[9] Lars Buitinck, Gilles Louppe, Mathieu Blondel, Fabian Pedregosa, Andreas Mueller, Olivier Grisel, Vlad Niculae, Peter Prettenhofer, Alexandre Gramfort, Jaques Grobler, Robert Layton, Jake VanderPlas, Arnaud Joly, Brian Holt, and Gael Varoquaux. API design for machine learning software: experiences from the scikit-learn project. In ECML PKDD Workshop: Languages for Data Mining and Machine Learning, pages 108–122, 2013.
[10] R. Chen and S. Wild. Randomized derivative-free optimization of noisy convex functions. arXiv preprint arXiv:1507.03332, 2015.
[11] K. Choromanski, M. Rowland, W. Chen, and A. Weller. Unifying orthogonal monte carlo methods. In International Conference on Machine Learning, pages 1203-1212. PMLR, 2019.
[12] K. Choromanski, M. Rowland, V. Sindhwani, R. Turner, and A. Weller. Structured evolution with compact architectures for scalable policy optimization. In Jennifer Dy and Andreas Krause, editors, Proceedings of the 35th International Conference on Machine Learning, volume 80 of Proceedings of Machine Learning Research, pages 970–978. PMLR, 10–15 Jul 2018.
[13] F. H. Clarke. Optimization and Nonsmooth Analysis. Society for Industrial and Applied Mathematics, 1990.
[14] A. R. Conn, K. Scheinberg, and L. N. Vicente. Introduction to Derivative-Free Optimization. Society for Industrial and Applied Mathematics, 2009.
[15] D. Davis, D. Drusvyatskiy, Y. T. Lee, S. Padmanabhan, and G. Ye. A gradient sampling method with complexity guarantees for lipschitz functions in high and low dimensions. In S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, and A. Oh, editors, Advances in Neural Information Processing Systems, volume 35, pages 6692-6703. Curran Associates, Inc., 2022.
[16] J. C. Duchi, P. L. Bartlett, L. Peter, and M. J. Wainwright. Randomized smoothing for stochastic optimization. SIAM Journal on Optimization, 22(2):674-701, 2012.
[17] J. C. Duchi, M. I. Jordan, M. J. Wainwright, and A. Wibisono. Optimal rates for zero-order convex optimization: The power of two function evaluations. IEEE Transactions on Information Theory, 61(5):2788-2806, 2015.
[18] A. Flaxman, A. Tauman Kalai, and B. McMahan. Online convex optimization in the bandit setting: Gradient descent without a gradient. In SODA '05 Proceedings of the sixteenth annual ACM-SIAM symposium on Discrete algorithms, pages 385-394, January 2005.
[19] M. Fornasier, T. Klock, and K. Riedl. Consensus-based optimization methods converge globally in mean-field law. arXiv 2103.15130, 2021.
[20] P. I. Frazier. Bayesian Optimization, chapter 11, pages 255-278. INFORMS, 2018.
[21] X. Gao, B. Jiang, and S. Zhang. On the information-adaptive variants of the admm: An iteration complexity perspective. Journal of Scientific Computing, 76(1):327-363, Jul 2018.
[22] A. Gasnikov, A. Novitskii, V. Novitskii, F. Abdukhakimov, D. Kamzolov, A. Beznosikov, M. Takac, P. Dvurechensky, and B. Gu. The power of first-order smooth optimization for black-box non-smooth problems. In Kamalika Chaudhuri, Stefanie Jegelka, Le Song, Csaba Szepesvari, Gang Niu, and Sivan Sabato, editors, Proceedings of the 39th International Conference on Machine Learning, volume 162 of Proceedings of Machine Learning Research, pages 7241-7265. PMLR, 17-23 Jul 2022.
[23] A. Genz. Methods for generating random orthogonal matrices. In Monte-Carlo and Quasi-Monte Carlo Methods 1998: Proceedings of a Conference held at the Claremont Graduate University, Claremont, California, USA, June 22-26, 1998, pages 199-213. Springer, 2000.
[24] S. Ghadimi and G. Lan. Stochastic first- and zeroth-order methods for nonconvex stochastic programming. SIAM Journal on Optimization, 23(4):2341–2368, 2013.
[25] G. N. Grapiglia. Worst-case evaluation complexity of a derivative-free quadratic regularization method. Optimization Letters, Feb 2023.