SOLVING SYSTEMS OF EQUATIONS, ITERATIVE METHODS

Similar documents
The Islamic University of Gaza Faculty of Engineering Civil Engineering Department. Numerical Analysis ECIV Chapter 11

Lecture Solution of a System of Linear Equation

Matrix Eigenvalues and Eigenvectors September 13, 2017

September 13 Homework Solutions

Second degree generalized gauss-seidel iteration method for solving linear system of equations. ABSTRACT

ITERATIVE SOLUTION REFINEMENT

Lecture 3. In this lecture, we will discuss algorithms for solving systems of linear equations.

Review of Gaussian Quadrature method

Matrices and Determinants

4.5 JACOBI ITERATION FOR FINDING EIGENVALUES OF A REAL SYMMETRIC MATRIX. be a real symmetric matrix. ; (where we choose θ π for.

Linear Algebra Introduction

along the vector 5 a) Find the plane s coordinate after 1 hour. b) Find the plane s coordinate after 2 hours. c) Find the plane s coordinate

M344 - ADVANCED ENGINEERING MATHEMATICS

A - INTRODUCTION AND OVERVIEW

How do we solve these things, especially when they get complicated? How do we know when a system has a solution, and when is it unique?

Bases for Vector Spaces

CSCI 5525 Machine Learning

Matrix Algebra. Matrix Addition, Scalar Multiplication and Transposition. Linear Algebra I 24

Things to Memorize: A Partial List. January 27, 2017

Engineering Analysis ENG 3420 Fall Dan C. Marinescu Office: HEC 439 B Office hours: Tu-Th 11:00-12:00

1 Linear Least Squares

Lecture 2e Orthogonal Complement (pages )

How do we solve these things, especially when they get complicated? How do we know when a system has a solution, and when is it unique?

Physics 1402: Lecture 7 Today s Agenda

Math 270A: Numerical Linear Algebra

COSC 3361 Numerical Analysis I Numerical Integration and Differentiation (III) - Gauss Quadrature and Adaptive Quadrature

1. Twelve less than five times a number is thirty three. What is the number

Matrices and Linear Algebra

Chapter 4 Contravariance, Covariance, and Spacetime Diagrams

CHAPTER 4a. ROOTS OF EQUATIONS

Chapter 6 Continuous Random Variables and Distributions

Math 1B, lecture 4: Error bounds for numerical methods

LINEAR ALGEBRA AND MATRICES. n ij. is called the main diagonal or principal diagonal of A. A column vector is a matrix that has only one column.

SCHOOL OF ENGINEERING & BUILT ENVIRONMENT

Lesson 1: Quadratic Equations

I1 = I2 I1 = I2 + I3 I1 + I2 = I3 + I4 I 3

2. VECTORS AND MATRICES IN 3 DIMENSIONS

1B40 Practical Skills

BİL 354 Veritabanı Sistemleri. Relational Algebra (İlişkisel Cebir)

Chapter 9 Many Electron Atoms

Chapter 3 Solving Nonlinear Equations

MATH 573 FINAL EXAM. May 30, 2007

INTRODUCTION TO LINEAR ALGEBRA

Duality # Second iteration for HW problem. Recall our LP example problem we have been working on, in equality form, is given below.

Jim Lambers MAT 169 Fall Semester Lecture 4 Notes

z TRANSFORMS z Transform Basics z Transform Basics Transfer Functions Back to the Time Domain Transfer Function and Stability

Operations with Matrices

Chapter 6 Notes, Larson/Hostetler 3e

Numerical quadrature based on interpolating functions: A MATLAB implementation

Equations and Inequalities

THE EXISTENCE-UNIQUENESS THEOREM FOR FIRST-ORDER DIFFERENTIAL EQUATIONS.

CHAPTER 6b. NUMERICAL INTERPOLATION

Designing Information Devices and Systems I Discussion 8B

1 nonlinear.mcd Find solution root to nonlinear algebraic equation f(x)=0. Instructor: Nam Sun Wang

Matrices, Moments and Quadrature, cont d

Parse trees, ambiguity, and Chomsky normal form

SOLUTION OF QUADRATIC NONLINEAR PROBLEMS WITH MULTIPLE SCALES LINDSTEDT-POINCARE METHOD. Mehmet Pakdemirli and Gözde Sarı

308K. 1 Section 3.2. Zelaya Eufemia. 1. Example 1: Multiplication of Matrices: X Y Z R S R S X Y Z. By associativity we have to choices:

CS 373, Spring Solutions to Mock midterm 1 (Based on first midterm in CS 273, Fall 2008.)

