prof. Michael T. Heath Department of Computer Science University of Illinois at Urbana-Champaign Copyright c 2002. Reproduction

More information

Jim Lambers MAT 460/560 Fall Semester Practice Final Exam

Jim Lambers MAT 460/560 Fall Semester Practice Final Exam Jim Lambers MAT 460/560 Fall Semester 2009-10 Practice Final Exam 1. Let f(x) = sin 2x + cos 2x. (a) Write down the 2nd Taylor polynomial P 2 (x) of f(x) centered around x 0 = 0. (b) Write down the corresponding

More information

Ordinary Differential Equations I

Ordinary Differential Equations I Ordinary Differential Equations I CS 205A: Mathematical Methods for Robotics, Vision, and Graphics Justin Solomon CS 205A: Mathematical Methods Ordinary Differential Equations I 1 / 32 Theme of Last Few

More information

Outline. Math Numerical Analysis. Intermediate Value Theorem. Lecture Notes Zeros and Roots. Joseph M. Mahaffy,

Outline. Math Numerical Analysis. Intermediate Value Theorem. Lecture Notes Zeros and Roots. Joseph M. Mahaffy, Outline Math 541 - Numerical Analysis Lecture Notes Zeros and Roots Joseph M. Mahaffy, jmahaffy@mail.sdsu.edu Department of Mathematics and Statistics Dynamical Systems Group Computational Sciences Research

More information

Math Numerical Analysis

Math Numerical Analysis Math 541 - Numerical Analysis Lecture Notes Zeros and Roots Joseph M. Mahaffy, jmahaffy@mail.sdsu.edu Department of Mathematics and Statistics Dynamical Systems Group Computational Sciences Research Center

More information

THE SECANT METHOD. q(x) = a 0 + a 1 x. with

THE SECANT METHOD. q(x) = a 0 + a 1 x. with THE SECANT METHOD Newton s method was based on using the line tangent to the curve of y = f (x), with the point of tangency (x 0, f (x 0 )). When x 0 α, the graph of the tangent line is approximately the

More information

Optimization II: Unconstrained Multivariable

Optimization II: Unconstrained Multivariable Optimization II: Unconstrained Multivariable CS 205A: Mathematical Methods for Robotics, Vision, and Graphics Justin Solomon CS 205A: Mathematical Methods Optimization II: Unconstrained Multivariable 1

More information

Simple Iteration, cont d

Simple Iteration, cont d Jim Lambers MAT 772 Fall Semester 2010-11 Lecture 2 Notes These notes correspond to Section 1.2 in the text. Simple Iteration, cont d In general, nonlinear equations cannot be solved in a finite sequence

More information

Optimization II: Unconstrained Multivariable

Optimization II: Unconstrained Multivariable Optimization II: Unconstrained Multivariable CS 205A: Mathematical Methods for Robotics, Vision, and Graphics Doug James (and Justin Solomon) CS 205A: Mathematical Methods Optimization II: Unconstrained

More information

CHAPTER-II ROOTS OF EQUATIONS

CHAPTER-II ROOTS OF EQUATIONS CHAPTER-II ROOTS OF EQUATIONS 2.1 Introduction The roots or zeros of equations can be simply defined as the values of x that makes f(x) =0. There are many ways to solve for roots of equations. For some

More information

MA 8019: Numerical Analysis I Solution of Nonlinear Equations

MA 8019: Numerical Analysis I Solution of Nonlinear Equations MA 8019: Numerical Analysis I Solution of Nonlinear Equations Suh-Yuh Yang ( 楊肅煜 ) Department of Mathematics, National Central University Jhongli District, Taoyuan City 32001, Taiwan syyang@math.ncu.edu.tw

More information

1.1: The bisection method. September 2017

1.1: The bisection method. September 2017 (1/11) 1.1: The bisection method Solving nonlinear equations MA385/530 Numerical Analysis September 2017 3 2 f(x)= x 2 2 x axis 1 0 1 x [0] =a x [2] =1 x [3] =1.5 x [1] =b 2 0.5 0 0.5 1 1.5 2 2.5 1 Solving

