# SuperSCS: fast and accurate large-scale conic optimization

arXiv: https://arxiv.org/abs/1903.06477 · HTML: https://mlsanity.com/paper/1903.06477/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Mar 2019.

## Built on by (most cited first, 3 of 3)

- [Learning to Warm-Start Fixed-Point Optimization Algorithms](https://mlsanity.com/paper/2309.07835/) (2023)
- [Efficient differentiable quadratic programming layers: an ADMM approach](https://mlsanity.com/paper/2112.07464/) (2021)
- [SCQPTH: an efficient differentiable splitting method for convex quadratic programming](https://mlsanity.com/paper/2308.08232/) (2023)

## Abstract (arXiv)

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.