SIXTY STUDY QUESTIONS TO THE COURSE NUMERISK BEHANDLING AV DIFFERENTIALEKVATIONER I



Similar documents
Introduction to the Finite Element Method

Numerical Methods for Differential Equations

Numerical Methods for Differential Equations

NUMERICAL ANALYSIS PROGRAMS

AN INTRODUCTION TO NUMERICAL METHODS AND ANALYSIS

Fourth-Order Compact Schemes of a Heat Conduction Problem with Neumann Boundary Conditions

5.4 The Heat Equation and Convection-Diffusion

Numerical Analysis Lecture Notes

Finite Difference Approach to Option Pricing

Mean value theorem, Taylors Theorem, Maxima and Minima.

Numerical Analysis Introduction. Student Audience. Prerequisites. Technology.

MATH 425, PRACTICE FINAL EXAM SOLUTIONS.

Lecture Notes to Accompany. Scientific Computing An Introductory Survey. by Michael T. Heath. Chapter 10

1. First-order Ordinary Differential Equations

Numerical methods for American options

Second Order Linear Partial Differential Equations. Part I

Numerical Analysis An Introduction

Høgskolen i Narvik Sivilingeniørutdanningen STE6237 ELEMENTMETODER. Oppgaver

Numerical Methods for Engineers

Chapter 5. Methods for ordinary differential equations. 5.1 Initial-value problems

SOLVING LINEAR SYSTEMS

Advanced CFD Methods 1

Numerical Methods I Solving Linear Systems: Sparse Matrices, Iterative Methods and Non-Square Systems

16.1 Runge-Kutta Method

G.A. Pavliotis. Department of Mathematics. Imperial College London

Notes for AA214, Chapter 7. T. H. Pulliam Stanford University

General Framework for an Iterative Solution of Ax b. Jacobi s Method

SOLUTION OF Partial Differential Equations. (PDEs)

1 Finite difference example: 1D implicit heat equation

Reaction diffusion systems and pattern formation

The Heat Equation. Lectures INF2320 p. 1/88

Nonlinear Algebraic Equations Example

MEL 807 Computational Heat Transfer (2-0-4) Dr. Prabal Talukdar Assistant Professor Department of Mechanical Engineering IIT Delhi

POISSON AND LAPLACE EQUATIONS. Charles R. O Neill. School of Mechanical and Aerospace Engineering. Oklahoma State University. Stillwater, OK 74078

College of the Holy Cross, Spring 2009 Math 373, Partial Differential Equations Midterm 1 Practice Questions

Using the Theory of Reals in. Analyzing Continuous and Hybrid Systems

Feature Commercial codes In-house codes

The Steepest Descent Algorithm for Unconstrained Optimization and a Bisection Line-search Method

Chapter 9 Partial Differential Equations

On computer algebra-aided stability analysis of dierence schemes generated by means of Gr obner bases

Part II: Finite Difference/Volume Discretisation for CFD

Numerical Solution of Differential Equations

Nonlinear Algebraic Equations. Lectures INF2320 p. 1/88

Diffusion: Diffusive initial value problems and how to solve them

An Introduction to Partial Differential Equations

Domain Decomposition Methods. Partial Differential Equations

(Quasi-)Newton methods

Stability Analysis for Systems of Differential Equations

Lecture 13 Linear quadratic Lyapunov theory

N 1. (q k+1 q k ) 2 + α 3. k=0

Homework 2 Solutions

Numerical Methods for Solving Systems of Nonlinear Equations

CITY UNIVERSITY LONDON. BEng Degree in Computer Systems Engineering Part II BSc Degree in Computer Systems Engineering Part III PART 2 EXAMINATION

FINITE DIFFERENCE METHODS

Optimization of Supply Chain Networks

Introduction to Algebraic Geometry. Bézout s Theorem and Inflection Points

Multigrid preconditioning for nonlinear (degenerate) parabolic equations with application to monument degradation

Iterative Solvers for Linear Systems

Inner Product Spaces

CHAPTER 2. Eigenvalue Problems (EVP s) for ODE s

Principles of Scientific Computing Nonlinear Equations and Optimization

Class Meeting # 1: Introduction to PDEs

MATH APPLIED MATRIX THEORY

3. Reaction Diffusion Equations Consider the following ODE model for population growth

Linear algebra and the geometry of quadratic equations. Similarity transformations and orthogonal matrices

