September 6, 2024TechnologiesOpen Access

MAMGD: Gradient-Based Optimization Method Using Exponential Decay

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The new MAMGD optimization method showed a high convergence speed in training neural networks, outperforming traditional techniques.
Experimental results indicated stability to fluctuations, which is crucial during neural network training processes.
Analysis using various optimization problems verified the effectiveness of the MAMGD method against existing gradient optimization strategies in depth and efficiency. The method was tested on popular datasets for classification and regression tasks, highlighting its versatility and reliability when applied to different scenarios.

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Randomized trial demonstrates high convergence speed and stability in neural network training, suggesting effective optimization techniques.

Sakovich et al. (2024) studied this question.

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Randomized trial demonstrates high convergence speed and stability in neural network training, suggesting effective optimization techniques.