Relaxation Methods - Computing and Numerical Methods - Exam, Exams of Mathematical Methods for Numerical Analysis and Optimization

Main points of this exam paper are: Relaxation Methods, Gauss Seidel Method, Numerical Methods, Suitable Example, Multiple Roots, Newton's Forward Difference, Newton Cotes Formulae

Typology: Exams

2012/2013

Uploaded on 04/13/2013

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Cork Institute of Technology
Bachelor of Engineering (Honours) in Mechanical Engineering
– Stage 2
(EMECH_8_Y2)
Autumn 2008
Computing and Numerical Methods
(Time: 3 Hours)
Instructions
Answer any FOUR questions.
All questions carry equal marks
Examiners: Dr. R. Sheehy
Mr. P. Clarke
Prof. M. Gilchrist
Q1. (a) Describe the Gauss Seidel Method for solving a system of linear equations.
Explain the use of relaxation methods to enhance convergence. (8 Marks)
(b) Write a program Gauss-Elim which uses the Gauss Elimination Method with partial
pivoting for solving a system of linear equations. (12 Marks)
(c) Illustrate using a suitable example an ill-conditioned system and list some techniques
for improving the solution. (5 Marks)
Q2. (a) Describe any two of the following methods for obtaining roots of an equation
(i) Newton-Raphson (ii) Bisection (iii) False-Position. (6 Marks)
(b) Explain the terms convergence and stability as applied to numerical methods for
obtaining roots and show that
() ()
()
()
1
xf
x f.xf
2
'
"
is a necessary condition for
convergence of Newton Raphson method. (8 Marks)
(c) Write a program for locating single roots using any of the methods in part (a).
(7 Marks)
(d) Illustrate, using a suitable example an equation with multiple roots. Outline any
numerical method for obtaining multiple roots. (4 Marks)
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Cork Institute of Technology

Bachelor of Engineering (Honours) in Mechanical Engineering

– Stage 2

(EMECH_8_Y2)

Autumn 2008

Computing and Numerical Methods

(Time: 3 Hours)

InstructionsAnswer any FOUR questions. All questions carry equal marks

Examiners: Dr. R. SheehyMr. P. Clarke Prof. M. Gilchrist

Q1. (a) Describe the Gauss Seidel Method for solving a system of linear equations. Explain the use of relaxation methods to enhance convergence. (8 Marks) (b) Write a program Gauss-Elim which uses the Gauss Elimination Method with partial pivoting for solving a system of linear equations. (12 Marks) (c) Illustrate using a suitable example an ill-conditioned system and list some techniques for improving the solution. (5 Marks)

Q2. (a) Describe any two of the following methods for obtaining roots of an equation (i) Newton-Raphson (ii) Bisection (iii) False-Position. (6 Marks) (b) Explain the terms convergence and stability as applied to numerical methods for

obtaining roots and show that f^ ( )(fx '( ).^ fx")( ) 2 x 〈 1

 (^) is a necessary condition for

convergence of Newton Raphson method. (8 Marks) (c) Write a program for locating single roots using any of the methods in part (a). (7 Marks) (d) Illustrate, using a suitable example an equation with multiple roots. Outline any numerical method for obtaining multiple roots. (4 Marks)

Q3. (a) Using Newton's Forward Difference Interpolating Polynomial with equally spaced intervals derive an expression for Trapazoidal Rule and Truncation error in a single interval. Hence show that:

a^ b = − + = +

N i i N N

f x f x f x f xdx b a 2

1 (^01)

with Error E K = −^12 ( b − Na 3 )^3 ∑ iN = 1 f '( α i ) (8 Marks)

(b) Outline the general structure of a program for Numerical Integration using any Newton Cotes formulae. (5 Marks) (c) Using the error term in part (a) show how Richardson's Extrapolation combines two integral estimates I(h 1 ) and I(h 2 ) to yield the improved estimate of the integral

I = I ( h 2 )+( h 1 / h^12 ) 2 − 1  ( I ( h 2 )− I ( h 1 )) (7 Marks)

(d) Explain Romberg integration (5 Marks)

Q4. (a) Briefly describe the terms (i) Interpolation (ii) Extrapolation. (6 Marks) (b) State the formula for Newton's Interpolating Polynomial fn (x) of degree n. Derive this formula for the case n = 2 (Quadratic Interpolation). (6 Marks) (c) The points (1,0), (4,1.386), (6,1.791) lie on the curve f(x) = ln x. Fit a 2nd^ order Newton's Interpolating polynomial to the data and hence calculate ln 2. (6 Marks) (d) Use the additional data point (5,1.609) to estimate the error for the 2 nd^ order polynomial in (c). (7 Marks)