Numerical Programming I (for CSE)
|
|
- Victor White
- 5 years ago
- Views:
Transcription
1 Technische Universität München WT 1/13 Fakultät für Mathematik Prof. Dr. M. Mehl B. Gatzhammer January 1, 13 Numerical Programming I (for CSE) Tutorial 1: Iterative Methods 1) Relaxation Methods a) Let C R n n be a diagonalizable matrix. Show: ρ(c) < 1 lim i C i =. Hint: Write down the powers of C by using its diagonalized representation. b) Consider the SOR method, i.e., the Gauss-Seidel method with relaxation, x (i+1) = x (i) + αy (i), c) where y (i) is the update of the Gauss-Seidel iteration. Prove that α (, ) is a necessary (but not sufficient) condition for convergence. Hint: Use the determinant of the iteration matrix det(c) of the SOR method and consider the relation between the eigenvalues λ i of C and det(c). An intermediate result is det(c) = (1 α) n. For which matrices do the Jacobi method and the Gauss-Seidel method converge? (i) (ii) 1 1 (iii) Solution: a) If C is diagonalizable, we can represent it as C = S 1 DS, where D is a diagonal matrix with the eigenvalues of C and S is a matrix made up by the eigenvectors of C. Computing C i, we get C i = (S 1 DS) i = S 1 DS S 1 DS... S 1 DS = S 1 DD...DS = S 1 D i S Since ρ(c) < 1, we have λ i < 1 and D i = diag(λ i 1,..., λ i n) i. Thus, lim i Ci = lim S 1 D i S = S 1 S =. i Remark: This can be also shown for an arbitrary matrix C. In the general case, you have to prove for the Jordan matrix J that J i i.
2 Numerical Programming I (for CSE) Tutorial 1: Iterative Methods, page b) With M = 1 α D A + L A, we get det(c) = det( M 1 (A M)) = triangular matrixes = = det(a M) = det (( 1 1 ) ) α DA + U A det( M) det ( 1 α D ) A L A det (( 1 1 ) ) ( α DA 1 det ( 1 α D ) = α) det(da ) ( A α) det(da ) ( ) 1 α 1 = (1 α) n α Thus, we have α / (, ) det(c) 1 i : λ i 1 a) C i i x x (i) i c) Matrix A is strictly diagonally dominant, iff a ii > j i a ij, i Jacobi Gauss-Seidel (i) + + A strictly diagonally dominant! (ii) + + Consider spectral radius of the iteration matrix! (iii) + Consider spectral radius of the iteration matrix! (ii) Jacobi: 3 M = D A = 1 1 C = M (A M) = 1 1 = 1 eigenvalues of C: λ 1,,3 = ρ(c) = < 1 convergence (ii) Gauss-Seidel: equivalent to Jacobi iteration convergence M = D A + L A = D A Attention: The positive eigenvalues of A are not sufficient for convergence of Gauss-Seidel (or Jacobi) because it holds: positive eigenvalues But, for symmetric matrices, it follows in general positive definite. symmetric + positive eigenvalues positive definite.
3 Numerical Programming I (for CSE) Tutorial 1: Iterative Methods, page 3 (iii) Jacobi: M = D A = I, C = M 1 (A M) = I A = 1 1 From characterisitc polynomial: det(c λi) = λ 3 = it follows, that eigenvalues of C: λ 1,,3 = ρ(c) = < 1 convergence (iii) Gauss-Seidel: 1 M = D A + L A = 1 1, 1 1 C = M 1 (A M) = = 3 1 eigenvalues of C: λ 1 =, λ,3 = ρ(c) = 1 no convergence ) Multilevel Methods Given the one-dimensional stationary heat equation T (x) x = s(x), (1) with x the spatial variable, T the temperature value, and s a source term. We look for a solution T (x) of equation (1) in the spatial domain Ω = [, π], with boundary conditions T () = T (π) =, and a source term s(x) = sin(x). To solve this problem numerically, a central finite difference scheme is applied, transforming equation (1) into the discrete algebraic form T i+1 + T i T i 1 h = s i. () Subindex i =,..., n + 1 denotes the discrete position x i = ih, where h is the discretization interval h = π/(n + 1), and n is the number of internal grid points, as denoted in Figure 1. The discrete unknowns T i are located at the internal nodes, boundary values are prescribed for T = T n+1 =. In the following, you will perform one coarse-grid correction of the fine-grid level solution. T 1 T i =T(x i ) T n 3.14 x h Figure 1: Spatial grid of discretized heat equation. a) For n f = 7, setup the fine-level system of equations A f x f = b f of the discrete problem given by equation (), the boundary conditions and source term. How can the resulting system matrix be classified? b) We do not aim at using the matrix in an explicit form, but to evaluate the equations only individually per unknown T i, i.e., node-wise, in an iterative manner. What advantages does this have? Using a Gauss-Seidel iterative scheme, write down the equation for updating one unknown, using index k and k+1 to denote the old and new iteration state of involved variables.