More information

Unit 2: Solving Scalar Equations. Notes prepared by: Amos Ron, Yunpeng Li, Mark Cowlishaw, Steve Wright Instructor: Steve Wright

Unit 2: Solving Scalar Equations. Notes prepared by: Amos Ron, Yunpeng Li, Mark Cowlishaw, Steve Wright Instructor: Steve Wright cs416: introduction to scientific computing 01/9/07 Unit : Solving Scalar Equations Notes prepared by: Amos Ron, Yunpeng Li, Mark Cowlishaw, Steve Wright Instructor: Steve Wright 1 Introduction We now

More information

Numerical Methods Lecture 3

Numerical Methods Lecture 3 Numerical Methods Lecture 3 Nonlinear Equations by Pavel Ludvík Introduction Definition (Root or zero of a function) A root (or a zero) of a function f is a solution of an equation f (x) = 0. We learn

More information

Nonlinear Equations. Chapter The Bisection Method

Nonlinear Equations. Chapter The Bisection Method Chapter 6 Nonlinear Equations Given a nonlinear function f(), a value r such that f(r) = 0, is called a root or a zero of f() For eample, for f() = e 016064, Fig?? gives the set of points satisfying y

More information

CS412: Introduction to Numerical Methods

CS412: Introduction to Numerical Methods CS412: Introduction to Numerical Methods MIDTERM #1 2:30PM - 3:45PM, Tuesday, 03/10/2015 Instructions: This exam is a closed book and closed notes exam, i.e., you are not allowed to consult any textbook,

More information

Queens College, CUNY, Department of Computer Science Numerical Methods CSCI 361 / 761 Spring 2018 Instructor: Dr. Sateesh Mane.

Queens College, CUNY, Department of Computer Science Numerical Methods CSCI 361 / 761 Spring 2018 Instructor: Dr. Sateesh Mane. Queens College, CUNY, Department of Computer Science Numerical Methods CSCI 361 / 761 Spring 2018 Instructor: Dr. Sateesh Mane c Sateesh R. Mane 2018 3 Lecture 3 3.1 General remarks March 4, 2018 This

More information

Ordinary Differential Equations I

Ordinary Differential Equations I Ordinary Differential Equations I CS 205A: Mathematical Methods for Robotics, Vision, and Graphics Doug James (and Justin Solomon) CS 205A: Mathematical Methods Ordinary Differential Equations I 1 / 35

More information

CS 221 Lecture 9. Tuesday, 1 November 2011

CS 221 Lecture 9. Tuesday, 1 November 2011 CS 221 Lecture 9 Tuesday, 1 November 2011 Some slides in this lecture are from the publisher s slides for Engineering Computation: An Introduction Using MATLAB and Excel 2009 McGraw-Hill Today s Agenda

More information

3.1 Introduction. Solve non-linear real equation f(x) = 0 for real root or zero x. E.g. x x 1.5 =0, tan x x =0.

3.1 Introduction. Solve non-linear real equation f(x) = 0 for real root or zero x. E.g. x x 1.5 =0, tan x x =0. 3.1 Introduction Solve non-linear real equation f(x) = 0 for real root or zero x. E.g. x 3 +1.5x 1.5 =0, tan x x =0. Practical existence test for roots: by intermediate value theorem, f C[a, b] & f(a)f(b)

More information

Nonlinear equations. Norms for R n. Convergence orders for iterative methods

Nonlinear equations. Norms for R n. Convergence orders for iterative methods Nonlinear equations Norms for R n Assume that X is a vector space. A norm is a mapping X R with x such that for all x, y X, α R x = = x = αx = α x x + y x + y We define the following norms on the vector

More information

Homework for Section 1.4, Continuity and One sided Limits. Study 1.4, # 1 21, 27, 31, 37 41, 45 53, 61, 69, 87, 91, 93. Class Notes: Prof. G.

