Fetching the paper…
Reading the bibliography…
Many convex optimization problems have structured objective function written as a sum of functions with different types of oracles (full gradient, coordinate derivative, stochastic gradient) and different evaluation complexity of these oracles.
Optimization Methods and Software (2021)
Stonyakin, F., Tyurin, A., Gasnikov, A., Dvurechensky, P., Agafonov, A., Dvinskikh, D., Alkousa, M., Pasechnyuk, D., Artamonov, S., Piskunova, V.: Inexact model: A framework for optimization and variational inequalities · 1902
Earlier work this paper cites.
Nemirovsky, A.S., Yudin, D.B.: Problem complexity and method efficiency in optimization. (1983)
1983
Earlier work this paper cites.
Springer-Verlag (1996)
Vapnik, V.: The Nature of Statistical Learning · 1996
Earlier work this paper cites.
MIT Press (2001)
Schölkopf, B., Smola, A.: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond (Adaptive Computation and Machine Learning) · 2001
Earlier work this paper cites.
Kamzolov, D., Gasnikov, A., Dvurechensky, P.: Optimal combination of tensor optimization methods · 2002
Earlier work this paper cites.
Zhang, Y., Xiao, L.: Stochastic primal-dual coordinate method for regularized empirical risk minimization · 2002
Earlier work this paper cites.
Mathematical Programming 103
Nesterov, Y.: Smooth minimization of non-smooth functions · 2005
Earlier work this paper cites.
Shibaev, I., Dvurechensky, P., Gasnikov, A.: Zeroth-order methods for noisy Hölder-gradient functions · 2006
Earlier work this paper cites.
Mathematical Programming 140
Nesterov, Y.: Gradient methods for minimizing composite functions · 2007
Earlier work this paper cites.
Found. Comput. Math. 17
Nesterov, Y., Spokoiny, V.: Random gradient-free minimization of convex functions · 2011
Earlier work this paper cites.
SIAM Journal on Optimization 22
Nesterov, Y.: Efficiency of coordinate descent methods on huge-scale optimization problems · 2012
Earlier work this paper cites.
SIAM Journal on Optimization 23
Monteiro, R.D., Svaiter, B.F.: An accelerated hybrid proximal extragradient method for convex optimization and its implications to second-order methods · 2013
Earlier work this paper cites.
Lin, Q., Lu, Z., Xiao, L.: An accelerated proximal coordinate gradient method · 2014
Earlier work this paper cites.
In: F. Bach, D. Blei (eds.) Proceedings of the 32nd International Conference on Machine Learning, Proceedings of Machine Learning Research , vol. 37, pp. 78–86. PMLR, Lille, France (2015)
Agarwal, A., Bottou, L.: A lower bound for the optimization of finite sums · 2015
Earlier work this paper cites.
SIAM Journal on Optimization 25
Fercoq, O., Richtárik, P.: Accelerated, parallel, and proximal coordinate descent · 2015
Earlier work this paper cites.
In: Advances in neural information processing systems, pp. 3384–3392 (2015)
Lin, H., Mairal, J., Harchaoui, Z.: A universal catalyst for first-order optimization · 2015
Earlier work this paper cites.
Allen-Zhu, Z., Qu, Z., Richtarik, P., Yuan, Y.: Even faster accelerated coordinate descent using non-uniform sampling · 2016
Cited alongside, same era.
Bogolubsky, L., Dvurechensky, P., Gasnikov, A., Gusev, G., Nesterov, Y., Raigorodskii, A.M., Tikhonov, A., Zhukovskii, M.: Learning supervised pagerank with gradient-based and gradient-free optimization methods · 2016
Cited alongside, same era.
Proceedings of the Moscow Institute of Physics and Technology 8
Gasnikov, A., Dvurechensky, P., Usmanova, I.: About accelerated randomized methods · 2016
Cited alongside, same era.
Mathematical Programming 159
Lan, G.: Gradient sliding for composite optimization · 2016
Cited alongside, same era.
arXiv preprint arXiv:1910.06028 (2019)
Spokoiny, V., Panov, M.: Accuracy of gaussian approximation in nonparametric bernstein–von mises theorem · 2019
Later among the works it cites.
In: AAAI (2019)
Tu, C.C., Ting, P.S., Chen, P.Y., Liu, S., Zhang, H., Yi, J., Hsieh, C.J., Cheng, S.M.: Autozoom: Autoencoder-based zeroth order optimization method for attacking black-box neural networks · 2019
Later among the works it cites.