4 Numerical Programming I (for CSE) Tutorial 1: Iterative Methods, page 4 P c) P d) P e) P f) P g) Implement the Gauss-Seidel method for the given problem. Perform iterations on the fine grid (so called pre-smoothing iterations) starting from an initial guess of Ti =, i. For comparison, compute the numerical solution of the fine-grid system using Matlab s builtin direct equation solver and also derive the analytical solution of equation (1). Plot and explain the differences between the three solutions. Hint: The analytical solution is a simple trigonometric function. The direct solver solution is very close to it, while the smoothed solution (after two iterations) is about a quarter in magnitude of the others. To obtain a coarse-grid correction for the fine-grid approximation computed by the Gauss- Seidel method, we need to use the residual r f of the fine grid approximation. Derive and implement a node-wise evaluation of the residual. After computing the residual, setup a coarser mesh with h c = h f and restrict the computed residual to the coarse mesh by using injection, i.e., coarse-grid residual values are set by copying the values from coinciding fine-grid nodes. Hint: The residuals should be approximately as large as the expected errors for this example. On the coarse level, we now want to solve for the error A c e c = r c, to later use the coarse-grid error as correction on the fine-grid level. Setup the coarse-grid equation system, assuming zero error at the boundaries. Solve the system by using Matlab s builtin direct solver. Prolongate the coarse grid error e c up to the fine grid e f by linear interpolation, i.e., coinciding nodes have same value and intermediate fine grid nodes get an average of left and right coarse grid node values (remember the zero boundaries on the coarse grid). Correct the smoothed solution by x f = x f e f. Plot and compare the corrected solution to the other solutions obtained in c). Hint: After the correction, the solution should approximately at 8% of the direct solution. Perform post-smoothing iterations with the Gauss-Seidel method implemented in c). Why do you think is a post-smoothing useful? Plot and compare the post-smoothed solution to the other solutions obtained in c) and f). Solution: a) A f x f = 64 π h = π 8, 1 h = 64 π T 1 T T 3 T 4 T 5 T 6 T 7 sin(1π/8) sin(π/8) sin(3π/8) = b f = sin(4π/8) sin(5π/8) sin(6π/8) sin(7π/8) The system matrix A f is a tri-diagonal matrix, i.e., a band-matrix with one main, upper, and lower diagonal. It is also sparse, i.e., most entries are zero, and symmetric. b) The advantage of not setting up an explicit matrix lies in not needing the storage for it. This allows for efficient dynamic grid adaptivity (e.g., grid changes and refinements) and possibly enables to solve for a larger amount of unknowns.
5 Numerical Programming I (for CSE) Tutorial 1: Iterative Methods, page 5 Gauss-Seidel update rule: Ti+1 k k+1 + Ti Ti 1 k+1 h = s i Ti k+1 = 1 (h s i Ti 1 k+1 T i+1) k Ti k+1 = 1 (h sin(x i ) Ti 1 k+1 T i+1) k P c) P d) Show and execute Matlab codes. Analytical solution: Exact solution of the original continuous problem. Direct solver solution: Numerical solution of the discretized problem and, thus, including a discretization and rounding error. Smoothed solution: Only the inital guess with smoothed error. The smoothening allows to represent the residual on a coarser grid, necessary for the coarse grid correction. Residual in matrix notation: r f = b f A f x f Component wise (omitting subindex f): n r i = b i a ij x j = sin(x i ) + 1 h (T i 1 T i + T i+1 ) j=1 Number of nodes on coarse level: n c = π h c 1 = π h f 1 = Injection of residuals: (draw sketch of injection) π π/(n f + 1) 1 = n f = = 3. r f, r c = r f,4 r f,6 P e) P f) P g) Coarse-grid correction system: A c e c = 16 1 π Execute Matlab codes e c,1 e c, e c,3 Prolongation: (draw sketch of linear interpolation).5e c,1 e c,1.5(e c,1 + e c, ) e f = e c,.5(e c, + e c,3 ) e c,3.5(e c,3 ) Execute matlab codes. Execute Matlab codes. = r c = Post-smoothing removes high-frequency errors potentially introduced by the prolongation. r f, r f,4 r f,6