Homework for Section 1.4, Continuity and One sided Limits. Study 1.4, # 1 21, 27, 31, 37 41, 45 53, 61, 69, 87, 91, 93. Class Notes: Prof. G. GOAL: 1. Understand definition of continuity at a point. 2. Evaluate functions for continuity at a point, and on open and closed intervals 3. Understand the Intermediate Value Theorum (IVT) Homework for

More information

Solving Non-Linear Equations (Root Finding)

Solving Non-Linear Equations (Root Finding) Solving Non-Linear Equations (Root Finding) Root finding Methods What are root finding methods? Methods for determining a solution of an equation. Essentially finding a root of a function, that is, a zero

More information

Test 2 Review Math 1111 College Algebra

Test 2 Review Math 1111 College Algebra Test 2 Review Math 1111 College Algebra 1. Begin by graphing the standard quadratic function f(x) = x 2. Then use transformations of this graph to graph the given function. g(x) = x 2 + 2 *a. b. c. d.

More information

CS 450 Numerical Analysis. Chapter 5: Nonlinear Equations

CS 450 Numerical Analysis. Chapter 5: Nonlinear Equations Lecture slides based on the textbook Scientific Computing: An Introductory Survey by Michael T. Heath, copyright c 2018 by the Society for Industrial and Applied Mathematics. http://www.siam.org/books/cl80

More information

Numerical Methods in Informatics

Numerical Methods in Informatics Numerical Methods in Informatics Lecture 2, 30.09.2016: Nonlinear Equations in One Variable http://www.math.uzh.ch/binf4232 Tulin Kaman Institute of Mathematics, University of Zurich E-mail: tulin.kaman@math.uzh.ch

More information

Numerical Optimization

Numerical Optimization Unconstrained Optimization (II) Computer Science and Automation Indian Institute of Science Bangalore 560 012, India. NPTEL Course on Unconstrained Optimization Let f : R R Unconstrained problem min x

More information

Lecture Notes to Accompany. Scientific Computing An Introductory Survey. by Michael T. Heath. Chapter 5. Nonlinear Equations

Lecture Notes to Accompany. Scientific Computing An Introductory Survey. by Michael T. Heath. Chapter 5. Nonlinear Equations Lecture Notes to Accompany Scientific Computing An Introductory Survey Second Edition by Michael T Heath Chapter 5 Nonlinear Equations Copyright c 2001 Reproduction permitted only for noncommercial, educational

More information

SOLUTION OF ALGEBRAIC AND TRANSCENDENTAL EQUATIONS BISECTION METHOD

SOLUTION OF ALGEBRAIC AND TRANSCENDENTAL EQUATIONS BISECTION METHOD BISECTION METHOD If a function f(x) is continuous between a and b, and f(a) and f(b) are of opposite signs, then there exists at least one root between a and b. It is shown graphically as, Let f a be negative

More information

NON-LINEAR ALGEBRAIC EQUATIONS Lec. 5.1: Nonlinear Equation in Single Variable

NON-LINEAR ALGEBRAIC EQUATIONS Lec. 5.1: Nonlinear Equation in Single Variable NON-LINEAR ALGEBRAIC EQUATIONS Lec. 5.1: Nonlinear Equation in Single Variable Dr. Niket Kaisare Department of Chemical Engineering IIT Madras NPTEL Course: MATLAB Programming for Numerical Computations

More information

Outline. Scientific Computing: An Introductory Survey. Nonlinear Equations. Nonlinear Equations. Examples: Nonlinear Equations

Outline. Scientific Computing: An Introductory Survey. Nonlinear Equations. Nonlinear Equations. Examples: Nonlinear Equations Methods for Systems of Methods for Systems of Outline Scientific Computing: An Introductory Survey Chapter 5 1 Prof. Michael T. Heath Department of Computer Science University of Illinois at Urbana-Champaign

More information

PART I Lecture Notes on Numerical Solution of Root Finding Problems MATH 435

PART I Lecture Notes on Numerical Solution of Root Finding Problems MATH 435 PART I Lecture Notes on Numerical Solution of Root Finding Problems MATH 435 Professor Biswa Nath Datta Department of Mathematical Sciences Northern Illinois University DeKalb, IL. 60115 USA E mail: dattab@math.niu.edu

More information

Numerical Analysis Fall. Roots: Open Methods

Numerical Analysis Fall. Roots: Open Methods Numerical Analysis 2015 Fall Roots: Open Methods Open Methods Open methods differ from bracketing methods, in that they require only a single starting value or two starting values that do not necessarily

