Numerical Analysis Lecture11.

Slides:



Advertisements
Similar presentations
Numerical Solution of Linear Equations
Advertisements

Chapter 2 Solutions of Systems of Linear Equations / Matrix Inversion
Chapter: 3c System of Linear Equations
Linear Algebra Applications in Matlab ME 303. Special Characters and Matlab Functions.
Algebraic, transcendental (i.e., involving trigonometric and exponential functions), ordinary differential equations, or partial differential equations...
Chapter 4 Systems of Linear Equations; Matrices Section 6 Matrix Equations and Systems of Linear Equations.
Matrices: Inverse Matrix
Lecture 9: Introduction to Matrix Inversion Gaussian Elimination Sections 2.4, 2.5, 2.6 Sections 2.2.3, 2.3.
Special Matrices and Gauss-Siedel
MECH300H Introduction to Finite Element Methods Lecture 2 Review.
ECIV 301 Programming & Graphics Numerical Methods for Engineers Lecture 20 Solution of Linear System of Equations - Iterative Methods.
Special Matrices and Gauss-Siedel
SYSTEMS OF LINEAR EQUATIONS
Major: All Engineering Majors Author(s): Autar Kaw
Computer Engineering Majors Authors: Autar Kaw
Matrices Square is Good! Copyright © 2014 Curt Hill.
Copyright © 2006 The McGraw-Hill Companies, Inc. Permission required for reproduction or display. by Lale Yurttas, Texas A&M University Chapter 111.
Copyright © The McGraw-Hill Companies, Inc. Permission required for reproduction or display. 1 Chapter 11.
ΑΡΙΘΜΗΤΙΚΕΣ ΜΕΘΟΔΟΙ ΜΟΝΤΕΛΟΠΟΙΗΣΗΣ 4. Αριθμητική Επίλυση Συστημάτων Γραμμικών Εξισώσεων Gaussian elimination Gauss - Jordan 1.
10/17/ Gauss-Siedel Method Industrial Engineering Majors Authors: Autar Kaw
Chapter 3 Solution of Algebraic Equations 1 ChE 401: Computational Techniques for Chemical Engineers Fall 2009/2010 DRAFT SLIDES.
10/26/ Gauss-Siedel Method Civil Engineering Majors Authors: Autar Kaw Transforming.
Lecture 7 - Systems of Equations CVEN 302 June 17, 2002.
Chapter 5 MATRIX ALGEBRA: DETEMINANT, REVERSE, EIGENVALUES.
Elliptic PDEs and the Finite Difference Method
Copyright © The McGraw-Hill Companies, Inc. Permission required for reproduction or display. 1 Part 3- Chapter 12 Iterative Methods.
Linear Systems – Iterative methods
Meeting 19 System of Linear Equations. Linear Equations A solution of a linear equation in n variables is a sequence of n real numbers s 1, s 2,..., s.
Copyright ©2015 Pearson Education, Inc. All rights reserved.
Mechanical Engineering Majors Authors: Autar Kaw
2/26/ Gauss-Siedel Method Electrical Engineering Majors Authors: Autar Kaw
Linear Systems Numerical Methods. 2 Jacobi Iterative Method Choose an initial guess (i.e. all zeros) and Iterate until the equality is satisfied. No guarantee.
Gaoal of Chapter 2 To develop direct or iterative methods to solve linear systems Useful Words upper/lower triangular; back/forward substitution; coefficient;
3/6/ Gauss-Siedel Method Major: All Engineering Majors Author: دکتر ابوالفضل رنجبر نوعی
Gaussian Elimination Civil Engineering Majors Author(s): Autar Kaw Transforming Numerical Methods Education for STEM.
Unit #1 Linear Systems Fall Dr. Jehad Al Dallal.
Autar Kaw Benjamin Rigsby Transforming Numerical Methods Education for STEM Undergraduates.
Lecture 9 Numerical Analysis. Solution of Linear System of Equations Chapter 3.
Numerical Methods. Introduction Prof. S. M. Lutful Kabir, BRAC University2  One of the most popular techniques for solving simultaneous linear equations.
Numerical Methods. Prof. S. M. Lutful Kabir, BRAC University2  One of the most popular techniques for solving simultaneous linear equations is the Gaussian.
Chapter: 3c System of Linear Equations
ECE 3301 General Electrical Engineering
Spring Dr. Jehad Al Dallal
Gaussian Elimination.
Solving Systems of Linear Equations: Iterative Methods
Gauss-Siedel Method.
Numerical Analysis Lecture12.
We will be looking for a solution to the system of linear differential equations with constant coefficients.
Advanced Numerical Methods (S. A. Sahu) Code: AMC 51151
Linear Algebra Lecture 15.
Chapter 10: Solving Linear Systems of Equations
Autar Kaw Benjamin Rigsby
Numerical Analysis Lecture 45.
Metode Eliminasi Pertemuan – 4, 5, 6 Mata Kuliah : Analisis Numerik
Numerical Analysis Lecture 16.
Larger Systems of Linear Equations and Matrices
Systems of Equations and Inequalities
Lial/Hungerford/Holcomb/Mullins: Mathematics with Applications 11e Finite Mathematics with Applications 11e Copyright ©2015 Pearson Education, Inc. All.
Systems of Linear Equations
Matrix Solutions to Linear Systems
Numerical Analysis Lecture14.
Numerical Analysis Lecture13.
6.5 Taylor Series Linearization
Numerical Analysis Lecture10.
Numerical Analysis Lecture 17.
Systems of Linear Equations
Chapter 11 Chapter 11.
Linear Algebra Lecture 16.
Pivoting, Perturbation Analysis, Scaling and Equilibration
Ax = b Methods for Solution of the System of Equations (ReCap):
Presentation transcript:

