  Multi-agent systems are increasingly widespread in a range of application
domains, with optimization and learning underpinning many of the tasks that
arise in this context. Different approaches have been proposed to enable the
cooperative solution of these optimization and learning problems, including
first- and second-order methods, and dual (or Lagrangian) methods, all of which
rely on consensus and message-passing. In this article we discuss these
algorithms through the lens of non-expansive operator theory, providing a
unifying perspective. We highlight the insights that this viewpoint delivers,
and discuss how it can spark future original research.
