  Nonlinear optimization problems are found at the heart of real-time
operations of critical infrastructures. These problems are computationally
challenging because they embed complex physical models that exhibit space-time
dynamics. We propose modeling these problems as graph-structured optimization
problems, and illustrate how their structure can be exploited at the modeling
level (for parallelizing function/derivative computations) and at the solver
level (for parallelizing linear algebra operations). Specifically, we present a
restricted additive Schwarz scheme that enables flexible decomposition of
complex graph structures within an interior-point algorithm. The proposed
approach is implemented as a general-purpose nonlinear programming solver that
we call MadNLP.jl; this Julia-based solver is interfaced to the graph-based
modeling package Plasmo.jl. The efficiency of this framework is demonstrated
via problems arising in transient gas network optimization and multi-period AC
optimal power flow. We show that our framework accelerates the solution
(compared to off-the-shelf tools) by over 300%; specifically, solution times
are reduced from 72.36 sec to 23.84 sec for the gas problem and from 515.81 sec
to 149.45 sec for the power flow problem.
