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arXiv · 2105.05210

Accelerated Forward-Backward Optimization using Deep Learning

Sebastian Banert·Jevgenija Rudzusika·Ozan Öktem·Jonas Adler
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Abstract

We propose several deep-learning accelerated optimization solvers with convergence guarantees. We use ideas from the analysis of accelerated forward-backward schemes like FISTA, but instead of the classical approach of proving convergence for a choice of parameters, such as a step-size, we show convergence whenever the update is chosen in a specific set. Rather than picking a point in this set using some predefined method, we train a deep neural network to pick the best update. Finally, we show that the method is applicable to several cases of smooth and non-smooth optimization and show superior results to established accelerated solvers.

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Sebastian Banert, Jevgenija Rudzusika, Ozan Öktem, Jonas Adler. 2021-05-11. Accelerated Forward-Backward Optimization using Deep Learning. https://arxiv.org/abs/2105.05210

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