  Fixed-point iterations are at the heart of numerical computing and are often
a computational bottleneck in real-time applications that typically need a fast
solution of moderate accuracy. We present neural fixed-point acceleration which
combines ideas from meta-learning and classical acceleration methods to
automatically learn to accelerate fixed-point problems that are drawn from a
distribution. We apply our framework to SCS, the state-of-the-art solver for
convex cone programming, and design models and loss functions to overcome the
challenges of learning over unrolled optimization and acceleration
instabilities. Our work brings neural acceleration into any optimization
problem expressible with CVXPY. The source code behind this paper is available
at https://github.com/facebookresearch/neural-scs

Machine Learning;Artificial Intelligence;Optimization and Control