  The aim of this paper is to study the convergence of the primal-dual dynamics
pertaining to Support Vector Machines (SVM). The optimization routine, used for
determining an SVM for classification, is first formulated as a dynamical
system. The dynamical system is constructed such that its equilibrium point is
the solution to the SVM optimization problem. It is then shown, using passivity
theory, that the dynamical system is global asymptotically stable. In other
words, the dynamical system converges onto the optimal solution asymptotically,
irrespective of the initial condition. Simulations and computations are
provided for corroboration.
