# Comparative Analysis of Novel NIRMAL Optimizer Against Adam and SGD with Momentum

arXiv: https://arxiv.org/abs/2508.04293 · HTML: https://mlsanity.com/paper/2508.04293/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 5 references that are in the map.
- First posted on arXiv 6 Aug 2025.

## Built on by (most cited first, 2 of 2)

- [Evolution of Optimization Methods: Algorithms, Scenarios, and Evaluations](https://mlsanity.com/paper/2604.12968/) (2026)
- [Enhanced NIRMAL Optimizer With Damped Nesterov Acceleration: A Comparative Analysis](https://mlsanity.com/paper/2508.16550/) (2025)

## Abstract (arXiv)

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.