Hi, that's what the plot on the left says. You can reproduce it by means of the following MATLAB code (thanks to Mike Croucher's blog):

[x y] = meshgrid( linspace(-3,3,50), linspace(-5,5,50) );
z = exp(-x.^2-0.5*y.^2).*cos(4*x) + exp(-3*((x+0.5).^2+0.5*y.^2));
idx = ( abs(z)>0.001 );
z(idx) = 0.001 * sign(z(idx));
patch(surf2patch(surf(x,y,z)), 'FaceColor','interp');
set(gca, 'Box','on', ...
'XColor',[.3 .3 .3], 'YColor',[.3 .3 .3], 'ZColor',[.3 .3 .3], 'FontSize',8)
title('$e^{-x^2 - \frac{y^2}{2}}\cos(4x) + e^{-3((x+0.5)^2+\frac{y^2}{2})}$', ...
'Interpreter','latex', 'FontSize',12)
colormap( [flipud(cool);cool] )
camlight headlight, lighting phong

World Community Grid

The World Community Grid (WCG) mission is to create the largest public computing grid benefiting humanity. I donate the time my computer is turned on, but is idle, to WCG's projects, that is to public and not-for-profit organizations to use in humanitarian research that might otherwise not be completed due to the high cost of the computer infrastructure required in the absence of a public grid. Anybody can join, and I suggest to do so. It takes seconds to register and download the required software. Click here to know more.
Below is a widget displaying in real time my WCG username, the time my pc has been devoted to WCG's projects computations and the related projects.


  1. Ah, what would be life without Non Sequitur, "the Wiley Miller's wry look at the absurdities of everyday life"?!
    pic 1
    pic 2

  2. PhD, Piled Higher & Deeper, a grad student comic strip by Jorge Cham. If you are into research you cannot miss this! Procrastinate with purpose and pride!!
    pic 1
    pic 2

MATLAB links

(first of all some self-celebration)
SDE Toolbox: by myself, simulates and estimates stochastic differential equations. Warning: implemented inferential methods are rather outdated and the toolbox is no more developed.

Lightspeed Toolbox: this library by T. Minka provides highly optimized versions of primitive functions such as repmat.

MATLAB tips and tricks: a useful collection of articles on good programming practice & computational tricks.

MATLAB utilities: an impressive collection of functions by P.J. Acklam.

Statistics Toolbox: this toolbox by A. Holtsberg provides several functions for statistical computations.

Good programming practice: an instructive thread from the newsgroup comp.soft-sys.matlab.

Another link on good programming practice: avoiding the use of global variables.

Optimization 1: a collection of iterative optimization methods by C.T. Kelley.

Optimization 2: CONDOR by F. Vanden Berghen, a very nice direct optimizator using trust regions.

Optimization 3: SolvOpt by A. Kuntsevich and F. Kappel, an algorithm for smooth and non-smooth optimization problems.

Optimization 4: another collection of optimizers by H. Bruun Nielsen.

Manuals 1: "Numerical Computing with MATLAB" by the creator of MATLAB C. Moler. The individual chapters of this book are downloadable in pdf format.

Manuals 2: "MATLAB array manipulation tips and tricks" by P.J. Acklam. A fundamental reference to exploit the MATLAB calculus capabilities, write fast programs and vectorize code. Highly recommended. A companion toolbox implementing the ideas suggested in the manual is available.

Manuals 3: "Writing fast MATLAB code" by P. Getreuer. Another recommended reference.

Manuals 4: "MATLAB tips and tricks" by G. Peyré. A list of useful tips and tricks with concise pieces of code and comments.

Manuals 5: "Writing MATLAB/CMEX Code" by P. Getreuer. To combine the power of MATLAB and C.

Markov Chain Monte Carlo

The following description and the beautiful picture below are from Jürgen Brauer's website: the picture shows 3 MCMC chains used for searching the minimum of the 3D Rosenbrock optimizer test function (which is non-convex). Note that only the red points are used at the end. The other points are discarded, since they stem from the "burn-in" period, where we start at a random point in the 3D state space, which does not have to do something with the underlying probability density function.