1. B. E. Woodworth, J. Wang, A. Smith, B. McMahan and N. Srebro, “Graph Oracle Models, Lower Bounds, and Gaps for Parallel Stochastic Optimization,” in Advances in Neural Information Processing Systems 31 (2018).
2. S. Bubeck, Q. Jiang, Y. T. Lee, Y. Li and A. Sidford, “Complexity of highly parallel non-smooth convex optimization,” in Advances in Neural Information Processing Systems 33 (2019).
3. J. Diakonikolas and C. Guzmán, “Lower bounds for parallel and randomized convex optimization,” J. Mach. Learn. Res. 21, Art. no. 5 (2020).
4. S. Bubeck, “Convex optimization: algorithms and complexity,” Found. Trends Mach. Learn. 8 (3-4), 231–357 (2015).
5. A. S. Nemirovskii and D. B. Yudin, Complexity of Problems and Effectiveness of Methods of Optimization (Nauka, Moscow, 1979) [in Russian].