  Optimal experimental designs are probability measures with finite support
enjoying an optimality property for the computation of least squares
estimators. We present an algorithm for computing optimal designs on finite
sets based on the long-time asymptotics of the gradient flow of the
log-determinant of the so called information matrix. We prove the convergence
of the proposed algorithm, and provide a sharp estimate on the rate its
convergence. Numerical experiments are performed on few test cases using the
new matlab package OptimalDesignComputation.
