Accelerated Gradient Descent for Faster Convergence with Minimal Overhead

Manuel Graca; L. Miguel Silveira; Arlindo Oliveira; Frank Liu

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arXiv · 2605.16017

Accelerated Gradient Descent for Faster Convergence with Minimal Overhead

Manuel Graca·L. Miguel Silveira·Arlindo Oliveira·Frank Liu

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Abstract

In this paper, we present CT-AGD (Curvature-Tuned Accelerated Gradient Descent), an optimization method for non-convex optimization problems in deep learning training tasks. CT-AGD is a general boosting procedure that accelerates first-order methods by explicitly capturing the local curvature using finite-difference quotients, and the development of heuristics aimed at mitigating noise and bias introduced by stochastic mini-batch training. CT-AGD has a comparable storage and computational overhead as adaptive gradient methods such as Adam. Our extensive experiments demonstrate that CT-AGD achieves the same level of accuracy as the baseline first-order methods, yet reduces the required training epochs by 33% on average.

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Manuel Graca, L. Miguel Silveira, Arlindo Oliveira, Frank Liu. 2026-05-15. Accelerated Gradient Descent for Faster Convergence with Minimal Overhead. https://arxiv.org/abs/2605.16017

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