A Theoretical and Experimental Study of a Novel Adaptive Learning Algorithm
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Computer Science > Machine Learning
Title:A Theoretical and Experimental Study of a Novel Adaptive Learning Algorithm
Abstract:A crucial component of machine learning algorithms is minimizing loss functions with less computational cost and less oscillations. While adaptive learning rate-based optimizers have been widely used for real-world tasks, they do not guarantee convergence, which is why AMSGrad was later introduced to investigate the non-convergence behaviour of Adam. In this paper, popular adaptive optimization methods like Adam and AMSGrad are critically reviewed with an emphasis on their fundamental design concepts. To address limitations of the above mentioned optimizers, a new optimizer variant, C-Adam, is proposed based on the line of sight approach. A theoretical proof for convergence is also provided and the optimizer is validated through a number of real-life based numerical experiments.
| Subjects: | Machine Learning (cs.LG); Optimization and Control (math.OC) |
| Cite as: | arXiv:2605.29273 [cs.LG] |
| (or arXiv:2605.29273v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.29273
arXiv-issued DOI via DataCite (pending registration)
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