Enhanced NIRMAL Optimizer With Damped Nesterov Acceleration: A Comparative Analysis
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915457425997824 |
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| author | Gaud, Nirmal Murthy, Prasad Krishna Hassan, Mostaque Md. Morshedur Ganguly, Abhijit Mali, Vinay Randive, Ms Lalita Bhagwat Singh, Abhaypratap |
| author_facet | Gaud, Nirmal Murthy, Prasad Krishna Hassan, Mostaque Md. Morshedur Ganguly, Abhijit Mali, Vinay Randive, Ms Lalita Bhagwat Singh, Abhaypratap |
| contents | This study introduces the Enhanced NIRMAL (Novel Integrated Robust Multi-Adaptation Learning with Damped Nesterov Acceleration) optimizer, an improved version of the original NIRMAL optimizer. By incorporating an $(α, r)$-damped Nesterov acceleration mechanism, Enhanced NIRMAL improves convergence stability while retaining chess-inspired strategies of gradient descent, momentum, stochastic perturbations, adaptive learning rates, and non-linear transformations.
We evaluate Enhanced NIRMAL against Adam, SGD with Momentum, Nesterov, and the original NIRMAL on four benchmark image classification datasets: MNIST, FashionMNIST, CIFAR-10, and CIFAR-100, using tailored convolutional neural network (CNN) architectures.
Enhanced NIRMAL achieves a test accuracy of 46.06\% and the lowest test loss (1.960435) on CIFAR-100, surpassing the original NIRMAL (44.34\% accuracy) and closely rivaling SGD with Momentum (46.43\% accuracy). These results underscore Enhanced NIRMAL's superior generalization and stability, particularly on complex datasets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_16550 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Enhanced NIRMAL Optimizer With Damped Nesterov Acceleration: A Comparative Analysis Gaud, Nirmal Murthy, Prasad Krishna Hassan, Mostaque Md. Morshedur Ganguly, Abhijit Mali, Vinay Randive, Ms Lalita Bhagwat Singh, Abhaypratap Information Retrieval Artificial Intelligence This study introduces the Enhanced NIRMAL (Novel Integrated Robust Multi-Adaptation Learning with Damped Nesterov Acceleration) optimizer, an improved version of the original NIRMAL optimizer. By incorporating an $(α, r)$-damped Nesterov acceleration mechanism, Enhanced NIRMAL improves convergence stability while retaining chess-inspired strategies of gradient descent, momentum, stochastic perturbations, adaptive learning rates, and non-linear transformations. We evaluate Enhanced NIRMAL against Adam, SGD with Momentum, Nesterov, and the original NIRMAL on four benchmark image classification datasets: MNIST, FashionMNIST, CIFAR-10, and CIFAR-100, using tailored convolutional neural network (CNN) architectures. Enhanced NIRMAL achieves a test accuracy of 46.06\% and the lowest test loss (1.960435) on CIFAR-100, surpassing the original NIRMAL (44.34\% accuracy) and closely rivaling SGD with Momentum (46.43\% accuracy). These results underscore Enhanced NIRMAL's superior generalization and stability, particularly on complex datasets. |
| title | Enhanced NIRMAL Optimizer With Damped Nesterov Acceleration: A Comparative Analysis |
| topic | Information Retrieval Artificial Intelligence |
| url | https://arxiv.org/abs/2508.16550 |