{"ID":2879534,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.16550","arxiv_id":"2508.16550","title":"Enhanced NIRMAL Optimizer With Damped Nesterov Acceleration: A Comparative Analysis","abstract":"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.","short_abstract":"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 retai...","url_abs":"https://arxiv.org/abs/2508.16550","url_pdf":"https://arxiv.org/pdf/2508.16550v1","authors":"[\"Nirmal Gaud\",\"Prasad Krishna Murthy\",\"Mostaque Md. Morshedur Hassan\",\"Abhijit Ganguly\",\"Vinay Mali\",\"Ms Lalita Bhagwat Randive\",\"Abhaypratap Singh\"]","published":"2025-08-22T17:16:06Z","proceeding":"cs.IR","tasks":"[\"cs.IR\",\"cs.AI\"]","methods":"[\"Convolutional Neural Network\"]","has_code":false}