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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.16017 |
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| _version_ | 1866911688490483712 |
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| author | Graca, Manuel Silveira, L. Miguel Oliveira, Arlindo Liu, Frank |
| author_facet | Graca, Manuel Silveira, L. Miguel Oliveira, Arlindo Liu, Frank |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_16017 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Accelerated Gradient Descent for Faster Convergence with Minimal Overhead Graca, Manuel Silveira, L. Miguel Oliveira, Arlindo Liu, Frank Machine Learning 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. |
| title | Accelerated Gradient Descent for Faster Convergence with Minimal Overhead |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2605.16017 |