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.
Abstract (translated)
这项研究介绍了增强型NIRMAL（集成鲁棒多适应学习与阻尼Nesterov加速）优化器，它是原始NIRMAL优化器的一个改进版本。通过引入$(\alpha, r)$-阻尼的Nesterov加速机制，Enhanced NIRMAL在保持棋盘启发策略的同时提高了收敛稳定性，这些策略包括梯度下降、动量、随机扰动、自适应学习率和非线性变换。我们在四个基准图像分类数据集（MNIST、FashionMNIST、CIFAR-10 和 CIFAR-100）上使用定制的卷积神经网络（CNN）架构对Enhanced NIRMAL进行了评估，并将其与Adam、带有动量的SGD、Nesterov以及原始NIRMAL进行对比。在CIFAR-100数据集上，Enhanced NIRMAL达到了46.06%的测试准确率和最低的测试损失（1.960435），超过了原版NIRMAL（44.34%准确率）并且与带有动量的SGD（46.43%准确率）非常接近。这些结果强调了Enhanced NIRMAL在复杂数据集上的优越泛化能力和稳定性。
URL
https://arxiv.org/abs/2508.16550
