# Enhanced NIRMAL Optimizer With Damped Nesterov Acceleration: A Comparative Analysis Source: [Enhanced NIRMAL Optimizer With Damped Nesterov Acceleration: A Comparative Analysis]() Collection index: [llms.txt]() Data updated: 2025-10-28T13:57:20.646Z Authors: Nirmal Gaud, Prasad Krishna Murthy, Mostaque Md. Morshedur Hassan, Abhijit Ganguly, Vinay Mali, Ms Lalita Bhagwat Randive, Abhaypratap Singh Published: 2025-08-22T17:16:06.000Z ## 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 $(\\alpha, r)$-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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()