780.20: 1094 Session 10
Handouts: Pang excerpt, pendulum power spectrum graphs,
printouts of multifit_test.cpp and
In this session,
we'll take a look at nonlinear least-squares
fitting with GSL.
Your goals for today (and probably Assignment 4):
- Wrap us as much of the Session 9 interpolation tasks as you
can do in the first half hour.
- Try out nonlinear minimization on a demonstration problem.
- Try out nonlinear least-squares fitting on a demonstration
- Think about
applying nonlinear minimization to a real physics
problem (from the Pang excerpt). [We'll finish this in a later
Please work in pairs (more or less).
The instructors will bounce around 1094 and answer questions.
Continue with Session 9
Budget your time so that you get through "GSL Differential Equation
Solver" and "GSL Interpolation Routines" in the first half hour.
If you have time, start "Cubic Splining"; don't worry about finishing
it, as we'll do the rest in Assignment 4.
Multidimensional Minimization with GSL Routines
The basic problem is to find a minimum of the scalar function
f(xvec) [scalar means that the result of evaluating the function
is just a number] where xvec = (x_0, x_1, ... , x_N) is an
N-dimensional vector. This is a much harder problem for N > 1 than
for N=1. The gsl library offers several algorithms; the best one to
use depends on the problem to be solved. Therefore, it is useful to
be able to switch between different choices to compare results. Gsl
makes this easy. Here we'll try a sample problem. For more details,
see the online gsl manual under "Multidimensional Minimization".
The routines used here find local minima only, and only one at a time.
They have no way of determining whether a minima is a global one or
not (we'll return to this point later).
- Look at the test code "multimin_test.cpp" and compare it to the
online gsl documentation under "Multidimensional Minimization".
Identify the steps in the minimization process.
- Create a multimin_test project, add multimin_test.cpp,
compile and run it.
Your results may differ from what is in the comments of the code.
Adjust the program so that your answer is good to 10-6
What is it that is found to that accuracy? (E.g., is it the positions
of the minimum or something else?)
- Modify the function minimized so it is a three-dimensional
paraboloid with your choice of minimum. Change the code so that the
dimension of xvec is a parameter. (Be sure to change ALL of the
relevant functions.) Verify that the minimum is still found.
- What algorithm is used initially to do the minimization?
Modify the code to try a couple of the other algorithms.
Which is "best" for this problem?
- In Session 14, we'll return to try to
reproduce the results illustrated in Fig. 4.2 of the
Pang handout (stable structures of
Take a look at that now to see if
you have any immediate questions.
Nonlinear Least-Squares Fitting with GSL Routines
Using the nonlinear least-squares fitting routines from gsl is similar
to using the gsl minimizer (and involves a special case of minimization).
Your main job is to figure out from the online documentation and this
example from gsl (only slightly modified from what you would find in the
documentation) how to use the routines.
Here we briefly explore the sample
problem, which also illustrates how to create a "noisy" (i.e.,
realistic) test case.
The model is that of a weighted exponential with a constant background:
y = A e-lambda t + b
You'll generate pseudo-data at a series of time steps ti
(in practice, ti is taken to be 0, 1, 2, ...). Noise is
added to each yi in the form a random number distributed like
a gaussian with a given standard deviation sigmai.
We'll revisit the gsl functions that generate this random distribution in the
next session; for now just accept that it works.
- Take a look at the multifit_test.cpp
code (there is a printout), make a multifit_test project
and run it, and figure
out the flow of the program.
What is the fitting function to be minimized?
What role does sigmai play?
- What is the "Jacobian" for? (Remember to check the online
- Modify the code so that you can plot both the initial data and
the fit curve using gnuplot. Look up how to add error bars for the
data in gnuplot. [Hint: Try "help set style", "help errors", and
"help plot errorbars" for
- The covariance matrix of the best-fit parameters can be used to
extract information about how good the fit is.
How is it used in the
program to estimate the uncertainties in the fit parameters? How do these
uncertainties scale with the number of time-steps used in the fit?
How do they scale with the magnitude of the noise? (I.e., if you
change the "amplitude" of the gaussian noise, how do the errors in the
fit parameters change?)
- Modify the function by adding a fourth parameter and try to fit
(and plot!) again. For example, you could change "b" to "b sin(omega
t)" with omega another parameter, or "A" to "A cos(omega t)". Your
780.20: 1094 Session 10.
Last modified: 07:58 pm, February 12, 2007.