More information

Roots of Equations. ITCS 4133/5133: Introduction to Numerical Methods 1 Roots of Equations

Roots of Equations. ITCS 4133/5133: Introduction to Numerical Methods 1 Roots of Equations Roots of Equations Direct Search, Bisection Methods Regula Falsi, Secant Methods Newton-Raphson Method Zeros of Polynomials (Horner s, Muller s methods) EigenValue Analysis ITCS 4133/5133: Introduction

More information

Solving nonlinear equations (See online notes and lecture notes for full details) 1.3: Newton s Method

Solving nonlinear equations (See online notes and lecture notes for full details) 1.3: Newton s Method Solving nonlinear equations (See online notes and lecture notes for full details) 1.3: Newton s Method MA385 Numerical Analysis September 2018 (1/16) Sir Isaac Newton, 1643-1727, England. Easily one of

More information

Optimization. CS Summer 2008 Jonathan Kaldor

Optimization. CS Summer 2008 Jonathan Kaldor Optimization CS3220 - Summer 2008 Jonathan Kaldor Problem Setup Suppose we have a function f(x) in one variable (for the moment) We want to find x such that f(x ) is a minimum of the function f(x) Can

More information

Numerical Methods. Roots of Equations

Numerical Methods. Roots of Equations Roots of Equations by Norhayati Rosli & Nadirah Mohd Nasir Faculty of Industrial Sciences & Technology norhayati@ump.edu.my, nadirah@ump.edu.my Description AIMS This chapter is aimed to compute the root(s)

More information

CHAPTER 4 ROOTS OF EQUATIONS

CHAPTER 4 ROOTS OF EQUATIONS CHAPTER 4 ROOTS OF EQUATIONS Chapter 3 : TOPIC COVERS (ROOTS OF EQUATIONS) Definition of Root of Equations Bracketing Method Graphical Method Bisection Method False Position Method Open Method One-Point

More information

An Improved Hybrid Algorithm to Bisection Method and Newton-Raphson Method

An Improved Hybrid Algorithm to Bisection Method and Newton-Raphson Method Applied Mathematical Sciences, Vol. 11, 2017, no. 56, 2789-2797 HIKARI Ltd, www.m-hikari.com https://doi.org/10.12988/ams.2017.710302 An Improved Hybrid Algorithm to Bisection Method and Newton-Raphson

More information

Chapter 1. Root Finding Methods. 1.1 Bisection method

Chapter 1. Root Finding Methods. 1.1 Bisection method Chapter 1 Root Finding Methods We begin by considering numerical solutions to the problem f(x) = 0 (1.1) Although the problem above is simple to state it is not always easy to solve analytically. This

More information

University of Houston, Department of Mathematics Numerical Analysis, Fall 2005

University of Houston, Department of Mathematics Numerical Analysis, Fall 2005 3 Numerical Solution of Nonlinear Equations and Systems 3.1 Fixed point iteration Reamrk 3.1 Problem Given a function F : lr n lr n, compute x lr n such that ( ) F(x ) = 0. In this chapter, we consider

More information

Nonlinear Equations. Your nonlinearity confuses me.

Nonlinear Equations. Your nonlinearity confuses me. Nonlinear Equations Your nonlinearity confuses me The problem of not knowing what we missed is that we believe we haven't missed anything Stephen Chew on Multitasking 1 Example General Engineering You

More information

BSM510 Numerical Analysis

BSM510 Numerical Analysis BSM510 Numerical Analysis Polynomial Interpolation Prof. Manar Mohaisen Department of EEC Engineering Review of Precedent Lecture Polynomial Regression Multiple Linear Regression Nonlinear Regression Lecture

More information

CHAPTER 2 POLYNOMIALS KEY POINTS

CHAPTER 2 POLYNOMIALS KEY POINTS CHAPTER POLYNOMIALS KEY POINTS 1. Polynomials of degrees 1, and 3 are called linear, quadratic and cubic polynomials respectively.. A quadratic polynomial in x with real coefficient is of the form a x

More information

Lecture 8. Root finding II

Lecture 8. Root finding II 1 Introduction Lecture 8 Root finding II In the previous lecture we considered the bisection root-bracketing algorithm. It requires only that the function be continuous and that we have a root bracketed

More information

MATH 3795 Lecture 12. Numerical Solution of Nonlinear Equations.