6. Iterative Methods for Linear Systems. The stepwise approach to the solution...
6 Iterative Methods for Linear Systems The stepwise approach to the solution Miriam Mehl: 6 Iterative Methods for Linear Systems The stepwise approach to the solution, January 18, 2013 1 61 Large Sparse
More informationNumerical Programming I (for CSE)
Technische Universität München WT / Fakultät für Mathematik Prof. Dr. M. Mehl B. Gatzhammer February 7, Numerical Programming I (for CSE) Repetition ) Floating Point Numbers and Rounding a) Let f : R R
More informationKasetsart University Workshop. Multigrid methods: An introduction
Kasetsart University Workshop Multigrid methods: An introduction Dr. Anand Pardhanani Mathematics Department Earlham College Richmond, Indiana USA pardhan@earlham.edu A copy of these slides is available
More informationChapter 12: Iterative Methods
ES 40: Scientific and Engineering Computation. Uchechukwu Ofoegbu Temple University Chapter : Iterative Methods ES 40: Scientific and Engineering Computation. Gauss-Seidel Method The Gauss-Seidel method
More information1. Fast Iterative Solvers of SLE
1. Fast Iterative Solvers of crucial drawback of solvers discussed so far: they become slower if we discretize more accurate! now: look for possible remedies relaxation: explicit application of the multigrid
More informationPreliminary/Qualifying Exam in Numerical Analysis (Math 502a) Spring 2012
Instructions Preliminary/Qualifying Exam in Numerical Analysis (Math 502a) Spring 2012 The exam consists of four problems, each having multiple parts. You should attempt to solve all four problems. 1.
More information9. Iterative Methods for Large Linear Systems
EE507 - Computational Techniques for EE Jitkomut Songsiri 9. Iterative Methods for Large Linear Systems introduction splitting method Jacobi method Gauss-Seidel method successive overrelaxation (SOR) 9-1
More informationStabilization and Acceleration of Algebraic Multigrid Method
Stabilization and Acceleration of Algebraic Multigrid Method Recursive Projection Algorithm A. Jemcov J.P. Maruszewski Fluent Inc. October 24, 2006 Outline 1 Need for Algorithm Stabilization and Acceleration
More informationMath/Phys/Engr 428, Math 529/Phys 528 Numerical Methods - Summer Homework 3 Due: Tuesday, July 3, 2018
Math/Phys/Engr 428, Math 529/Phys 528 Numerical Methods - Summer 28. (Vector and Matrix Norms) Homework 3 Due: Tuesday, July 3, 28 Show that the l vector norm satisfies the three properties (a) x for x
More informationand let s calculate the image of some vectors under the transformation T.
Chapter 5 Eigenvalues and Eigenvectors 5. Eigenvalues and Eigenvectors Let T : R n R n be a linear transformation. Then T can be represented by a matrix (the standard matrix), and we can write T ( v) =
More informationBackground. Background. C. T. Kelley NC State University tim C. T. Kelley Background NCSU, Spring / 58
Background C. T. Kelley NC State University tim kelley@ncsu.edu C. T. Kelley Background NCSU, Spring 2012 1 / 58 Notation vectors, matrices, norms l 1 : max col sum... spectral radius scaled integral norms
More informationTheory of Iterative Methods
Based on Strang s Introduction to Applied Mathematics Theory of Iterative Methods The Iterative Idea To solve Ax = b, write Mx (k+1) = (M A)x (k) + b, k = 0, 1,,... Then the error e (k) x (k) x satisfies
More informationAMS526: Numerical Analysis I (Numerical Linear Algebra)
AMS526: Numerical Analysis I (Numerical Linear Algebra) Lecture 24: Preconditioning and Multigrid Solver Xiangmin Jiao SUNY Stony Brook Xiangmin Jiao Numerical Analysis I 1 / 5 Preconditioning Motivation:
More informationIntroduction to Scientific Computing
(Lecture 5: Linear system of equations / Matrix Splitting) Bojana Rosić, Thilo Moshagen Institute of Scientific Computing Motivation Let us resolve the problem scheme by using Kirchhoff s laws: the algebraic
More informationChapter 7 Iterative Techniques in Matrix Algebra
Chapter 7 Iterative Techniques in Matrix Algebra Per-Olof Persson persson@berkeley.edu Department of Mathematics University of California, Berkeley Math 128B Numerical Analysis Vector Norms Definition
More informationNumerical Solution Techniques in Mechanical and Aerospace Engineering
Numerical Solution Techniques in Mechanical and Aerospace Engineering Chunlei Liang LECTURE 3 Solvers of linear algebraic equations 3.1. Outline of Lecture Finite-difference method for a 2D elliptic PDE
More informationAlgebraic Multigrid Preconditioners for Computing Stationary Distributions of Markov Processes
Algebraic Multigrid Preconditioners for Computing Stationary Distributions of Markov Processes Elena Virnik, TU BERLIN Algebraic Multigrid Preconditioners for Computing Stationary Distributions of Markov
More informationSolving PDEs with Multigrid Methods p.1
Solving PDEs with Multigrid Methods Scott MacLachlan maclachl@colorado.edu Department of Applied Mathematics, University of Colorado at Boulder Solving PDEs with Multigrid Methods p.1 Support and Collaboration
More informationMultigrid Methods and their application in CFD
Multigrid Methods and their application in CFD Michael Wurst TU München 16.06.2009 1 Multigrid Methods Definition Multigrid (MG) methods in numerical analysis are a group of algorithms for solving differential
