[2] Yossi Arjevani, Ohad Shamir, and Nathan Srebro. A tight convergence analysis for stochastic gradient descent with delayed updates. In Algorithmic Learning Theory, pages 111-132. PMLR, 2020.
[3] Karl Backström, Marina Papatriantafilou, and Philippas Tsigas. Mindthestep-asyncpsgd: Adaptive asynchronous parallel stochastic gradient descent. In 2019 IEEE International Conference on Big Data (Big Data), pages 16-25. IEEE, 2019.
[4] Dimitri Bertsekas and John Tsitsiklis. Parallel and distributed computation: numerical methods. Athena Scientific, 2015.
[5] Vasileios Charisopoulos, Yudong Chen, Damek Davis, Mateo Diaz, Lijun Ding, and Dmitriy Drusvyatskiy. Low-rank matrix recovery with composite optimization: good conditioning and rapid convergence. Foundations of Computational Mathematics, 21(6):1505-1593, 2021.
[6] Shixiang Chen, Alfredo Garcia, and Shahin Shahrampour. On distributed non-convex optimization: Projected subgradient method for weakly convex problems in networks. IEEE Transactions on Automatic Control, 2021.
[7] Alon Cohen, Amit Daniely, Yoel Drori, Tomer Koren, and Mariano Schain. Asynchronous stochastic optimization robust to arbitrary delays. Advances in Neural Information Processing Systems, 34, 2021.
[8] Damek Davis and Dmitriy Drusvyatskiy. Stochastic model-based minimization of weakly convex functions. SIAM Journal on Optimization, 29(1):207-239, 2019.
[9] Damek Davis, Dmitriy Drusvyatskiy, and Kellie J MacPhee. Stochastic model-based minimization under high-order growth. arXiv preprint arXiv:1807.00255, 2018.
[10] Jeffrey Dean, Greg Corrado, Rajat Monga, Kai Chen, Matthieu Devin, Mark Mao, Marc'aurlio Ranzato, Andrew Senior, Paul Tucker, Ke Yang, et al. Large scale distributed deep networks. Advances in neural information processing systems, 25, 2012.
[11] Qi Deng and Wenzhi Gao. Minibatch and momentum model-based methods for stochastic weakly convex optimization. Advances in Neural Information Processing Systems, 34, 2021.
[12] Dmitriy Drusvyatskiy and Courtney Paquette. Efficiency of minimizing compositions of convex functions and smooth maps. Mathematical Programming, pages 1-56, 2018.
[13] John C Duchi and Feng Ruan. Stochastic methods for composite and weakly convex optimization problems. SIAM Journal on Optimization, 28(4):3229-3259, 2018.
[14] John C Duchi and Feng Ruan. Solving (most) of a set of quadratic equalities: Composite optimization for robust phase retrieval. Information and Inference: A Journal of the IMA, 8(3):471-529, 2019.
[15] R Fletcher. A model algorithm for composite nondifferentiable optimization problems. In Nondifferential and Variational Techniques in Optimization, pages 67-76. Springer, 1982.
[16] Trevor Hastie, Robert Tibshirani, Jerome H Friedman, and Jerome H Friedman. The elements of statistical learning: data mining, inference, and prediction, volume 2. Springer, 2009.
[17] Sai Praneeth Karimireddy, Quentin Rebjock, Sebastian Stich, and Martin Jaggi. Error feedback fixes signsgd and other gradient compression schemes. In International Conference on Machine Learning, pages 3252-3261. PMLR, 2019.
[18] Anastasiia Koloskova, Sebastian U Stich, and Martin Jaggi. Sharper convergence guarantees for asynchronous sgd for distributed and federated learning. Advances in Neural Information Processing Systems, 35:17202-17215, 2022.
[19] Adrian S Lewis and Stephen J Wright. A proximal method for composite minimization. Mathematical Programming, 158(1):501-546, 2016.
[20] Xiao Li, Zhihui Zhu, Anthony Man-Cho So, and Jason D Lee. Incremental methods for weakly convex optimization. arXiv preprint arXiv:1907.11687, 2019.