  We develop a second order primal-dual method for optimization problems in
which the objective function is given by the sum of a strongly convex twice
differentiable term and a possibly nondifferentiable convex regularizer. After
introducing an auxiliary variable, we utilize the proximal operator of the
nonsmooth regularizer to transform the associated augmented Lagrangian into a
function that is once, but not twice, continuously differentiable. The saddle
point of this function corresponds to the solution of the original optimization
problem. We employ a generalization of the Hessian to define second order
updates on this function and prove global exponential stability of the
corresponding differential inclusion. Furthermore, we develop a globally
convergent customized algorithm that utilizes the primal-dual augmented
Lagrangian as a merit function. We show that the search direction can be
computed efficiently and prove quadratic/superlinear asymptotic convergence. We
use the $\ell_1$-regularized model predictive control problem and the problem
of designing a distributed controller for a spatially-invariant system to
demonstrate the merits and the effectiveness of our method.