MATH 3795 Lecture 12. Numerical Solution of Nonlinear Equations. MATH 3795 Lecture 12. Numerical Solution of Nonlinear Equations. Dmitriy Leykekhman Fall 2008 Goals Learn about different methods for the solution of f(x) = 0, their advantages and disadvantages. Convergence

More information

Root Finding Convergence Analysis

Root Finding Convergence Analysis Root Finding Convergence Analysis Justin Ross & Matthew Kwitowski November 5, 2012 There are many different ways to calculate the root of a function. Some methods are direct and can be done by simply solving

More information

Midterm Review. Igor Yanovsky (Math 151A TA)

Midterm Review. Igor Yanovsky (Math 151A TA) Midterm Review Igor Yanovsky (Math 5A TA) Root-Finding Methods Rootfinding methods are designed to find a zero of a function f, that is, to find a value of x such that f(x) =0 Bisection Method To apply

More information

ROOT FINDING REVIEW MICHELLE FENG

ROOT FINDING REVIEW MICHELLE FENG ROOT FINDING REVIEW MICHELLE FENG 1.1. Bisection Method. 1. Root Finding Methods (1) Very naive approach based on the Intermediate Value Theorem (2) You need to be looking in an interval with only one

More information

Root Finding (and Optimisation)

Root Finding (and Optimisation) Root Finding (and Optimisation) M.Sc. in Mathematical Modelling & Scientific Computing, Practical Numerical Analysis Michaelmas Term 2018, Lecture 4 Root Finding The idea of root finding is simple we want

More information

GENG2140, S2, 2012 Week 7: Curve fitting

GENG2140, S2, 2012 Week 7: Curve fitting GENG2140, S2, 2012 Week 7: Curve fitting Curve fitting is the process of constructing a curve, or mathematical function, f(x) that has the best fit to a series of data points Involves fitting lines and

More information

Line Search Methods. Shefali Kulkarni-Thaker

Line Search Methods. Shefali Kulkarni-Thaker 1 BISECTION METHOD Line Search Methods Shefali Kulkarni-Thaker Consider the following unconstrained optimization problem min f(x) x R Any optimization algorithm starts by an initial point x 0 and performs

More information

MATH 131A: REAL ANALYSIS (BIG IDEAS)

MATH 131A: REAL ANALYSIS (BIG IDEAS) MATH 131A: REAL ANALYSIS (BIG IDEAS) Theorem 1 (The Triangle Inequality). For all x, y R we have x + y x + y. Proposition 2 (The Archimedean property). For each x R there exists an n N such that n > x.

More information

Lecture 5: Random numbers and Monte Carlo (Numerical Recipes, Chapter 7) Motivations for generating random numbers

Lecture 5: Random numbers and Monte Carlo (Numerical Recipes, Chapter 7) Motivations for generating random numbers Lecture 5: Random numbers and Monte Carlo (Numerical Recipes, Chapter 7) Motivations for generating random numbers To sample a function in a statistically controlled manner (i.e. for Monte Carlo integration)

More information

Ordinary Differential Equations II

Ordinary Differential Equations II Ordinary Differential Equations II CS 205A: Mathematical Methods for Robotics, Vision, and Graphics Justin Solomon CS 205A: Mathematical Methods Ordinary Differential Equations II 1 / 33 Almost Done! Last

More information

Math 551 Homework Assignment 3 Page 1 of 6

Math 551 Homework Assignment 3 Page 1 of 6 Math 551 Homework Assignment 3 Page 1 of 6 Name and section: ID number: E-mail: 1. Consider Newton s method for finding + α with α > 0 by finding the positive root of f(x) = x 2 α = 0. Assuming that x

More information

Numerical Methods. Root Finding

Numerical Methods. Root Finding Numerical Methods Solving Non Linear 1-Dimensional Equations Root Finding Given a real valued function f of one variable (say ), the idea is to find an such that: f() 0 1 Root Finding Eamples Find real

More information

1 The best of all possible worlds

1 The best of all possible worlds Notes for 2017-03-18 1 The best of all possible worlds Last time, we discussed three methods of solving f(x) = 0: Newton, modified Newton, and bisection. Newton is potentially faster than bisection; bisection