2.4 Linear Inequalities and Interval Notation

A-Level Mathematics Transition Task (compulsory for all maths students and all further maths student)

p-adic Egyptian Fractions

A Modified ADM for Solving Systems of Linear Fredholm Integral Equations of the Second Kind

ELE B7 Power System Engineering. Unbalanced Fault Analysis

Linear Systems with Constant Coefficients

Numerical Methods for Chemical Engineers

In Section 5.3 we considered initial value problems for the linear second order equation. y.a/ C ˇy 0.a/ D k 1 (13.1.4)

6.004 Computation Structures Spring 2009

Math 154B Elementary Algebra-2 nd Half Spring 2015

Adding and Subtracting Rational Expressions

Linear Inequalities. Work Sheet 1

Lecture 13 - Linking E, ϕ, and ρ

13: Diffusion in 2 Energy Groups

CS:4330 Theory of Computation Spring Regular Languages. Equivalences between Finite automata and REs. Haniel Barbosa

MASTER CLASS PROGRAM UNIT 4 SPECIALIST MATHEMATICS WEEK 11 WRITTEN EXAMINATION 2 SOLUTIONS SECTION 1 MULTIPLE CHOICE QUESTIONS

Mathematics. Area under Curve.

Section 3.2 Maximum Principle and Uniqueness

Before we can begin Ch. 3 on Radicals, we need to be familiar with perfect squares, cubes, etc. Try and do as many as you can without a calculator!!!

AUTOMATA AND LANGUAGES. Definition 1.5: Finite Automaton

Nondeterminism. Nondeterministic Finite Automata. Example: Moves on a Chessboard. Nondeterminism (2) Example: Chessboard (2) Formal NFA

MATRIX DEFINITION A matrix is any doubly subscripted array of elements arranged in rows and columns.

1. Extend QR downwards to meet the x-axis at U(6, 0). y

Rudimentary Matrix Algebra

FABER Formal Languages, Automata and Models of Computation

The graphs of Rational Functions

Math 4310 Solutions to homework 1 Due 9/1/16

Multivariate problems and matrix algebra

Quadratic Forms. Quadratic Forms

How do you know you have SLE?

Module 6 Value Iteration. CS 886 Sequential Decision Making and Reinforcement Learning University of Waterloo

Applied Physics Introduction to Vibrations and Waves (with a focus on elastic waves) Course Outline

Math Lecture 23

If C = 60 and = P, find the value of P. c 2 = a 2 + b 2 2abcos 60 = a 2 + b 2 ab a 2 + b 2 = c 2 + ab. c a

Elements of Matrix Algebra

Section 6.1 INTRO to LAPLACE TRANSFORMS

378 Relations Solutions for Chapter 16. Section 16.1 Exercises. 3. Let A = {0,1,2,3,4,5}. Write out the relation R that expresses on A.

III. Lecture on Numerical Integration. File faclib/dattab/lecture-notes/numerical-inter03.tex /by EC, 3/14/2008 at 15:11, version 9

Chapter 4: Techniques of Circuit Analysis. Chapter 4: Techniques of Circuit Analysis

1 Online Learning and Regret Minimization

Continuous Random Variables Class 5, Jeremy Orloff and Jonathan Bloom

Transcription:

ELM Numericl Anlysis Dr Muhrrem Mercimek SOLVING SYSTEMS OF EQUATIONS, ITERATIVE METHODS ELM Numericl Anlysis Some of the contents re dopted from Lurene V. Fusett, Applied Numericl Anlysis using MATLAB. Prentice Hll Inc., 999

ELM Numericl Anlysis Dr Muhrrem Mercimek Tody s lecture Common clssicl itertive techniques for liner eqution systems Jcoi Method Guss- Seidel Method Successive Over Reltion

ELM Numericl Anlysis Dr Muhrrem Mercimek Itertive techniques for liner eqution systems Systems of liner equtions for which numericl solutions re needed re often very lrge. Using generl methods such s Guss elimintion is computtionlly epensive. If the coefficient system is hving specific structure itertive techniques re preferle.

ELM Numericl Anlysis Dr Muhrrem Mercimek 4 Itertive techniques for liner eqution systems For lrge, sprse systems (mny coefficients whose vlue is zero) itertive techniques re preferle.

