  In this paper, we propose a distributed algorithm for solving large-scale
separable convex problems using Lagrangian dual decomposition and the
interior-point framework. By adding self-concordant barrier terms to the
ordinary Lagrangian, we prove under mild assumptions that the corresponding
family of augmented dual functions is self-concordant. This makes it possible
to efficiently use the Newton method for tracing the central path. We show that
the new algorithm is globally convergent and highly parallelizable and thus it
is suitable for solving large-scale separable convex problems.
