High-Order Accumulative Regularization for Gradient Minimization in Convex Programming

High-order optimization methods achieve fast convergence for function-value residuals, but often exhibit a significant gap when it comes to reducing the gradient norm. This paper introduces a unified Accumulative Regularization (AR) framework that closes this gap by systematically transforming fast function-value residual convergence rates into matching gradient norm convergence rates.