On optimal universal first-order methods for minimizing heterogeneous sums

Author:

Grimmer BenjaminORCID

Publisher

Springer Science and Business Media LLC

Subject

Control and Optimization,Business, Management and Accounting (miscellaneous)

Reference34 articles.

1. Antonakopoulos, K., Pethick, T., Kavis, A., Mertikopoulos, P., Cevher, V.: Sifting through the noise: universal first-order methods for stochastic variational inequalities. In: Advances in Neural Information Processing Systems, vol. 34, pp. 13099–13111. Curran Associates, Inc. (2021)

2. Arjevani, Y., Daniely, A., Jegelka, S., Lin, H.: On the complexity of minimizing convex finite sums without using the indices of the individual functions (2020). arXiv:2002.03273

3. Bolte, J., Daniilidis, A., Lewis, A.: The łojasiewicz inequality for nonsmooth subanalytic functions with applications to subgradient dynamical systems. SIAM J. Optim. 17(4), 1205–1223 (2007). https://doi.org/10.1137/050644641

4. Bolte, J., Nguyen, T.P., Peypouquet, J., Suter, B.W.: From error bounds to the complexity of first-order descent methods for convex functions. Math. Program. 165(2), 471–507 (2017). https://doi.org/10.1007/s10107-016-1091-6

5. Chang, C.C., Lin, C.J.: Libsvm data: classification (binary class). https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary.html. Accessed 06 Jan 2023

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