More informationCAAM 454/554: Stationary Iterative Methods
CAAM 454/554: Stationary Iterative Methods Yin Zhang (draft) CAAM, Rice University, Houston, TX 77005 2007, Revised 2010 Abstract Stationary iterative methods for solving systems of linear equations are
More informationLecture 18 Classical Iterative Methods
Lecture 18 Classical Iterative Methods MIT 18.335J / 6.337J Introduction to Numerical Methods Per-Olof Persson November 14, 2006 1 Iterative Methods for Linear Systems Direct methods for solving Ax = b,
More informationSolving Linear Systems
Solving Linear Systems Iterative Solutions Methods Philippe B. Laval KSU Fall 207 Philippe B. Laval (KSU) Linear Systems Fall 207 / 2 Introduction We continue looking how to solve linear systems of the
More informationIterative Methods for Solving A x = b
Iterative Methods for Solving A x = b A good (free) online source for iterative methods for solving A x = b is given in the description of a set of iterative solvers called templates found at netlib: http
More informationAlgebraic Multigrid as Solvers and as Preconditioner
Ò Algebraic Multigrid as Solvers and as Preconditioner Domenico Lahaye domenico.lahaye@cs.kuleuven.ac.be http://www.cs.kuleuven.ac.be/ domenico/ Department of Computer Science Katholieke Universiteit Leuven
More informationQuestion: Given an n x n matrix A, how do we find its eigenvalues? Idea: Suppose c is an eigenvalue of A, then what is the determinant of A-cI?
Section 5. The Characteristic Polynomial Question: Given an n x n matrix A, how do we find its eigenvalues? Idea: Suppose c is an eigenvalue of A, then what is the determinant of A-cI? Property The eigenvalues
More informationCOURSE Iterative methods for solving linear systems
COURSE 0 4.3. Iterative methods for solving linear systems Because of round-off errors, direct methods become less efficient than iterative methods for large systems (>00 000 variables). An iterative scheme
More information6. Multigrid & Krylov Methods. June 1, 2010
June 1, 2010 Scientific Computing II, Tobias Weinzierl page 1 of 27 Outline of This Session A recapitulation of iterative schemes Lots of advertisement Multigrid Ingredients Multigrid Analysis Scientific
More informationLinear Algebra review Powers of a diagonalizable matrix Spectral decomposition
Linear Algebra review Powers of a diagonalizable matrix Spectral decomposition Prof. Tesler Math 283 Fall 2016 Also see the separate version of this with Matlab and R commands. Prof. Tesler Diagonalizing
More informationHere is an example of a block diagonal matrix with Jordan Blocks on the diagonal: J
Class Notes 4: THE SPECTRAL RADIUS, NORM CONVERGENCE AND SOR. Math 639d Due Date: Feb. 7 (updated: February 5, 2018) In the first part of this week s reading, we will prove Theorem 2 of the previous class.
More informationComputational Linear Algebra
Computational Linear Algebra PD Dr. rer. nat. habil. Ralf Peter Mundani Computation in Engineering / BGU Scientific Computing in Computer Science / INF Winter Term 2017/18 Part 3: Iterative Methods PD
More informationSolving Symmetric Indefinite Systems with Symmetric Positive Definite Preconditioners
Solving Symmetric Indefinite Systems with Symmetric Positive Definite Preconditioners Eugene Vecharynski 1 Andrew Knyazev 2 1 Department of Computer Science and Engineering University of Minnesota 2 Department
More informationBootstrap AMG. Kailai Xu. July 12, Stanford University
Bootstrap AMG Kailai Xu Stanford University July 12, 2017 AMG Components A general AMG algorithm consists of the following components. A hierarchy of levels. A smoother. A prolongation. A restriction.
More informationComputational Linear Algebra
Computational Linear Algebra PD Dr. rer. nat. habil. Ralf-Peter Mundani Computation in Engineering / BGU Scientific Computing in Computer Science / INF Winter Term 2018/19 Part 4: Iterative Methods PD
More informationNumerical Methods - Numerical Linear Algebra
Numerical Methods - Numerical Linear Algebra Y. K. Goh Universiti Tunku Abdul Rahman 2013 Y. K. Goh (UTAR) Numerical Methods - Numerical Linear Algebra I 2013 1 / 62 Outline 1 Motivation 2 Solving Linear
More informationNotes for CS542G (Iterative Solvers for Linear Systems)
Notes for CS542G (Iterative Solvers for Linear Systems) Robert Bridson November 20, 2007 1 The Basics We re now looking at efficient ways to solve the linear system of equations Ax = b where in this course,
More informationLinear Algebra review Powers of a diagonalizable matrix Spectral decomposition
Linear Algebra review Powers of a diagonalizable matrix Spectral decomposition Prof. Tesler Math 283 Fall 2018 Also see the separate version of this with Matlab and R commands. Prof. Tesler Diagonalizing
More informationUp to this point, our main theoretical tools for finding eigenvalues without using det{a λi} = 0 have been the trace and determinant formulas
Finding Eigenvalues Up to this point, our main theoretical tools for finding eigenvalues without using det{a λi} = 0 have been the trace and determinant formulas plus the facts that det{a} = λ λ λ n, Tr{A}
More informationRemark By definition, an eigenvector must be a nonzero vector, but eigenvalue could be zero.
