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arXiv · 2411.14855

Applications of fractional calculus in learned optimization

Teodor Alexandru Szente·James Harrison·Mihai Zanfir·Cristian Sminchisescu
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Abstract

Fractional gradient descent has been studied extensively, with a focus on its ability to extend traditional gradient descent methods by incorporating fractional-order derivatives. This approach allows for more flexibility in navigating complex optimization landscapes and offers advantages in certain types of problems, particularly those involving non-linearities and chaotic dynamics. Yet, the challenge of fine-tuning the fractional order parameters remains unsolved. In this work, we demonstrate that it is possible to train a neural network to predict the order of the gradient effectively.

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Teodor Alexandru Szente, James Harrison, Mihai Zanfir, Cristian Sminchisescu. 2024-11-22. Applications of fractional calculus in learned optimization. https://arxiv.org/abs/2411.14855

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