Comparative Analysis of Novel NIRMAL Optimizer Against Adam and SGD with Momentum
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866912523175854080 |
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| author | Gaud, Nirmal Mouli, Surej Katiyar, Preeti Ramya, Vaduguru Venkata |
| author_facet | Gaud, Nirmal Mouli, Surej Katiyar, Preeti Ramya, Vaduguru Venkata |
| contents | This study proposes NIRMAL (Novel Integrated Robust Multi-Adaptation Learning), a novel optimization algorithm that combines multiple strategies inspired by the movements of the chess piece. These strategies include gradient descent, momentum, stochastic perturbations, adaptive learning rates, and non-linear transformations. We carefully evaluated NIRMAL against two widely used and successful optimizers, Adam and SGD with Momentum, on four benchmark image classification datasets: MNIST, FashionMNIST, CIFAR-10, and CIFAR-100. The custom convolutional neural network (CNN) architecture is applied on each dataset. The experimental results show that NIRMAL achieves competitive performance, particularly on the more challenging CIFAR-100 dataset, where it achieved a test accuracy of 45.32\%and a weighted F1-score of 0.4328. This performance surpasses Adam (41.79\% accuracy, 0.3964 F1-score) and closely matches SGD with Momentum (46.97\% accuracy, 0.4531 F1-score). Also, NIRMAL exhibits robust convergence and strong generalization capabilities, especially on complex datasets, as evidenced by stable training results in loss and accuracy curves. These findings underscore NIRMAL's significant ability as a versatile and effective optimizer for various deep learning tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_04293 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Comparative Analysis of Novel NIRMAL Optimizer Against Adam and SGD with Momentum Gaud, Nirmal Mouli, Surej Katiyar, Preeti Ramya, Vaduguru Venkata Information Retrieval Artificial Intelligence This study proposes NIRMAL (Novel Integrated Robust Multi-Adaptation Learning), a novel optimization algorithm that combines multiple strategies inspired by the movements of the chess piece. These strategies include gradient descent, momentum, stochastic perturbations, adaptive learning rates, and non-linear transformations. We carefully evaluated NIRMAL against two widely used and successful optimizers, Adam and SGD with Momentum, on four benchmark image classification datasets: MNIST, FashionMNIST, CIFAR-10, and CIFAR-100. The custom convolutional neural network (CNN) architecture is applied on each dataset. The experimental results show that NIRMAL achieves competitive performance, particularly on the more challenging CIFAR-100 dataset, where it achieved a test accuracy of 45.32\%and a weighted F1-score of 0.4328. This performance surpasses Adam (41.79\% accuracy, 0.3964 F1-score) and closely matches SGD with Momentum (46.97\% accuracy, 0.4531 F1-score). Also, NIRMAL exhibits robust convergence and strong generalization capabilities, especially on complex datasets, as evidenced by stable training results in loss and accuracy curves. These findings underscore NIRMAL's significant ability as a versatile and effective optimizer for various deep learning tasks. |
| title | Comparative Analysis of Novel NIRMAL Optimizer Against Adam and SGD with Momentum |
| topic | Information Retrieval Artificial Intelligence |
| url | https://arxiv.org/abs/2508.04293 |