  This paper proposes methods for identification of large-scale networked
systems with guarantees that the resulting model will be contracting -- a
strong form of nonlinear stability -- and/or monotone, i.e. order relations
between states are preserved. The main challenges that we address are:
simultaneously searching for model parameters and a certificate of stability,
and scalability to networks with hundreds or thousands of nodes. We propose a
model set that admits convex constraints for stability and monotonicity, and
has a separable structure that allows distributed identification via the
alternating directions method of multipliers (ADMM). The performance and
scalability of the approach is illustrated on a variety of linear and
non-linear case studies, including a nonlinear traffic network with a
200-dimensional state space.
