1. Zang, I.: A smoothing-out technique for min-max optimization. Math. Programming. 19(1), 61–77 (1980)
2. Zhao, G., Wang, Z., Mou, H.: Uniform approximation of min/max functions by smooth splines. J. Comput. Appl. Math. 236(5), 699–703 (2011). The 7th International Conference on Scientific Computing and Applications, June 13–16, 2010, Dalian, China
3. Asadi, K., Littman, M.L.: An alternative softmax operator for reinforcement learning. In: Proceedings of the 34th International Conference on Machine Learning, Vol 70. ICML’17, pp. 243–252. JMLR.org, (Online) (2017)
4. Liu, W., Wen, Y., Yu, Z., Yang, M.: Large-margin softmax loss for convolutional neural networks. In: Balcan, M., Weinberger, K. (eds.) International Conference on Machine Learning, Vol 48. Proceedings of Machine Learning Research, vol. 48 (2016). 33rd International Conference on Machine Learning, New York, NY, JUN 20–22, 2016
5. Nielsen, F., Sun, K.: Guaranteed bounds on information-theoretic measures of univariate mixtures using piecewise log-sum-exp inequalities. Entropy. 18(12) (2016). https://doi.org/10.3390/e18120442