# SuperSCS: fast and accurate large-scale conic optimization

## TL;DR

SuperSCS introduces a novel, efficient method for large-scale convex conic problems by integrating SuperMann with Douglas-Rachford splitting on a self-dual embedding, enhancing speed and accuracy.

## Contribution

It combines SuperMann and Douglas-Rachford splitting on a self-dual embedding to improve large-scale conic optimization solving capabilities.

## Key findings

- Faster convergence on large-scale problems
- High accuracy in solutions
- Effective handling of infeasibility and unboundedness

## Abstract

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.