Fast Convex Optimization with GeNIOS.jl
We introduce GeNIOS.jl, a package for large-scale data-driven convex optimization. This package leverages randomized numerical linear algebra and inexact subproblem solves to dramatically speed up the alternating direction method of multipliers (ADMM). We showcase performance on a logistic regression problem and a constrained quadratic program. Finally, we show how this package can be extended to almost any convex optimization problem that appears in practice.
