Higher-order Accumulative Regularization Methods for Gradient Minimization

We introduce a unified Accumulative Regularization (AR) framework that closes the gap between fast function-value residual convergence and slow gradient norm convergence in high-order convex optimization. The framework systematically transforms fast function-value residual convergence rates into matching gradient norm convergence rates.