1) Implement Gaussian elimination. Suggestion:

void gaussianElimination(Matrix_nxn *A, Vector_n *b,

Vector_n *sol);

(2) Implement Gaussian elimination with partial pivoting. Suggestion:

void gaussianEliminationPivoting(Matrix_nxn *A, Vector_n *b,

Vector_n *sol);

(3) Implement the QR decomposition using Gram-Schmidt. Suggestion:

void QR_gramSchmidt(Matrix_nxn *A,

Matrix_nxn *Q, Matrix_nxn *R);

(4) Implement the QR decomposition using Householder reflections. Suggestion:

void QR_householder(Matrix_nxn *A,

Matrix_nxn *Q, Matrix_nxn *R);

5) Implement solving a linear equation using QR decomposition. Suggestion:

void solve_QR(Matrix_nxn *Q, Matrix_nxn *R, Vector_n *b,

Vector_n *sol);

(6) Implement solving a linear equation using Gauss-Seidel iteration.
Suggestion:

void solve_GaussSeidel(Matrix_nxn *A, Vector_n *b,

double precision,

Vector_n *sol);

(7) Implement solving a linear equation using Jacobi iteration. Suggestion:

void solve_Jacobi(Matrix_nxn *A, Vector_n *b,

double precision,

Vector_n *sol);

You are free to use any programming language of your choice to implement the

algorithms. You are al lowed to use basic vector, matrix operations (addition,
multiplication ,

dot- products and norms ) provided by libraries and/or the programming language.

As far as notation of the project description is concerned, let

for any n-vector v and any n × n matrix A.

As a criterion to stop the ite ration algorithms I would suggest you to choose

either one of the following after computing x_{n+1} out of x_{n}

then stop. (The latter case is

counted as if the algorithm did not converge.)

then stop. (The latter case is counted

as if the algorithm did not converge.)

where ε is the relative error tolerance (e.g.
)
and N the maximum number

of iteration steps your are willing to spend (e.g.
).

The requested plots can be generated by whichever software you can use. It is

definitely not asked to write software for that part of the project.

## 2. Project – Part 1

For any matrix size n≥2 consider the n × n matrix

For matrix sizes

n = 3, 4, 5, . . . , 20

do

• Compute the QR decomposition of A using Gram-Schmidt and Householder.

• Check the quality of your result of Q by computing

for both decompositions.

• Check the quality of your result of Q and R by computing

for both decompositions.

Summarize your finding by plotting
against n

for both methods . Try to explain your results (a few sentences are enough).

## 3. Project – Part 2

For any matrix size n≥2 let A be the matrix of
equation (1), and let the vector

b be given by

For matrix sizes

n = 3, 4, 5, . . . 20

try to find the solution to Ax = b by using

(1) Gaussian elimination.

(2) Gaussian elimination with partial pivoting.

(3) Computing first a Q-R decomposition of A using Gram-Schmidt, and solving

Rx = Q^{T}b using only the back-substitution part of the Gaussian

elimination algorithm.

(4) Computing first a Q-R decomposition of A using Householder, and solving

Rx = Q^{T}b using only the back-substitution part of the Gaussian elimination algorithm.

(5) Using Jacobi iteration (e.g. start with x_{0} = 0)

(6) Using Gauss-Seidel iteration (e.g. start with x_{0} = 0)

Summarize your findings by plotting
against n for all the above

algorithms. Try to explain your result (in a few sentences).

## 4. Project – Part 3

For any matrix size n≥2 generate the matrix A and the
vector b via the following

algorithm (working C/C++ code, except for setting the matrix/vector elements).

int i, j, n;

double randomU, sum, value ;

/*

* setup the matrix

*/

randomU = 0.123456789;

for(i=0; i<n; i++) {

sum = 0.0;

for(j=0; j<i; j++) {

randomU = fmod( 3.0*randomU, 1.0);

value = 2.0*randomU -1.0;

ELEMENT_Matrix_nxn(A, i,j) = value;

sum+= fabs(value);

}

for(j=i+1; j<n; j++) {

randomU = fmod( 3.0*randomU, 1.0);

value = 2.0*randomU -1.0;

ELEMENT_Matrix_nxn(A, i,j) = value;

sum+= fabs(value);

}

if(randomU<0.5)

ELEMENT_Matrix_nxn(A, i,i) = sum;

else

ELEMENT_Matrix_nxn(A, i,i) = -sum;

}

/*

* setup the vector

*/

for(i=0; i<n; i++) {

randomU = fmod( 3.0*randomU, 1.0);

value = 2.0*randomU -1.0;

ELEMENT_Vector_n(b, i) = value;

}

For the matrix sizes

n = 2, 3, . . . , 20, 50, 100

find the solution to the equation Ax = b using the same algorithms as in part
2.

Summarize your results in two plots . One plot is
against n, the other

plot is the run time against n

In order to measure the run time you might want to solve the very same prob-

lem repeatedly (say 10^{5} or so) times. Also, if you are using a compiled language

( like C/C ++ or Java) you probably want to disable optimizations, as this extra

loop might be optimized away by the compiler (unless you know how to trick the

compiler).