Description
Begin by downloading the in-class demo w02w GEerr.m from the Canvas page. Recall, from lecture, that
this demo produced the distribution of errors resulting from the Gaussian Elimination (GE) algorithm
with finite-precision arithmetic. The goal of this exercise is to understand the statistics of the growth of
truncation error as matrix size N increases.
As presented in the notes, a brief summary of the experimental method is:
• produce a random matrix [A],
• construct the right-side vector ~b = [A]~x0, where ~x0 is the N-component vector of all 1’s (using the
Matlab ones command),
• use Matlab’s backslash to get the GE solution ~x1 = [A]
~b,
• compute the finite-precision solution error, sol err = rms(~x1 − ~x0),
• compute the finite-precision residual error, res err = rms([A]~x1 −~b ),
• gather data for statistical analysis.
The rms command is Matlab is the Root-Mean-Squared (RMS) function, which converts the vector of
errors to a non-negative scalar number
rms(~x) = |~x|
N
where |~x| is the vector magnitude, which is then divided by the number of components of ~x — so it gives a
measure of the error per unknown (so we don’t grow the error when comparing to ever larger N values.)
Furthermore, by considering the log10 of the RMS error, it is basically converted into a number that
roughly indicates the first corrupted decimal place (as in its power of 10).
In exact arithmetic, we should expect the errors to be 0. However as seem in lecture, in the world of
floating-point arithmetic, even small matrix-size results in measurable non-zero errors. You will learn in
this exercise that, as N increases, so do the GE errors — and the goal of this assignment is to produce a
rough estimate for the matrix size N∗ at which the error resulting from GE becomes as large
as the solution itself.
Of course, it is not possible to do computations for matrices of size N∗
. Therefore, we will have to do
computations for a number of smaller N values, and use these to obtain a rough, but logical, estimate for
the value of N∗
.
Since the error computed for each GE depends on the random matrix A, we can determine what the
’typical’ error for matrix size N is by averaging over many experiments (different random matrices). If
we run Nex number of experiments, and call res err(k)
the errors obtained from the k
th experiment, we
can then define the average log10 error as
εres(N) = 1
Nex
X
Nex
k=1
log10(res err(k)
) = meank=1→Nex n
log10(res err(k)
)
o
.
We will take this error to be representative of the typical GE error for matrices of size N. Once you have
several of these εres(N) values, show them in a plot which you then base your estimate for N∗
.
DJM 1
MACM 316- D100 COMPUTING ASSIGNMENT 1
Your one-page computing report should include/address the following:
(a) Obtain values εres(N) for several different values of N. But to do this, YOU will need to decide on
the values for N and Nexp to be used. All of these choices will affect the quality of your estimate.
Clearly indicate which values you selected and discuss how you made your choices.
(b) Include a plot of the points (log10 N, εres(N)).
(c) Use your plot from (b) to argue for your value of an estimated value N∗ where εres(N∗
) ≈ 0. You
only need to present a rough order of magnitude here, and you should indicate how achievable, or
not, your value of N∗
is.
DJM 2