7 Gaussian Elimination and LU Factorization

arxiv: v1 [quant-ph] 3 Mar 2016

x = + x 2 + x

Yousef Saad University of Minnesota Computer Science and Engineering. CRM Montreal - April 30, 2008

INTEGRAL METHODS IN LOW-FREQUENCY ELECTROMAGNETICS

Nonlinear normal modes of three degree of freedom mechanical oscillator

Linköping University Electronic Press

Examination paper for TMA4205 Numerical Linear Algebra

r (t) = 2r(t) + sin t θ (t) = r(t) θ(t) + 1 = 1 1 θ(t) Write the given system in matrix form x = Ax + f ( ) sin(t) x y z = dy cos(t)

A Brief Review of Elementary Ordinary Differential Equations

Parameter Estimation for Bingham Models

3.2 Sources, Sinks, Saddles, and Spirals

Code: MATH 274 Title: ELEMENTARY DIFFERENTIAL EQUATIONS

Vector and Matrix Norms

Introduction to Engineering System Dynamics

[1] Diagonal factorization

CS Software Engineering for Scientific Computing. Lecture 16: Particle Methods; Homework #4

Heavy Parallelization of Alternating Direction Schemes in Multi-Factor Option Valuation Models. Cris Doloc, Ph.D.

Inner products on R n, and more

Geometric evolution equations with triple junctions. junctions in higher dimensions

ORDINARY DIFFERENTIAL EQUATIONS

ME 7103: Theoretical Vibrations Course Notebook

GEC320 COURSE COMPACT. Four hours per week for 15 weeks (60 hours)

MAT 242 Test 2 SOLUTIONS, FORM T

An Introduction to Partial Differential Equations in the Undergraduate Curriculum

Lecture 8 February 4

Chapter 7 Nonlinear Systems

1 Error in Euler s Method

4 Lyapunov Stability Theory

List the elements of the given set that are natural numbers, integers, rational numbers, and irrational numbers. (Enter your answers as commaseparated

Transcription:

Lennart Edsberg, Nada, KTH Autumn 2008 SIXTY STUDY QUESTIONS TO THE COURSE NUMERISK BEHANDLING AV DIFFERENTIALEKVATIONER I Parameter values and functions occurring in the questions belowwill be exchanged at the exam. (4) 1. Given a system of ODE s ẏ = Ay, wherea is an n n-matrix. Give a sufficient condition that the analytical solution is bounded for all t>0. Will there be bounded solutions when the matrix A =...? (4) 2. For a nonlinear system of ODE s ẏ = f(y), what is meant by the critical points of the system? Give a sufficient condition that a critical point is stable. Are the critical point of the following system stable?... (4) 3. Given a nonlinear system of ODE s ẏ = f(y). Assume that the right hand side consists of differentiable functions. Describe a Newton s method for computing the critical points of the system. For the following system, choose an initial vector and make one iteration with Newton s method.... (3) 4. What does it mean that an n n matrix A is diagonalizable? Is the following matrix A diagonalizable?... (4) 5. For a system of ODE s ẏ = Ay, y(0) = y 0, the solution can be written as y(t) =e At y 0. What is meant by the matrix e At?Whatise At when A =...? (1) 6. For a system of ODE s ẏ = f(y) what is meant by an initial value problem? (4) 7. What is meant by the eigenvalues and the eigenvectors of an n n matrix A? The eigenvectors are roots of the characteristic equation. What is the characteristic equation of A? Which are the eigenvalues and eigenvectors for the matrix A =...? (4) 8. Given a difference equation y n+1 = a 1 y n +a 2 y n 1 +...+a k+1 y n k where a 1,a 2,...a k+1 are real. Formulate a sufficient condition that the sequence y n is bounded for all n. Is the solution bounded when n>0 for the following difference equation?... (4) 9. Given a difference equation on the form y n + 1 2 2 y n =0. Isthesolutiony n bounded for all n>0? (2) 10. Formulate the following 3rd order ODE as a system of three first order ODE s... (3) 11. Give the recursion formula when the Euler backward method with time step h is used on e.g. the ODE-problem y = y 3 + t, y(0) = 1. In each time step t n+1 a nonlinear 1

