1. Allen-Zhu Z.: Natasha: Faster non-convex stochastic optimization via strongly non-convex parameter. In: International Conference on Machine Learning, vol. 70, pp. 89–97, PMLR Press (2017)
2. Asi, H., Duchi, J.C.: Stochastic (approximate) proximal point methods: convergence, optimality, and adaptivity. SIAM. J. Optim. 29(3), 2257–2290 (2019)
3. Asi, H., Duchi, J.C.: The importance of better models in stochastic optimization. Proc. Natl. Acad. Sci. USA 116, 22924–22930 (2019)
4. Atchaé, Y.F., Fort, G., Moulines, E.: On perturbed proximal gradient algorithms. J. Mach. Learn. Res. 18(1), 1–33 (2017)
5. Bertsekas, D.: Convex Optimization Algorithms. Athena Scientific (2015)