Numerical Methods Qualification Exam Problems and Solutions (University of Maryland)/January 2008

Problem 1Edit

Consider the system  . The GMRES method starts with a point   and normalizes the residual   so that   has 2-norm one. It then constructs orthonormal Krylov bases   satisfying


where   is a   upper Hessenberg matrix. One then looks for an approximation to   of the form


choosing   so that   is minimized, where   is the usual Euclidean norm.

Problem 1aEdit

Show that   minimizes  .

Solution 1aEdit

We wish to show that  


Problem 1bEdit

Suppose we choose to solve the least squares problem in part a for   by the method of orthogonal triangularization (QR). What is the order of the floating point operation for this method. Give reasons.

Solution 1bEdit

We would like to transform  , a   upper Hessenberg matrix, into QR form.

The cost is on the order of   from the cost of Given's Rotations and backsolving.

Given's Rotations CostEdit

We need   Given's Rotation multiplies to zero out each of the   subdiagonal entries and hence transform   into upper triangular form  ,

Each successive Given's Rotations multiply on an upper Hessenberg matrix requires four fewer multiplies because each previous subdiagonal entry has been zeroed out by a Given's Rotation multiply.

Hence the cost of   Given's Rotations multiplies is


Back Solving CostEdit

  is a   upper triangular matrix with last row zero. Hence, we need to backsolve   upper triangular rows.


Problem 2Edit

We wish to approximate the integral  

Problem 2aEdit

State the composite trapezoidal rule   for approximating   on a uniform partitioning of width  , and give a formula for the error   that is in a form suitable for extrapolation.

Solution 2aEdit

The composite trapezoidal rule is


The error is


where  .

Derivation of Composite Trapezoidal ErrorEdit

The local error, the error on one interval, is


Observe that


which implies


Hence, the Intermediate Value Theorem implies there exists a   such that


Multiplying both sides of the equation by  ,


Using this relationship, we have


Derivation of Local ErrorEdit

The error in polynomial interpolation can be found by using the following theorem:

 Assume   exists on   and     interpolates   at  .  
 Then there is a   (  is dependent on  ) such that 
 where   lies in  ,   ,   

Applying the theorem yields,




Since   is the start of the interval,   is always positive. Conversely, since   is the end of the interval,   is always negative. Hence,   is always of one sign. Hence from the mean value theorem of integration, there exists a   such that


Note that   is a constant and does not depend on  .

Integrating  , yields,


Problem 2bEdit

Use the error formula to derive a new quadrature rule obtained by performing one step of extrapolation on the composite trapezoidal rule. What is this rule, and how does its error depend on  ? You may assume here that   is as smooth as you need it to be.

Solution 2bEdit



The error for the composite trapezoidal rule at   points in   is


With   points, there are twice as many intervals, but the intervals are half as wide. Hence, the error for the composite trapezoidal rule at   points in   is


We can eliminate the   term by choosing using an appropriate linear combination of   and  . This gives a new error rule with  error.


Substituting our equations for   and   on the left had side gives



If we call our new rule   we have

  whose error is on the order of  

Problem 3Edit

Consider the shifted QR iteration for computing a 2 x 2 matrix  : starting with  , compute


where   is a scalar shift.

Problem 3aEdit



specify the orthogonal matrix   used to perform this step when Givens rotations are used. The matrix should be described in terms of the entries of the shifted matrix.

Solution 3aEdit

Let   be the  -shifted matrix of   i.e.


  is an orthogonal 2x2 Given's rotations matrix.  's entries are chosen such that when   is pre-multiplied against the 2x2 matrix  ,   will zero out the (2,1) entry of   and scale  's remaining three entries i.e.


where   denotes calculable scalar values we are not interested in and   is our desired upper triangular matrix sought by the QR algorithm.

Since   is orthogonal, the above equation implies


The Given's rotation   is given by




Taking the transpose of   yields


Problem 3bEdit

Suppose  , a small number, and  . Demonstrate that with an appropriate shift  , the (2,1)-entry of   is of magnitude  . What does this suggest about the convergence rate of the QR iteration?

Solution 3bEdit

From hypothesis and part (a),


Let   be the (2,1) entry of  . Using the above equation, we can find   by finding the inner product of the second row of   and first column   and adding the (2,1) entry of   i.e.


We need to find the value of   so we need to calculate  .

From hypothesis and the orthogonality of  , we have


From  , we can find its (2,2) entry   by using inner products.


Now that we have   we can calculate   by using appropriate substitutions


since   is a small number.

Let our shift  . Then the above equation yields,





Hence we have shown that   is of order  .

If   is small, the QR convergence rate will be quadratic.