Automation and Remote Control 80
Vorontsova, E.A., Gasnikov, A.V., Gorbunov, E.A., Dvurechenskii, P.E.: Accelerated gradient-free optimization methods with a non-euclidean proximal operator · 2019
Later among the works it cites.
IFAC-PapersOnLine 53
Beznosikov, A., Gorbunov, E., Gasnikov, A.: Derivative-free method for composite optimization with applications to decentralized distributed optimization · 2020
Closest in time.
European Journal of Operational Research 290
Dvurechensky, P., Gorbunov, E., Gasnikov, A.: An accelerated directional derivative method for smooth stochastic convex optimization · 2020
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Lan, G., Ouyang, Y.: Accelerated gradient sliding for structured convex optimization · 2016
Cited alongside, same era.
SIAM Journal on Optimization 26
Lan, G., Zhou, Y.: Conditional gradient sliding for convex optimization · 2016
Cited alongside, same era.
The Journal of Machine Learning Research 18
Allen-Zhu, Z.: Katyusha: The first direct acceleration of stochastic gradient methods · 2017
Cited alongside, same era.
Association for Computing Machinery, New York, NY, USA (2017)
Chen, P.Y., Zhang, H., Sharma, Y., Yi, J., Hsieh, C.J.: ZOO: Zeroth Order Optimization Based Black-Box Attacks to Deep Neural Networks without Training Substitute Models, p. 15–26 · 2017
Cited alongside, same era.
Dvurechensky, P., Gasnikov, A., Tiurin, A., Zholobov, V.: Unifying framework for accelerated randomized methods in convex optimization · 2017
Cited alongside, same era.
SIAM Journal on Optimization 27
Nesterov, Y., Stich, S.U.: Efficiency of the accelerated coordinate descent method on structured optimization problems · 2017
Cited alongside, same era.
Dvurechensky, P., Gasnikov, A., Gorbunov, E.: An accelerated method for derivative-free smooth stochastic convex optimization · 2018
Cited alongside, same era.
Mathematical programming 171
Lan, G., Zhou, Y.: An optimal randomized incremental gradient method · 2018
Cited alongside, same era.
Ivanova, A., Gasnikov, A., Dvurechensky, P., Dvinskikh, D., Tyurin, A., Vorontsova, E., Pasechnyuk, D.: Oracle complexity separation in convex optimization · 2020
Closest in time.
Journal of Inverse and Ill-posed Problems 29
Dvinskikh, D., Gasnikov, A.: Decentralized and parallel primal and dual accelerated methods for stochastic convex programming problems · 2021
Closest in time.
Computational Mathematics and Mathematical Physics 61
Gasnikov, A., Dvinskikh, D., Dvurechensky, P., Kamzolov, D., Matyukhin, V., Pasechnyuk, D., Tupitsa, N., Chernov, A.: Accelerated meta-algorithm for convex optimization problems · 2021
Closest in time.
Gladin, E., Sadiev, A., Gasnikov, A., Dvurechensky, P., Beznosikov, A., Alkousa, M.: Solving smooth min-min and min-max problems by mixed oracle algorithms · 2021
Closest in time.
In: International Conference on Optimization and Applications, pp. 20–37. Springer (2021)
Ivanova, A., Pasechnyuk, D., Grishchenko, D., Shulgin, E., Gasnikov, A., Matyukhin, V.: Adaptive catalyst for smooth convex optimization · 2021
Closest in time.
arXiv preprint arXiv:2111.00996 (2021)
Lan, G., Ouyang, Y.: Mirror-prox sliding methods for solving a class of monotone variational inequalities · 2021
Closest in time.
arXiv preprint arXiv:2102.07758 (2021)
Rogozin, A., Beznosikov, A., Dvinskikh, D., Kovalev, D., Dvurechensky, P., Gasnikov, A.: Decentralized distributed optimization for saddle point problems · 2021
Closest in time.
In: 2021 60th IEEE Conference on Decision and Control (CDC) (2021)
Rogozin, A., Bochko, M., Dvurechensky, P., Gasnikov, A., Lukoshkin, V.: An accelerated method for decentralized distributed stochastic optimization over time-varying graphs · 2021
Closest in time.
arXiv preprint arXiv:2107.05951 (2021)
Stepanov, I., Voronov, A., Beznosikov, A., Gasnikov, A.: One-point gradient-free methods for composite optimization with applications to distributed optimization · 2021
Closest in time.
Tominin, V., Tominin, Y., Borodich, E., Kovalev, D., Gasnikov, A., Dvurechensky, P.: On accelerated methods for saddle-point problems with composite structure · 2021
Closest in time.