A Theoretical and Experimental Study of a Novel Adaptive Learning Algorithm

• URL: http://arxiv.org/abs/2605.29273v1
• Date: Thu, 28 May 2026 02:48:19 GMT
• Title: A Theoretical and Experimental Study of a Novel Adaptive Learning Algorithm
• Abstract summary: A crucial component of machine learning algorithms is minimizing loss functions with less computational cost and less oscillations.<n>In this paper, popular adaptive optimization methods like Adam and AMSGrad are reviewed with an emphasis on their fundamental design concepts.<n>A new variant, C-Adam, is proposed based on the line of sight approach to address limitations of the above mentioned methods.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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