MATLAB's Optimization Toolbox tackles wide variety of solvers such as linear programming, mixed-integer linear programming, quadratic programming, nonlinear optimization. In this tutorial, we learn how to use simulated annealing to find the minimum of a function. The test function is the well known Rosenbrock function.

fig 1

Fig.1. Two dimensional Rosenbrock function along x-y plane.

Tutorial files

It is easiest to start with the tutorial command. In the command prompt, type

 $ tutorial tutorial-matlab-SimulatedAnnealing # Copies input and script files to the directory tutorial-matlab-SimulatedAnnealing.

This will create a directory tutorial-matlab-SimulatedAnnealing. Inside the directory, you will see the following files

SA_Opt.m               # matlab script - simulated annealing optimization of the function `simple_objective.m`
simple_objective.m     # matlab script - defines the actual objective function
SA_Opt                 # matlab compiled binary
SA_Opt.submit          # Condor job description file
SA_Opt.sh          # Executable file
Log/                   # Directory to copy standard output, error and log files from condor jobs.

MATLAB script - Objective function

Lets take a look at the objective function that takes an argument of an array x. The size of the array is two.

function y = simple_objective(x)
     y = (1-x(1))^2 + 100*(x(2)-x(1)^2)^2;

The objective function is defined in the matlab file simple_objective.m.

MATLAB script - Optimization

The simulated annealing script calls the objective function and optimizes via simulannealbnd.

%Simulated Annealing optimization of a given function
function SA_Optimization(fnumber)
    filenumber = num2str(fnumber);
    outfilename = sprintf ( '%s%s%s', 'rosen-sa-opt', filenumber, '.dat' );
    fileID = fopen(outfilename,'w');

    ObjectiveFunction = @simple_objective;
    for n = 1:1:5
        rx = rand(1:2)*100.0;
        X0 = [2.0 2.0] + rx;   % Starting point
        [x,fval,exitFlag,output] = simulannealbnd(ObjectiveFunction,X0) %
        fprintf(fileID,'f= %12.6f  x= %9.5f   y= %9.5f x0= %9.5f  y0=%9.5f\n', fval, x, X0 );

In the above script, the optimization is repeated for five times with random initial conditions.

MATLAB runtime execution

As mentioned in the [lesson on basics of MATLAB compilation] (https://support.opensciencegrid.org/support/solutions/articles/5000660751-basics-of-compiled-matlab-applications-hello-world-example), we need to compile the matlab script on a machine with license. At present, we don't have license for matlab on OSG-Conect. On a machine with matlab license, invoke the compiler mcc. It is important to turn off all graphical options (-nodisplay), disable Java (-nojvm), and instruct MATLAB to run this program as a single-threaded application (-singleCompThread). The flag -m means c language translation during compilation.

mcc -m -R -singleCompThread -R -nodisplay -R -nojvm SA_Opt.m

would produce the files: SA_Opt, run_SA_Opt.sh, mccExcludedFiles.log and readme.txt. The file SA_Opt is the compiled binary file that we would like to run on OSG Connect.

Job execution and submission files

Let us take a look at SA_Opt.submit file:

Universe = vanilla                          # One OSG Connect vanilla, the prefered job universe is "vanilla"

Executable =  SA_Opt.sh    # Job execution file which is transfered to worker machine
Arguments = $(Process)     #  process ID passed as an argument
transfer_input_files = SA_Opt               # list of file(s) need be transfered to the remote worker machine

Output = Log/job.$(Process).out⋅            # standard ouput 
Error =  Log/job.$(Process).err             # standard error
Log =    Log/job.$(Process).log             # log information about job execution

requirements = HAS_MODULES == True

queue 10                                   # Submit 10  jobs

The above job description instructs condor to submit 10 jobs. Each job would start with different random intial conditions.

The executable is a wrapper⋅ script SA_Opt.sh

source /cvmfs/oasis.opensciencegrid.org/osg/modules/lmod/current/init/bash
module load matlab/2014b
chmod +x SA_Opt
./SA_Opt $1

that loads the module matlab/2014b and executes the MATLAB compiled binary SA_Opt. The only required argument is a numerical⋅label that would be attached with the name of the output file.

Job submision

We submit the job using condor_submit command as follows

$ condor_submit SA_Opt.submit //Submit the condor job description file "SA_Opt.submit"

Now you have submitted an ensemble of 10 jobs. The jobs should be finished quickly (less than an hour). You can check the status of the submitted job by using the condor_q command as follows

$ condor_q username  # The status of the job is printed on the screen. Here, username is your login name.

Each job produce rosen-sa-opt$(Process).dat file, where $(Process) is the process ID that runs from 0 to 9.

Post process

After all jobs finished, we want to gather the output data. The script post-script.bash gathers the output values and numerically sort them according to function values.

$ post-script.bash⋅

Getting Help

For assistance or questions, please email the OSG User Support team at user-support@opensciencegrid.org or visit the help desk and community forums.


This page was updated on Dec 19, 2018 at 07:02 from tutorials/tutorial-matlab-SimulatedAnnealing/README.md.