ELM Numericl Anlysis Dr Muhrrem Mercimek 5 Wht is n eqution System? When you hve A B derive the equivlent system C d nd solve it Generte sequence of pproimtion (), (),..., where ( k) C ( k ) d

ELM Numericl Anlysis Dr Muhrrem Mercimek Jcoi Method Emple : A eqution system A = = C + d A = = y = Strt with y / y y y y Simultneous updting C hs zeros in the digonl New vlues of the vriles re not used until new itertion step is egun y () () y 4 4 4 4

ELM Numericl Anlysis Dr Muhrrem Mercimek 7 Jcoi Method A = = y = / () y 4 4 () y 4 4 () () y 8 8 () () y 8 8 Stopping Criteri: Stop the itertions when The function A (k) less thn tol vlue.(. is -Norm or Eucliden Norm) y The m numer of itertions m _iter hs reched

ELM Numericl Anlysis Dr Muhrrem Mercimek 8 Jcoi Method Emple : Consider the system 0.5 0.5 0.5 0.5 0.5 0.5 0.5.0.5 () () () 0.0 0.5 0.5 Strt with 0.5 0.5 0.0 0.5 0.5 0.0 (0,0,0) 0.5.0.5 () () () 0.0 0.5 0.5 0.5 0.0 0.5 0.5 0.5 0.0 0.5.5 0.5.0.5 The method converges in itertions () = [.00.00 0.9997] T

ELM Numericl Anlysis Dr Muhrrem Mercimek 9 Jcoi Method Emple : A necessry nd sufficient condition for the convergence of the Jcoi method the mgnitude of the lrgest eigenvlue of the itertion mtri C e less thn A necessry condition (not sufficient) for the convergence of the Jcoi method A should e digonlly dominnt. Mgnitude of digonl element should e greter thn sum f mgnitudes of other elements of the row A = = y = y y y y () y 5 y () 5 When you run the other itertions you will see it diverges

ELM Numericl Anlysis Dr Muhrrem Mercimek 0 Guss-Seidel Method Emple 4: A eqution system A = A = = y = Strt with y / y y y y Sequentil updting y () () New vlues of the vriles re used in the sme itertion step y () 4 4 8 8 y () () y () () 5 5

ELM Numericl Anlysis Dr Muhrrem Mercimek Guss-Seidel Method Emple 5: Consider the three-y-three system new ( old) 0.5 0.5 new new 0.5 0.5 new new new 0.5 0.5 Strt with After 0 itertions =[.000.9999 -.000] (0,0,0) ( old) ( old) 0.5.0.5 Stopping Criteri: Stop the itertions when The function A (k) less thn tol vlue.(. is -Norm or Eucliden Norm) The m numer of itertions m _iter hs reched

ELM Numericl Anlysis Dr Muhrrem Mercimek Guss-Seidel Method Discussion The Guss-Seidel method is sensitive to the form of the coefficient mtri A The Guss-Seidel method typiclly converges more rpidly thn the Jcoi method The Guss-Seidel method is more difficult to use for prllel computtion

ELM Numericl Anlysis Dr Muhrrem Mercimek Successive Over Reltion Introduce n dditionl prmeter, ω, tht my ccelerte the convergence of the itertions. A comintion of current updte (From Guss-Seidel) nd previous vlue. A proportion from current updte, s well s proportion from previous vlue is summed up. ω Similry = ω( ) dd ω to oth sides nd use it in nd updtes cn e found multiply ech eqution with ω Similry nd updtes cn e found Guss-Seidel Method updtes new old Similry nd old updtes old ( ) ( ) cn e found new old new old ( ) ( ) new ( ) old ( new new ) A proportion from current updte

ELM Numericl Anlysis Dr Muhrrem Mercimek 4 Successive Over Reltion (SOR) Consider the three-y-three system A 4 0 5 0 5 8 9 4 new old old ( ) ( ) new old new 5 old ( ) ( new old 5 new 4 ( ) ( ) 9 ) 4

ELM Numericl Anlysis Dr Muhrrem Mercimek 5 Successive Over Reltion (SOR) A 4 0 5 0 5 8 9 4 Required numer of itertions for different vlues of the reltion prmeter Strt with Tolernce = 0.0000 ω 0.8 0.9.0..5..4 No. of itertions 44 9 8 5 5

ELM Numericl Anlysis Dr Muhrrem Mercimek Successive Over Reltion (SOR) Discussion The SOR method cn e derived y multiplying the decomposed system otined from the Guss-Seidel method y the reltion prmeter w The itertive prmeter w should lwys e chosen such tht 0 < w <

ELM Numericl Anlysis Dr Muhrrem Mercimek 7 Summry Jcoi method Guss-seidel method k k k SOR method k k k k k k k k k k k k new old old old ( ) ( ) new old new old ( ) ( ) new ( ) old ( new new ) k k k 7