<ul><li>Researchers propose both serial and parallel proximal (linearized) alternating direction method of multipliers (ADMM) algorithms for training residual neural networks.</li><li>The proposed algorithms mitigate the exploding gradient issue and are suitable for parallel and distributed training through regional updates.</li><li>The algorithms converge at an R-linear (sublinear) rate for both the iteration points and the objective function values.</li><li>Experimental results validate the proposed ADMM algorithms, showing rapid and stable convergence, improved performance, and high computational efficiency.</li></ul>

ADMM Algorithms for Residual Network Training: Convergence Analysis and Parallel Implementation

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