equation must be solved. Formulate that equation on the form F (y n+1 )=0andthe iteration formula when Newton s method is applied to this equation. (2) 12. What is meant by an explicit method for solving an initial value problem ẏ = f(y),y(0) = y 0 Give at least two examples (with their formulas) of explicit methods often used to solve such a problem. (2) 13. What is meant by an implicit method for solving an initial value problem ẏ = f(y),y(0) = y 0 Give at least two examples of implicit methods (with their formulas) often used to solve such a problem. (2) 14. What is meant by the order of accuracy for a method used to solve an initial value problem ẏ = f(y),y(0) = y 0? What is the order of the Euler forward method and the classical Runge-Kutta method? (2) 15. What is meant by automatic stepsize control for a method used to solve an initial value problem ẏ = f(y),y(0) = y 0? (4) 16. Why is the midpoint method not suited for ODE-systems where the eigenvalues of the jacobian are real and negative? Are there any ODE-systems for which the method could be suitable? (2) 17. Explain the difference between local error and global error for the explicit Euler method. (2) 18. An ODE-problem, initial value and boundary value problem, can be solved by a discretization method or an ansatz method. Give a brief description of what is meant by these two method classes. (4) 19. When an initial value problem ẏ = f(y),y(0) = y 0 is solved with a discretization method, what is meant by the stability area in the complex hq-plane for the method? Give a sketch of the stability area for the method... (2) 20. What is meant by a stiff system of ODE s ẏ = f(y)? (2)21. ForalinearsystemofODE sẏ = Ay, where the eigenvalues of A are λ 1,λ 2,...,λ n, when is the system stable? Assume that all eigenvalues are real and negative, when is the system stiff? 2

(2) 22. Describe some discretization methods that are suitable for stiff initial value ODE problems. (2) 23. Which is the computational problem when a stiff initial value ODE-system is solved with an explicit method? (3) 24. Given e.g. the following ODE-system ẏ = 100y + z, y(0) = 1, ż = 0.1z, z(0) = 1. For which values of the stepsize h is the Euler forward method stable? Same question for the Euler backward method. (4) 25. Given the vibration equation mẍ + cẋ + kx =0, x(0) = 0, ẋ(0) = v 0 Use scaling of x and t to formulate this ODE on dimensionless form. Determine scaling factors so that the scaled equation contains as fewparameters as possible. (2) 26. Describe the finite difference method used to solve a boundary value problem y + a(x)y + b(x)y = c(x),y (0) = a, y (1) + y(1) = b. (4) 27. Verify with Taylor expansion that the following two approximations are of second order, i.e. O(h 2 ). 1) y (x) (y(x + h) y(x h))/2h, 2)y (x) (y(x + h) 2y(x)+ y(x h))/h 2 (4) 28. Derive a second order difference approximation to y (4) (x) using the values y(x + 2h),y(x + h),y(x),y(x h) andy(x 2h). (4) 29. Derive a second order difference approximation to y (x) using the values y(x),y(x h) and y(x 2h). (2) 30. Given a second order ODE y = f(x, y, y ). Assume a Dirichlet boundary value is given in the left interval point y(0) = 1. Present two other ways in which a boundary contition can be given in the right boundary point. (4) 31. When a boundary value problem y = p(x)y +q(x)y+r(x),y(0) = 1,y(1) = 0 is solved with discretization based on the approximations y (x n ) (y n+1 2y n +y n 1 )/h 2 and y (x n ) (y n+1 y n 1 )/2h we obtain a linear system Ay = b of equations to be solved. Set up this system. Which special structure does the matrix A have? (2) 32. What is meant by a tridiagonal n n matrix A? The number of flops needed to solve a corresponding linear system of equations Ax = b can be expressed as O(n p ). What is the value of p? The number of bytes needed in the memory of a computer to store such a system can be expressed as O(n q ). What is the value of q? (2) 33. What is meant by a banded n n matrix A? Describe some way of storing such a matrix in a sparse way. Same question for a profile matrix. 3