Numerical Analysis Lecture11

Chapter 3

Solution of Linear System of Equations and Matrix Inversion

Introduction Gaussian Elimination Gauss-Jordon Elimination Crout’s Reduction Jacobi’s Gauss- Seidal Iteration Relaxation Matrix Inversion

Jacobi’s Method

This is an iterative method, where initial approximate solution to a given system of equations is assumed and is improved towards the exact solution in an iterative way.

In general, when the coefficient matrix of the system of equations is a sparse matrix (many elements are zero), iterative methods have definite advantage over direct methods in respect of economy of computer memory

Such sparse matrices arise in computing the numerical solution of partial differential equations.

Let us consider

In this method, we assume that the coefficient matrix [A] is strictly diagonally dominant, that is, in each row of [A] the modulus of the diagonal element exceeds the sum of the off-diagonal elements.

We also assume that the diagonal element do not vanish We also assume that the diagonal element do not vanish. If any diagonal element vanishes, the equations can always be rearranged to satisfy this condition.

Now the above system of equations can be written as

We shall take this solution vector We shall take this solution vector as a first approximation to the exact solution of system. For convenience, let us denote the first approximation vector by got after taking as an initial starting vector.

Substituting this first approximation in the right-hand side of system, we obtain the second approximation to the given system in the form

This second approximation is substituted into the right-hand side of Equations and obtain the third approximation and so on.

This process is repeated and (r+1)th approximation is calculated

Briefly, we can rewrite these Equations as

It is also known as method of simultaneous displacements, since no element of is used in this iteration until every element is computed.

A sufficient condition for convergence of the iterative solution to the exact solution is When this condition (diagonal dominance) is true, Jacobi’s method converges

Example Find the solution to the following system of equations using Jacobi’s iterative method for the first five iterations:

Solution

Taking the initial starting of solution vector as from Eq. ,we have the first approximation as

Now, using Eq. ,the second approximation is computed from the equations

Making use of the last two equations we get the second approximation as

Similar procedure yields the third, fourth and fifth approximations to the required solution and they are tabulated as below;

Variables Iteration number r x y z 1 1.1446 2.0000 2.4483 2 0.9976 1.2339 1.7424 3 1.0651 1.4301 2.0046 4 1.0517 1.3555 1.9435 5 1.0587 1.3726 1.9655

Numerical Analysis Lecture11