Acceleration Methods

This monograph covers some recent advances in a range of acceleration techniques frequently used convex optimization. We first use quadratic optimization problems to introduce two key families methods, namely momentum and nested schemes. They coincide the case form Chebyshev method. discuss methods detail, starting with seminal work Nesterov structure convergence proofs using few master templates, such as that for optimized gradient which provide benefit showing how optimize guarantees. further cover proximal acceleration, at heart Catalyst Accelerated Hybrid Proximal Extragradient frameworks, similar algorithmic patterns. Common rely directly on knowledge regularity parameters problem hand. conclude by discussing restart schemes, set simple reaching nearly optimal rates while adapting unobserved parameters.