  This paper provides examples of various synchronous and asynchronous signal
processing systems for performing optimization, utilizing the framework and
elements developed in a preceding paper. The general strategy in that paper was
to perform a linear transformation of stationarity conditions applicable to a
class of convex and nonconvex optimization problems, resulting in algorithms
that operate on a linear superposition of the associated primal and dual
decision variables. The examples in this paper address various specific
optimization problems including the LASSO problem, minimax-optimal filter
design, the decentralized training of a support vector machine classifier, and
sparse filter design for acoustic equalization. Where appropriate, multiple
algorithms for solving the same optimization problem are presented,
illustrating the use of the underlying framework in designing a variety of
distinct classes of algorithms. The examples are accompanied by numerical
simulation and a discussion of convergence.
