Accelerated Gradient Descent for Faster Convergence with Minimal Overhead

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

Keywords: optimization, training, SGD, gradient descent, BFGS

TL;DR: CT-AGD, an optimization method for non-convex problems in deep learning training tasks.

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.

Primary Area: optimization

Submission Number: 9530