Learning Algorithm Hyperparameters for Fast Parametric Convex Optimization
A machine-learning framework to learn the hyperparameter sequence of first-order methods to quickly solve parametric convex optimization problems, and shows how to learn hyperparameters for several popular algorithms: gradient descent, proximal gradient descent, and two ADMM-based solvers.