(2) 34. What input and output data are suitable for a Matlab function solving a tridiagonal system of linear equations Ax = b if the goal is to save number of flops and computer memory? (4) 35. The boundary value problem y = f(x),y(0) = y(1) = 0 can be solved with an ansatz method based on Galerkin s method. Formulate the Galerkin method for this problem. (4) 36. Classify the following PDEs with respect to linearity (linear or nonlinear), order (first, second), type (elliptic, parabolic, hyperbolic): a) u t = u xx +u, b)u xx +2u yy =0,c)u t =(a(u)u x ) x,d)u t +uu x =0,e)u y = u x x+x (4) 37. Formulate the ODE-system u = Au + b when the Method of Lines is applied to the PDE-problem u t = u xx + u, t > 0, 0 x 1,u(0,t)=0,u(1,t)=1,u(x, 0) = x u xx is approximated by the central difference formula. (4) 38. Formulate the ODE-system u = Au + b when the Method of Lines is applied to the PDE-problem u t + u x =0,t>0, 0 x 1,u(0,t)=0,u(x, 0) = x u x is to be approximated by the backward Euler difference formula. (2) 39. What is meant by an upwind scheme for solving u t + au x =0,a>0? (2) 40. Derive a difference approximation and the corresponding stencil to e.g. the Laplace operator 2 u x 2 + 2 u y 2 + u x + u y (2) 41. What is the order of accuracy in t and x when the Method of Lines with central differences in the x-variable and the implicit Euler method is applied to e.g. the heat equation u t = u xx? (2) 42. Give a second order accurate approximation difference formula of (d(x)u x ) x. (2) 43. Describe the Crank-Nicolson method for solving the heat equation. 4

(2) 44. Formulate the Galerkin method for the elliptic problem u xx + u yy = f(x, y), (x, y) Ω u(x, y) =0, (x, y) Ω (2) 45. Formulate the Galerkin method for the parabolic problem u t = u xx,u(x, 0) = f(x),u(0,t)=u(1,t)=0 (2) 46. In an ansatz method for a 1D problem the solution u(x) is approximated by the linear expression u h (x) = α i ϕ i (x), where the basis functions ϕ i (x) can be chosen differently. Give a description of the roof-functions, i.e. the piecewise linear basis functions in x. (2) 47. In an ansatz method for a 2D problem the solution u(x, y) is approximated by the linear expression u h (x, y) = α i ϕ i (x, y), where the basis functions ϕ i (x, y) canbe chosen differently. Give a description of the pyramid-functions, i.e. the piecewise linear basis functions in x, y. (2) 48. When solving PDE-problems in 2D and 3D with difference or ansatz methods we are lead to solving large linear systems of equations Ax = b. What is meant by a direct method and an iterative method for solving Ax = b? Give examples by name of some direct methods and some iterative methods. (1) 49. What is meant by dissipation and dispersion when a conservation law is solved numerically? (2) 50. What is meant by fill-in when solving a sparse linear system of equations Ax = b with a direct method? (3) 51. What is meant by the Cholesky-factorization of a symmetric positive definite matrix A? Can the following matrix A be Cholesky factorized? (2) 52. If Ax = b is rewritten on the form x = Hx + c and the iteration x k+1 = Hx k + c is defined, what is the condition on H for convergence of the iterations to the solution of Ax = b? Also formulate a condition on H for fast convergence. (2) 53. What is meant by the steepest descent method for solving Ax = b, wherea is symmetric and positive definite? (4) 54. Describe preconditioning when used with the steepest descent method. Assume that the matrix A is symmetric and positive definite. 5

(3) 55. For a hyperbolic PDE in x and t the solution u(x, t) is constant along certain curves in the x, t-plane. What are these curves called? Give the analytic expression for these curves belonging to u t +2u x =0. (4) 56. Given the system of PDEs ( ) 0 1 u t + u 2 0 x =0 Is the system hyperbolic? Which are the characteristics? (3) 57. Given the systems of PDEs ( ) 0 1 u t + u 2 0 x =0 A solution is wanted on the interval 0 x 1, t 0. Suggest initial and boundary conditions that give a mathematically well posed problem. (2) 58. What is meant by a numerical boundary condition? (2) 59. Use Neumann analysis to verify that the upwind scheme is unstable when applied to u t = au x,a>0. (2) 60. What is meant by artificial diffusion? Why is that sometimes used when solving hyperbolic PDEs? 6