# Enhanced NIRMAL Optimizer With Damped Nesterov Acceleration: A Comparative Analysis

arXiv: https://arxiv.org/abs/2508.16550 · HTML: https://mlsanity.com/paper/2508.16550/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 8 references that are in the map.
- First posted on arXiv 22 Aug 2025.

## Builds on (most cited first, 3 of 3)

- [Nesterov's Accelerated Gradient and Momentum as approximations to Regularised Update Descent](https://mlsanity.com/paper/1607.01981/) (2016)
- [Comparative Analysis of Novel NIRMAL Optimizer Against Adam and SGD with Momentum](https://mlsanity.com/paper/2508.04293/) (2025)
- [A class of generalized Nesterov's accelerated gradient method from dynamical perspective](https://mlsanity.com/paper/2508.12816/) (2025)

## Abstract (arXiv)

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.