Sec 6 Eigenvalues and Eigenvectors Definition An eigenvector of an n n matrix A is a nonzero vector x such that A x λ x for some scalar λ A scalar λ is called an eigenvalue of A if there is a nontrivial
More informationSolving Linear Systems
Solving Linear Systems Iterative Solutions Methods Philippe B. Laval KSU Fall 2015 Philippe B. Laval (KSU) Linear Systems Fall 2015 1 / 12 Introduction We continue looking how to solve linear systems of
More informationLecture Note 7: Iterative methods for solving linear systems. Xiaoqun Zhang Shanghai Jiao Tong University
Lecture Note 7: Iterative methods for solving linear systems Xiaoqun Zhang Shanghai Jiao Tong University Last updated: December 24, 2014 1.1 Review on linear algebra Norms of vectors and matrices vector
More informationReview Notes for Linear Algebra True or False Last Updated: January 25, 2010
Review Notes for Linear Algebra True or False Last Updated: January 25, 2010 Chapter 3 [ Eigenvalues and Eigenvectors ] 31 If A is an n n matrix, then A can have at most n eigenvalues The characteristic
More informationIterative Methods and Multigrid
Iterative Methods and Multigrid Part 1: Introduction to Multigrid 1 12/02/09 MG02.prz Error Smoothing 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 Initial Solution=-Error 0 10 20 30 40 50 60 70 80 90 100 DCT:
More information1 Number Systems and Errors 1
Contents 1 Number Systems and Errors 1 1.1 Introduction................................ 1 1.2 Number Representation and Base of Numbers............. 1 1.2.1 Normalized Floating-point Representation...........
More informationNumerical Analysis: Solving Systems of Linear Equations
Numerical Analysis: Solving Systems of Linear Equations Mirko Navara http://cmpfelkcvutcz/ navara/ Center for Machine Perception, Department of Cybernetics, FEE, CTU Karlovo náměstí, building G, office
More informationIterative Methods. Splitting Methods
Iterative Methods Splitting Methods 1 Direct Methods Solving Ax = b using direct methods. Gaussian elimination (using LU decomposition) Variants of LU, including Crout and Doolittle Other decomposition
More informationIterative Methods and Multigrid
Iterative Methods and Multigrid Part 1: Introduction to Multigrid 2000 Eric de Sturler 1 12/02/09 MG01.prz Basic Iterative Methods (1) Nonlinear equation: f(x) = 0 Rewrite as x = F(x), and iterate x i+1
More informationEXAMPLES OF CLASSICAL ITERATIVE METHODS
EXAMPLES OF CLASSICAL ITERATIVE METHODS In these lecture notes we revisit a few classical fixpoint iterations for the solution of the linear systems of equations. We focus on the algebraic and algorithmic
More informationIterative Methods for Ax=b
1 FUNDAMENTALS 1 Iterative Methods for Ax=b 1 Fundamentals consider the solution of the set of simultaneous equations Ax = b where A is a square matrix, n n and b is a right hand vector. We write the iterative
More informationGoal: to construct some general-purpose algorithms for solving systems of linear Equations
Chapter IV Solving Systems of Linear Equations Goal: to construct some general-purpose algorithms for solving systems of linear Equations 4.6 Solution of Equations by Iterative Methods 4.6 Solution of
More informationINTRODUCTION TO MULTIGRID METHODS
INTRODUCTION TO MULTIGRID METHODS LONG CHEN 1. ALGEBRAIC EQUATION OF TWO POINT BOUNDARY VALUE PROBLEM We consider the discretization of Poisson equation in one dimension: (1) u = f, x (0, 1) u(0) = u(1)
More informationx x2 2 + x3 3 x4 3. Use the divided-difference method to find a polynomial of least degree that fits the values shown: (b)
Numerical Methods - PROBLEMS. The Taylor series, about the origin, for log( + x) is x x2 2 + x3 3 x4 4 + Find an upper bound on the magnitude of the truncation error on the interval x.5 when log( + x)
More informationIntroduction to Scientific Computing II Multigrid
Introduction to Scientific Computing II Multigrid Miriam Mehl Slide 5: Relaxation Methods Properties convergence depends on method clear, see exercises and 3), frequency of the error remember eigenvectors
More informationCAAM 335 Matrix Analysis
CAAM 335 Matrix Analysis Solutions to Homework 8 Problem (5+5+5=5 points The partial fraction expansion of the resolvent for the matrix B = is given by (si B = s } {{ } =P + s + } {{ } =P + (s (5 points
More informationNUMERICAL COMPUTATION IN SCIENCE AND ENGINEERING
NUMERICAL COMPUTATION IN SCIENCE AND ENGINEERING C. Pozrikidis University of California, San Diego New York Oxford OXFORD UNIVERSITY PRESS 1998 CONTENTS Preface ix Pseudocode Language Commands xi 1 Numerical
More informationScientific Computing II
Technische Universität München ST 008 Institut für Informatik Dr. Miriam Mehl Scientific Computing II Final Exam, July, 008 Iterative Solvers (3 pts + 4 extra pts, 60 min) a) Steepest Descent and Conjugate
More informationRemark 1 By definition, an eigenvector must be a nonzero vector, but eigenvalue could be zero.
