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Accelerated Douglas-Rachford splitting algorithm using neural network
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DOI：10.1016/j.cnsns.2025.109462.png)
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Abstract
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• Introduce Douglas-Rachford splitting algorithms with deviations for solving monotone inclusion problems. • Weak convergence is established for the general case of maximally monotone operators. • Strong convergence and linear convergence are established when either operator is strongly-monotone. • Using the neural network to pick the best update of deviation.
Keywords:
Monotone inclusion problem
the Douglas–Rachford splitting algorithm
Deviations
Maximally monotone operator
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