  This paper considers decentralized consensus optimization problems where
different summands of a global objective function are available at nodes of a
network that can communicate with neighbors only. The proximal method of
multipliers is considered as a powerful tool that relies on proximal primal
descent and dual ascent updates on a suitably defined augmented Lagrangian. The
structure of the augmented Lagrangian makes this problem non-decomposable,
which precludes distributed implementations. This problem is regularly
addressed by the use of the alternating direction method of multipliers. The
exact second order method (ESOM) is introduced here as an alternative that
relies on: (i) The use of a separable quadratic approximation of the augmented
Lagrangian. (ii) A truncated Taylor's series to estimate the solution of the
first order condition imposed on the minimization of the quadratic
approximation of the augmented Lagrangian. The sequences of primal and dual
variables generated by ESOM are shown to converge linearly to their optimal
arguments when the aggregate cost function is strongly convex and its gradients
are Lipschitz continuous. Numerical results demonstrate advantages of ESOM
relative to decentralized alternatives in solving least squares and logistic
regression problems.
