  Decomposition techniques for linear programming are difficult to extend to
conic optimization problems with general non-polyhedral convex cones because
the conic inequalities introduce an additional nonlinear coupling between the
variables. However in many applications the convex cones have a partially
separable structure that allows them to be characterized in terms of simpler
lower-dimensional cones. The most important example is sparse semidefinite
programming with a chordal sparsity pattern. Here partial separability derives
from the clique decomposition theorems that characterize positive semidefinite
and positive-semidefinite-completable matrices with chordal sparsity patterns.
The paper describes a decomposition method that exploits partial separability
in conic linear optimization. The method is based on Spingarn's method for
equality constrained convex optimization, combined with a fast interior-point
method for evaluating proximal operators.