More information

x 2 x n r n J(x + t(x x ))(x x )dt. For warming-up we start with methods for solving a single equation of one variable.

x 2 x n r n J(x + t(x x ))(x x )dt. For warming-up we start with methods for solving a single equation of one variable. Maria Cameron 1. Fixed point methods for solving nonlinear equations We address the problem of solving an equation of the form (1) r(x) = 0, where F (x) : R n R n is a vector-function. Eq. (1) can be written

More information

Chapter 6. Nonlinear Equations. 6.1 The Problem of Nonlinear Root-finding. 6.2 Rate of Convergence

Chapter 6. Nonlinear Equations. 6.1 The Problem of Nonlinear Root-finding. 6.2 Rate of Convergence Chapter 6 Nonlinear Equations 6. The Problem of Nonlinear Root-finding In this module we consider the problem of using numerical techniques to find the roots of nonlinear equations, f () =. Initially we

More information

Motivation: We have already seen an example of a system of nonlinear equations when we studied Gaussian integration (p.8 of integration notes)

Motivation: We have already seen an example of a system of nonlinear equations when we studied Gaussian integration (p.8 of integration notes) AMSC/CMSC 460 Computational Methods, Fall 2007 UNIT 5: Nonlinear Equations Dianne P. O Leary c 2001, 2002, 2007 Solving Nonlinear Equations and Optimization Problems Read Chapter 8. Skip Section 8.1.1.

More information

1. Method 1: bisection. The bisection methods starts from two points a 0 and b 0 such that

1. Method 1: bisection. The bisection methods starts from two points a 0 and b 0 such that Chapter 4 Nonlinear equations 4.1 Root finding Consider the problem of solving any nonlinear relation g(x) = h(x) in the real variable x. We rephrase this problem as one of finding the zero (root) of a

More information

Ordinary Differential Equations II

Ordinary Differential Equations II Ordinary Differential Equations II CS 205A: Mathematical Methods for Robotics, Vision, and Graphics Justin Solomon CS 205A: Mathematical Methods Ordinary Differential Equations II 1 / 29 Almost Done! No

More information

Math 273a: Optimization Netwon s methods

Math 273a: Optimization Netwon s methods Math 273a: Optimization Netwon s methods Instructor: Wotao Yin Department of Mathematics, UCLA Fall 2015 some material taken from Chong-Zak, 4th Ed. Main features of Newton s method Uses both first derivatives

More information

Numerical Solution of f(x) = 0

Numerical Solution of f(x) = 0 Numerical Solution of f(x) = 0 Gerald W. Recktenwald Department of Mechanical Engineering Portland State University gerry@pdx.edu ME 350: Finding roots of f(x) = 0 Overview Topics covered in these slides

More information

Chapter 3: Root Finding. September 26, 2005

Chapter 3: Root Finding. September 26, 2005 Chapter 3: Root Finding September 26, 2005 Outline 1 Root Finding 2 3.1 The Bisection Method 3 3.2 Newton s Method: Derivation and Examples 4 3.3 How To Stop Newton s Method 5 3.4 Application: Division

More information

Appendix 8 Numerical Methods for Solving Nonlinear Equations 1

Appendix 8 Numerical Methods for Solving Nonlinear Equations 1 Appendix 8 Numerical Methods for Solving Nonlinear Equations 1 An equation is said to be nonlinear when it involves terms of degree higher than 1 in the unknown quantity. These terms may be polynomial

More information

p 1 p 0 (p 1, f(p 1 )) (p 0, f(p 0 )) The geometric construction of p 2 for the se- cant method.

p 1 p 0 (p 1, f(p 1 )) (p 0, f(p 0 )) The geometric construction of p 2 for the se- cant method. 80 CHAP. 2 SOLUTION OF NONLINEAR EQUATIONS f (x) = 0 y y = f(x) (p, 0) p 2 p 1 p 0 x (p 1, f(p 1 )) (p 0, f(p 0 )) The geometric construction of p 2 for the se- Figure 2.16 cant method. Secant Method The