Sec 5 Eigenvectors and Eigenvalues In this chapter, vector means column vector Definition An eigenvector of an n n matrix A is a nonzero vector x such that A x λ x for some scalar λ A scalar λ is called
More informationMultigrid absolute value preconditioning
Multigrid absolute value preconditioning Eugene Vecharynski 1 Andrew Knyazev 2 (speaker) 1 Department of Computer Science and Engineering University of Minnesota 2 Department of Mathematical and Statistical
More informationThe Conjugate Gradient Method
The Conjugate Gradient Method Classical Iterations We have a problem, We assume that the matrix comes from a discretization of a PDE. The best and most popular model problem is, The matrix will be as large
More informationAIMS Exercise Set # 1
AIMS Exercise Set #. Determine the form of the single precision floating point arithmetic used in the computers at AIMS. What is the largest number that can be accurately represented? What is the smallest
More informationAlgebra C Numerical Linear Algebra Sample Exam Problems
Algebra C Numerical Linear Algebra Sample Exam Problems Notation. Denote by V a finite-dimensional Hilbert space with inner product (, ) and corresponding norm. The abbreviation SPD is used for symmetric
More informationLinear algebra II Tutorial solutions #1 A = x 1
Linear algebra II Tutorial solutions #. Find the eigenvalues and the eigenvectors of the matrix [ ] 5 2 A =. 4 3 Since tra = 8 and deta = 5 8 = 7, the characteristic polynomial is f(λ) = λ 2 (tra)λ+deta
More informationElliptic Problems / Multigrid. PHY 604: Computational Methods for Physics and Astrophysics II
Elliptic Problems / Multigrid Summary of Hyperbolic PDEs We looked at a simple linear and a nonlinear scalar hyperbolic PDE There is a speed associated with the change of the solution Explicit methods
More informationMath 471 (Numerical methods) Chapter 3 (second half). System of equations
Math 47 (Numerical methods) Chapter 3 (second half). System of equations Overlap 3.5 3.8 of Bradie 3.5 LU factorization w/o pivoting. Motivation: ( ) A I Gaussian Elimination (U L ) where U is upper triangular
More informationDEN: Linear algebra numerical view (GEM: Gauss elimination method for reducing a full rank matrix to upper-triangular
form) Given: matrix C = (c i,j ) n,m i,j=1 ODE and num math: Linear algebra (N) [lectures] c phabala 2016 DEN: Linear algebra numerical view (GEM: Gauss elimination method for reducing a full rank matrix
More informationAn Introduction to Algebraic Multigrid (AMG) Algorithms Derrick Cerwinsky and Craig C. Douglas 1/84
An Introduction to Algebraic Multigrid (AMG) Algorithms Derrick Cerwinsky and Craig C. Douglas 1/84 Introduction Almost all numerical methods for solving PDEs will at some point be reduced to solving A
More informationCLASSICAL ITERATIVE METHODS
CLASSICAL ITERATIVE METHODS LONG CHEN In this notes we discuss classic iterative methods on solving the linear operator equation (1) Au = f, posed on a finite dimensional Hilbert space V = R N equipped
More informationDepartment of Mathematics California State University, Los Angeles Master s Degree Comprehensive Examination in. NUMERICAL ANALYSIS Spring 2015
Department of Mathematics California State University, Los Angeles Master s Degree Comprehensive Examination in NUMERICAL ANALYSIS Spring 2015 Instructions: Do exactly two problems from Part A AND two
More informationModeling and Simulation with ODE for MSE
Zentrum Mathematik Technische Universität München Prof. Dr. Massimo Fornasier WS 6/7 Dr. Markus Hansen Sheet 7 Modeling and Simulation with ODE for MSE The exercises can be handed in until Wed, 4..6,.
