\begin{thebibliography}{1} \bibitem{foster2019complexity} Dylan~J Foster, Ayush Sekhari, Ohad Shamir, Nathan Srebro, Karthik Sridharan, and Blake Woodworth. \newblock The complexity of making the gradient small in stochastic convex optimization. \newblock In {\em Conference on Learning Theory}, pages 1319--1345. PMLR, 2019. \bibitem{globook} R.~Horst and H.~Tuy. \newblock {\em Global Optimization: Deterministic Approaches}. \newblock Springer-Verlag. \bibitem{nest} Y.~Nesterov. \newblock {\em Introductory Lectures on Convex Optimization A Basic Course}. \newblock Kluwer Academic Publishers. \bibitem{pour} M.~Pour-El and J.~Richards. \newblock {\em Computability in analysis and physics}. \newblock Springer, Heidelberg, 1989. \bibitem{sorbook} R.I. Soare. \newblock {\em Turing Computability: Theory and Applications}. \newblock Springer-Verlag. \bibitem{zhang2020complexity} Jingzhao Zhang, Hongzhou Lin, Stefanie Jegelka, Suvrit Sra, and Ali Jadbabaie. \newblock Complexity of finding stationary points of nonconvex nonsmooth functions. \newblock In {\em International Conference on Machine Learning}, pages 11173--11182. PMLR, 2020. \end{thebibliography}