More information

INTRODUCTION TO NUMERICAL ANALYSIS

INTRODUCTION TO NUMERICAL ANALYSIS INTRODUCTION TO NUMERICAL ANALYSIS Cho, Hyoung Kyu Department of Nuclear Engineering Seoul National University 3. SOLVING NONLINEAR EQUATIONS 3.1 Background 3.2 Estimation of errors in numerical solutions

More information

MATH 4211/6211 Optimization Basics of Optimization Problems

MATH 4211/6211 Optimization Basics of Optimization Problems MATH 4211/6211 Optimization Basics of Optimization Problems Xiaojing Ye Department of Mathematics & Statistics Georgia State University Xiaojing Ye, Math & Stat, Georgia State University 0 A standard minimization

More information

Solution of Nonlinear Equations

Solution of Nonlinear Equations Solution of Nonlinear Equations (Com S 477/577 Notes) Yan-Bin Jia Sep 14, 017 One of the most frequently occurring problems in scientific work is to find the roots of equations of the form f(x) = 0. (1)

More information

Conjugate Gradients I: Setup

Conjugate Gradients I: Setup Conjugate Gradients I: Setup CS 205A: Mathematical Methods for Robotics, Vision, and Graphics Justin Solomon CS 205A: Mathematical Methods Conjugate Gradients I: Setup 1 / 22 Time for Gaussian Elimination

More information

Zeroes of Transcendental and Polynomial Equations. Bisection method, Regula-falsi method and Newton-Raphson method

Zeroes of Transcendental and Polynomial Equations. Bisection method, Regula-falsi method and Newton-Raphson method Zeroes of Transcendental and Polynomial Equations Bisection method, Regula-falsi method and Newton-Raphson method PRELIMINARIES Solution of equation f (x) = 0 A number (real or complex) is a root of the

More information

Single Variable Minimization

Single Variable Minimization AA222: MDO 37 Sunday 1 st April, 2012 at 19:48 Chapter 2 Single Variable Minimization 2.1 Motivation Most practical optimization problems involve many variables, so the study of single variable minimization

More information

Numerical Analysis. EE, NCKU Tien-Hao Chang (Darby Chang)

Numerical Analysis. EE, NCKU Tien-Hao Chang (Darby Chang) Numerical Analysis EE, NCKU Tien-Hao Chang (Darby Chang) 1 In the previous slide Error (motivation) Floating point number system difference to real number system problem of roundoff Introduced/propagated

More information

CLASS NOTES Models, Algorithms and Data: Introduction to computing 2018

CLASS NOTES Models, Algorithms and Data: Introduction to computing 2018 CLASS NOTES Models, Algorithms and Data: Introduction to computing 2018 Petros Koumoutsakos, Jens Honore Walther (Last update: April 16, 2018) IMPORTANT DISCLAIMERS 1. REFERENCES: Much of the material

More information

1 Review of Interpolation using Cubic Splines

1 Review of Interpolation using Cubic Splines cs412: introduction to numerical analysis 10/10/06 Lecture 12: Instructor: Professor Amos Ron Cubic Hermite Spline Interpolation Scribes: Yunpeng Li, Mark Cowlishaw 1 Review of Interpolation using Cubic

More information

MS 2001: Test 1 B Solutions

MS 2001: Test 1 B Solutions MS 2001: Test 1 B Solutions Name: Student Number: Answer all questions. Marks may be lost if necessary work is not clearly shown. Remarks by me in italics and would not be required in a test - J.P. Question

More information

Unconstrained optimization I Gradient-type methods

Unconstrained optimization I Gradient-type methods Unconstrained optimization I Gradient-type methods Antonio Frangioni Department of Computer Science University of Pisa www.di.unipi.it/~frangio frangio@di.unipi.it Computational Mathematics for Learning

More information

8 Numerical methods for unconstrained problems

8 Numerical methods for unconstrained problems 8 Numerical methods for unconstrained problems Optimization is one of the important fields in numerical computation, beside solving differential equations and linear systems. We can see that these fields

More information

Chapter 4: Numerical Methods for Common Mathematical Problems

Chapter 4: Numerical Methods for Common Mathematical Problems 1 Capter 4: Numerical Metods for Common Matematical Problems Interpolation Problem: Suppose we ave data defined at a discrete set of points (x i, y i ), i = 0, 1,..., N. Often it is useful to ave a smoot

More information

Nonlinear Equations. Not guaranteed to have any real solutions, but generally do for astrophysical problems.