More informationMath Introduction to Numerical Analysis - Class Notes. Fernando Guevara Vasquez. Version Date: January 17, 2012.
Math 5620 - Introduction to Numerical Analysis - Class Notes Fernando Guevara Vasquez Version 1990. Date: January 17, 2012. 3 Contents 1. Disclaimer 4 Chapter 1. Iterative methods for solving linear systems
More informationEigenvalue and Eigenvector Homework
Eigenvalue and Eigenvector Homework Olena Bormashenko November 4, 2 For each of the matrices A below, do the following:. Find the characteristic polynomial of A, and use it to find all the eigenvalues
More informationIntroduction to Applied Linear Algebra with MATLAB
Sigam Series in Applied Mathematics Volume 7 Rizwan Butt Introduction to Applied Linear Algebra with MATLAB Heldermann Verlag Contents Number Systems and Errors 1 1.1 Introduction 1 1.2 Number Representation
More informationAMS526: Numerical Analysis I (Numerical Linear Algebra for Computational and Data Sciences)
AMS526: Numerical Analysis I (Numerical Linear Algebra for Computational and Data Sciences) Lecture 19: Computing the SVD; Sparse Linear Systems Xiangmin Jiao Stony Brook University Xiangmin Jiao Numerical
More informationPowerPoints organized by Dr. Michael R. Gustafson II, Duke University
Part 3 Chapter 10 LU Factorization PowerPoints organized by Dr. Michael R. Gustafson II, Duke University All images copyright The McGraw-Hill Companies, Inc. Permission required for reproduction or display.
More informationComputationally, diagonal matrices are the easiest to work with. With this idea in mind, we introduce similarity:
Diagonalization We have seen that diagonal and triangular matrices are much easier to work with than are most matrices For example, determinants and eigenvalues are easy to compute, and multiplication
More informationPreface to the Second Edition. Preface to the First Edition
n page v Preface to the Second Edition Preface to the First Edition xiii xvii 1 Background in Linear Algebra 1 1.1 Matrices................................. 1 1.2 Square Matrices and Eigenvalues....................
More information4. Linear transformations as a vector space 17
4 Linear transformations as a vector space 17 d) 1 2 0 0 1 2 0 0 1 0 0 0 1 2 3 4 32 Let a linear transformation in R 2 be the reflection in the line = x 2 Find its matrix 33 For each linear transformation
More information9.1 Preconditioned Krylov Subspace Methods
Chapter 9 PRECONDITIONING 9.1 Preconditioned Krylov Subspace Methods 9.2 Preconditioned Conjugate Gradient 9.3 Preconditioned Generalized Minimal Residual 9.4 Relaxation Method Preconditioners 9.5 Incomplete
More information6. Iterative Methods: Roots and Optima. Citius, Altius, Fortius!
Citius, Altius, Fortius! Numerisches Programmieren, Hans-Joachim Bungartz page 1 of 1 6.1. Large Sparse Systems of Linear Equations I Relaxation Methods Introduction Systems of linear equations, which
More informationJordan Journal of Mathematics and Statistics (JJMS) 5(3), 2012, pp A NEW ITERATIVE METHOD FOR SOLVING LINEAR SYSTEMS OF EQUATIONS
Jordan Journal of Mathematics and Statistics JJMS) 53), 2012, pp.169-184 A NEW ITERATIVE METHOD FOR SOLVING LINEAR SYSTEMS OF EQUATIONS ADEL H. AL-RABTAH Abstract. The Jacobi and Gauss-Seidel iterative
More informationSolving Linear Systems of Equations
November 6, 2013 Introduction The type of problems that we have to solve are: Solve the system: A x = B, where a 11 a 1N a 12 a 2N A =.. a 1N a NN x = x 1 x 2. x N B = b 1 b 2. b N To find A 1 (inverse
More informationLab 1: Iterative Methods for Solving Linear Systems
Lab 1: Iterative Methods for Solving Linear Systems January 22, 2017 Introduction Many real world applications require the solution to very large and sparse linear systems where direct methods such as
More informationNumerical Analysis Preliminary Exam 10 am to 1 pm, August 20, 2018
Numerical Analysis Preliminary Exam 1 am to 1 pm, August 2, 218 Instructions. You have three hours to complete this exam. Submit solutions to four (and no more) of the following six problems. Please start
More informationBindel, Fall 2016 Matrix Computations (CS 6210) Notes for
1 Iteration basics Notes for 2016-11-07 An iterative solver for Ax = b is produces a sequence of approximations x (k) x. We always stop after finitely many steps, based on some convergence criterion, e.g.
More informationTMA4125 Matematikk 4N Spring 2017
Norwegian University of Science and Technology Institutt for matematiske fag TMA15 Matematikk N Spring 17 Solutions to exercise set 1 1 We begin by writing the system as the augmented matrix.139.38.3 6.
