1. Agrawal, A., Verschueren, R., Diamond, S., Boyd, S.: A rewriting system for convex optimization problems. J. Control Dec. 5(1), 42–60 (2018)
2. Asi, H., Chadha, K., Cheng, G., Duchi, J.C.: Minibatch stochastic approximate proximal point methods. In: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M.F., Lin, H. (eds.) Advances in Neural Information Processing Systems, vol. 33, pp. 21958–21968. Curran Associates Inc, New York (2020)
3. Asi, H., Duchi, J.C.: The importance of better models in stochastic optimization. Proc. Natl. Acad. Sci. 116(46), 22924–22930 (2019). https://doi.org/10.1073/pnas.1908018116.
4. Asi, H., Duchi, J.C.: Modeling simple structures and geometry for better stochastic optimization algorithms. In: Chaudhuri, K., Sugiyama, M. (eds.) Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, Proceedings of Machine Learning Research, vol. 89, pp. 2425–2434. PMLR (2019). http://proceedings.mlr.press/v89/asi19a.html
5. Asi, H., Duchi, J.C.: Stochastic (approximate) proximal point methods: convergence, optimality, and adaptivity. SIAM J. Optim. 29(3), 2257–2290 (2019). https://doi.org/10.1137/18M1230323