Nonlinear Equations. Not guaranteed to have any real solutions, but generally do for astrophysical problems. Nonlinear Equations Often (most of the time??) the relevant system of equations is not linear in the unknowns. Then, cannot decompose as Ax = b. Oh well. Instead write as: (1) f(x) = 0 function of one

More information

Math 320: Real Analysis MWF 1pm, Campion Hall 302 Homework 8 Solutions Please write neatly, and in complete sentences when possible.

Math 320: Real Analysis MWF 1pm, Campion Hall 302 Homework 8 Solutions Please write neatly, and in complete sentences when possible. Math 320: Real Analysis MWF pm, Campion Hall 302 Homework 8 Solutions Please write neatly, and in complete sentences when possible. Do the following problems from the book: 4.3.5, 4.3.7, 4.3.8, 4.3.9,

More information

4. We accept without proofs that the following functions are differentiable: (e x ) = e x, sin x = cos x, cos x = sin x, log (x) = 1 sin x

4. We accept without proofs that the following functions are differentiable: (e x ) = e x, sin x = cos x, cos x = sin x, log (x) = 1 sin x 4 We accept without proofs that the following functions are differentiable: (e x ) = e x, sin x = cos x, cos x = sin x, log (x) = 1 sin x x, x > 0 Since tan x = cos x, from the quotient rule, tan x = sin

More information

Lecture 7: Minimization or maximization of functions (Recipes Chapter 10)

Lecture 7: Minimization or maximization of functions (Recipes Chapter 10) Lecture 7: Minimization or maximization of functions (Recipes Chapter 10) Actively studied subject for several reasons: Commonly encountered problem: e.g. Hamilton s and Lagrange s principles, economics

More information

Root Finding: Close Methods. Bisection and False Position Dr. Marco A. Arocha Aug, 2014

Root Finding: Close Methods. Bisection and False Position Dr. Marco A. Arocha Aug, 2014 Root Finding: Close Methods Bisection and False Position Dr. Marco A. Arocha Aug, 2014 1 Roots Given function f(x), we seek x values for which f(x)=0 Solution x is the root of the equation or zero of the

More information

Quiescent Steady State (DC) Analysis The Newton-Raphson Method

Quiescent Steady State (DC) Analysis The Newton-Raphson Method Quiescent Steady State (DC) Analysis The Newton-Raphson Method J. Roychowdhury, University of California at Berkeley Slide 1 Solving the System's DAEs DAEs: many types of solutions useful DC steady state:

More information

Line Search Techniques

Line Search Techniques Multidisciplinary Design Optimization 33 Chapter 2 Line Search Techniques 2.1 Introduction Most practical optimization problems involve many variables, so the study of single variable minimization may

More information

Figure 1: Graph of y = x cos(x)

Figure 1: Graph of y = x cos(x) Chapter The Solution of Nonlinear Equations f(x) = 0 In this chapter we will study methods for find the solutions of functions of single variables, ie values of x such that f(x) = 0 For example, f(x) =

More information

Solution of Nonlinear Equations: Graphical and Incremental Sea

Solution of Nonlinear Equations: Graphical and Incremental Sea Outlines Solution of Nonlinear Equations: Graphical and s September 2, 2004 Outlines Part I: Solution of Nonlinear Equations Solution of Nonlinear Equations Introduction General Form of the Problem Types

More information

Exam 2. Average: 85.6 Median: 87.0 Maximum: Minimum: 55.0 Standard Deviation: Numerical Methods Fall 2011 Lecture 20

Exam 2. Average: 85.6 Median: 87.0 Maximum: Minimum: 55.0 Standard Deviation: Numerical Methods Fall 2011 Lecture 20 Exam 2 Average: 85.6 Median: 87.0 Maximum: 100.0 Minimum: 55.0 Standard Deviation: 10.42 Fall 2011 1 Today s class Multiple Variable Linear Regression Polynomial Interpolation Lagrange Interpolation Newton

More information