More informationIntroduction and Stationary Iterative Methods
Introduction and C. T. Kelley NC State University tim kelley@ncsu.edu Research Supported by NSF, DOE, ARO, USACE DTU ITMAN, 2011 Outline Notation and Preliminaries General References What you Should Know
More informationTherefore, A and B have the same characteristic polynomial and hence, the same eigenvalues.
Similar Matrices and Diagonalization Page 1 Theorem If A and B are n n matrices, which are similar, then they have the same characteristic equation and hence the same eigenvalues. Proof Let A and B be
More informationCourse Notes: Week 1
Course Notes: Week 1 Math 270C: Applied Numerical Linear Algebra 1 Lecture 1: Introduction (3/28/11) We will focus on iterative methods for solving linear systems of equations (and some discussion of eigenvalues
More informationSolutions Problem Set 8 Math 240, Fall
Solutions Problem Set 8 Math 240, Fall 2012 5.6 T/F.2. True. If A is upper or lower diagonal, to make det(a λi) 0, we need product of the main diagonal elements of A λi to be 0, which means λ is one of
More informationA MULTIGRID ALGORITHM FOR. Richard E. Ewing and Jian Shen. Institute for Scientic Computation. Texas A&M University. College Station, Texas SUMMARY
A MULTIGRID ALGORITHM FOR THE CELL-CENTERED FINITE DIFFERENCE SCHEME Richard E. Ewing and Jian Shen Institute for Scientic Computation Texas A&M University College Station, Texas SUMMARY In this article,
More informationDefinition (T -invariant subspace) Example. Example
Eigenvalues, Eigenvectors, Similarity, and Diagonalization We now turn our attention to linear transformations of the form T : V V. To better understand the effect of T on the vector space V, we begin
More informationMAT 1302B Mathematical Methods II
MAT 1302B Mathematical Methods II Alistair Savage Mathematics and Statistics University of Ottawa Winter 2015 Lecture 19 Alistair Savage (uottawa) MAT 1302B Mathematical Methods II Winter 2015 Lecture
More informationToday s class. Linear Algebraic Equations LU Decomposition. Numerical Methods, Fall 2011 Lecture 8. Prof. Jinbo Bi CSE, UConn
Today s class Linear Algebraic Equations LU Decomposition 1 Linear Algebraic Equations Gaussian Elimination works well for solving linear systems of the form: AX = B What if you have to solve the linear
More informationScientific Computing: An Introductory Survey
Scientific Computing: An Introductory Survey Chapter 11 Partial Differential Equations Prof. Michael T. Heath Department of Computer Science University of Illinois at Urbana-Champaign Copyright c 2002.
More information7.3 The Jacobi and Gauss-Siedel Iterative Techniques. Problem: To solve Ax = b for A R n n. Methodology: Iteratively approximate solution x. No GEPP.
7.3 The Jacobi and Gauss-Siedel Iterative Techniques Problem: To solve Ax = b for A R n n. Methodology: Iteratively approximate solution x. No GEPP. 7.3 The Jacobi and Gauss-Siedel Iterative Techniques
More informationEigenvalues and Eigenvectors
5 Eigenvalues and Eigenvectors 5.2 THE CHARACTERISTIC EQUATION DETERMINANATS nn Let A be an matrix, let U be any echelon form obtained from A by row replacements and row interchanges (without scaling),
More informationLinear Algebra- Final Exam Review
Linear Algebra- Final Exam Review. Let A be invertible. Show that, if v, v, v 3 are linearly independent vectors, so are Av, Av, Av 3. NOTE: It should be clear from your answer that you know the definition.
More informationMath 3191 Applied Linear Algebra
Math 9 Applied Linear Algebra Lecture 9: Diagonalization Stephen Billups University of Colorado at Denver Math 9Applied Linear Algebra p./9 Section. Diagonalization The goal here is to develop a useful
More informationComparison of V-cycle Multigrid Method for Cell-centered Finite Difference on Triangular Meshes
Comparison of V-cycle Multigrid Method for Cell-centered Finite Difference on Triangular Meshes Do Y. Kwak, 1 JunS.Lee 1 Department of Mathematics, KAIST, Taejon 305-701, Korea Department of Mathematics,
More informationSection 1.7: Properties of the Leslie Matrix
Section 1.7: Properties of the Leslie Matrix Definition: A matrix A whose entries are nonnegative (positive) is called a nonnegative (positive) matrix, denoted as A 0 (A > 0). Definition: A square m m
More informationMath Spring 2011 Final Exam
Math 471 - Spring 211 Final Exam Instructions The following exam consists of three problems, each with multiple parts. There are 15 points available on the exam. The highest possible score is 125. Your
More information