Neural Fixed-Point Acceleration for SCS
We present neural fixed-point acceleration, a framework to automatically learn to accelerate convex fixed-point problems that are drawn from a distribution, using ideas from meta-learning and classical acceleration algorithms. We apply our framework to SCS, the state-of-the-art solver for convex cone programming. Our work brings neural acceleration into any optimization problem expressible with CVXPY.
Requirements
The following packages are required to run our code:
torch
numpy
scipy
matplotlib
cvxpy
tensorboard
hydra-core
pandas
