6810: 1094 Session 14

In this session, we'll continue our survey of Monte Carlo computational methods with a look at autocorrelation and variational Monte Carlo applied to simple problems that are easily generalized.

Autocorrelation

When evaluating an average over Monte Carlo configurations, we want to skip the first n1 steps and then use data taken every n0 Monte Carlo steps. How do we determine how large to take n0 and n1? We'll use a simple integration problem to explore these issues, by integrating x2e-x2 from 0 to 10 and dividing by the integral of e-x2 (so this is like < x2 > with a probability distribution proportional to e-x2).

  1. Calculate the (normalized) integral in Mathematica for reference. Answer:
  2. Consider first random sampling as a review/recap of our discussions.
  3. Next consider the Metropolis algorithm. There are three parameters for you to adjust (besides the total number of iterations): "max_step", "initial_skip" and "skip".


  4. To figure out a good value for "skip", we'll calculate the "autocorrelation function". This is defined in the Session notes as equation (14.2). For us, "A" is "x2". Your task is to generate a figure like the one in the notes.
  5. Now that the Metropolis results are reliable, repeat step 2. above, but now for the Metropolis sampling. What do you find?

Variational Monte Carlo

We'll do a simple example of variational Monte Carlo to illustrate the basic idea. Generalizing to more complex systems is straightforward (but takes a lot longer to run!).

  1. Take a look at the "variational_SHO.cpp" code and see how it implements variational Monte Carlo for a one-dimensional harmonic oscillator using the VariationalMC class. Compile, link, and run it (there is a makefile). What is the trial wave function? Why is this a reasonable one to use?



  2. You'll be asked to supply a range and step size for the variational parameter "a". This will require some experimentation to make sure the minimum with respect to "a" is in the interval you select.
  3. The variational_SHO.dat file can be plotted in gnuplot. The plotfile "variational_SHO.plt" is provided to plot it with error bars. Try it out a few times each with 100, 1000, 10000 steps (attach a plot with a sample result). Does the graph make sense for a variational calculation? What about the error bars? How might you modify the code to find the minimum automatically (rather than graphically)?



  4. Does this code implement all the features explored in the "Autocorrelation" section? If not, how would you improve this code?




Checking for Bad Input Values

You may have noticed that the nonlinear pendulum codes with the menus went crazy if you accidently typed a letter or a symbol rather than an integer. Here we see how to avoid this.

  1. Use make_input_check to create input_check and run it. First try some integers and then -1 to see how it is supposed to work. Then restart it and put in a letter or symbol (or whatever you want). To stop it, use Ctrl-c.
  2. A fix is given in input_check_fixed (which has its own makefile). Try it out. Look at the printout or on the screen to see what was done. What was done to fix it?



6810: 1094 Session 14. Last modified: 03:17 pm, April 29, 2013.
furnstahl.1@osu.edu