  We present SuperSCS: a fast and accurate method for solving large-scale
convex conic problems. SuperSCS combines the SuperMann algorithmic framework
with the Douglas-Rachford splitting which is applied on the homogeneous
self-dual embedding of conic optimization problems: a model for conic
optimization problems which simultaneously encodes the optimality conditions
and infeasibility/unboundedness certificates for the original problem.
SuperMann allows the use of fast quasi-Newtonian directions such as a modified
restarted Broyden-type direction and Anderson's acceleration.
