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This work is devoted to solving the composite optimization problem with the mixture oracle: for the smooth part of the problem, we have access to the gradient, and for the non-smooth part, only the one-point zero-order oracle is available.
A.S. Nemirovsky and D.B. Yudin, Problem complexity and method efficiency in optimization. (1983)
1983
Earlier work this paper cites.
D. Kempe, A. Dobra, and J. Gehrke, Gossip-based computation of aggregate information , in 44th Annual IEEE Symposium on Foundations of Computer Science, 2003. Proceedings. IEEE, 2003, pp. 482–491
2003
Earlier work this paper cites.
B. Taskar, V. Chatalbashev, D. Koller, and C. Guestrin, Learning structured prediction models: a large margin approach , 2004
2004
Earlier work this paper cites.
S. Boyd, A. Ghosh, B. Prabhakar, and D. Shah, Randomized gossip algorithms , IEEE transactions on information theory 52 (2006), pp. 2508–2530
2006
Earlier work this paper cites.
A. Nedic and A. Ozdaglar, Distributed subgradient methods for multi-agent optimization , IEEE Transactions on Automatic Control 54 (2009), pp. 48–61
2009
Earlier work this paper cites.
A. Juditsky, A. Nemirovski, and C. Tauvel, Solving variational inequalities with stochastic mirror-prox algorithm , Stochastic Systems 1 (2011), pp. 17–58
2011
Earlier work this paper cites.
S. Bubeck, N. Cesa-Bianchi, et al. , Regret analysis of stochastic and nonstochastic multi-armed bandit problems , Foundations and Trends® in Machine Learning 5 (2012), pp. 1–122
2012
Earlier work this paper cites.
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 (2015), pp. 2788–2806
2015
Earlier work this paper cites.
X. Lian, Y. Huang, Y. Li, and J. Liu, Asynchronous parallel stochastic gradient for nonconvex optimization , Advances in Neural Information Processing Systems 28 (2015), pp. 2737–2745
2015
Earlier work this paper cites.
S. Minsker, et al. , Geometric median and robust estimation in banach spaces , Bernoulli 21 (2015), pp. 2308–2335
2015
Earlier work this paper cites.
F. Bach and V. Perchet, Highly-smooth zero-th order online optimization , in Conference on Learning Theory . PMLR, 2016, pp. 257–283
2016
Earlier work this paper cites.
G. Lan, Gradient sliding for composite optimization , Mathematical Programming 159 (2016), pp. 201–235
2016
Earlier work this paper cites.
P.Y. Chen, H. Zhang, Y. Sharma, J. Yi, and C.J. Hsieh, Zoo , Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security - AISec ’17 (2017). Available at http://dx.doi.org/10.1145/3128572.3140448
2017
Earlier work this paper cites.
A.V. Gasnikov, E.A. Krymova, A.A. Lagunovskaya, I.N. Usmanova, and F.A. Fedorenko, Stochastic online optimization. single-point and multi-point non-linear multi-armed bandits. convex and strongly-convex case , Automation and remote control 78 (2017), pp. 224–234
2017
Earlier work this paper cites.
Y. Nesterov and V.G. Spokoiny, Random gradient-free minimization of convex functions , Foundations of Computational Mathematics 17 (2017), pp. 527–566
2017
Earlier work this paper cites.
2017
Cited alongside, same era.
O. Shamir, An optimal algorithm for bandit and zero-order convex optimization with two-point feedback , The Journal of Machine Learning Research 18 (2017), pp. 1703–1713
2017
Cited alongside, same era.
O. Shamir, An optimal algorithm for bandit and zero-order convex optimization with two-point feedback. , Journal of Machine Learning Research 18 (2017), pp. 1–11
2017
Cited alongside, same era.
K. Choromanski, M. Rowland, V. Sindhwani, R. Turner, and A. Weller, Structured Evolution with Compact Architectures for Scalable Policy Optimization , in Proceedings of the 35th International Conference on Machine Learning , J. Dy and A. Krause, eds., Proceedings of Machine Learning Research Vol. 80, 10–15 Jul. PMLR, 2018, pp. 970–978. Available at https://proceedings.mlr.press/v80/choromanski18a.html
H. Li, C. Fang, W. Yin, and Z. Lin, Decentralized accelerated gradient methods with increasing penalty parameters , IEEE Transactions on Signal Processing 68 (2020), pp. 4855–4870
2020
Later among the works it cites.
A. Akhavan, M. Pontil, and A. Tsybakov, Distributed zero-order optimization under adversarial noise , Advances in Neural Information Processing Systems 34 (2021), pp. 10209–10220
2021
Closest in time.
F. Bach, Learning theory from first principles (2021)
2021
Closest in time.
D. Dvinskikh and A. Gasnikov, Decentralized and parallel primal and dual accelerated methods for stochastic convex programming problems , Journal of Inverse and Ill-posed Problems 29 (2021), pp. 385–405
2021
Closest in time.
P. Dvurechensky, E. Gorbunov, and A. Gasnikov, An accelerated directional derivative method for smooth stochastic convex optimization , European Journal of Operational Research 290 (2021), pp. 601–621
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2018
Cited alongside, same era.
M. Fazel, R. Ge, S. Kakade, and M. Mesbahi, Global convergence of policy gradient methods for the linear quadratic regulator , in International Conference on Machine Learning . PMLR, 2018, pp. 1467–1476
2018
Cited alongside, same era.
Y. Nesterov, et al. , Lectures on convex optimization , Vol. 137, Springer, 2018
2018
Cited alongside, same era.
2019
Cited alongside, same era.
G. Lan, Lectures on Optimization Methods for Machine Learning , H. Milton Stewart School of Industrial and Systems Engineering Georgia Institute of Technology, Atlanta, GA, 2019
2019
Cited alongside, same era.
A. Akhavan, M. Pontil, and A. Tsybakov, Exploiting higher order smoothness in derivative-free optimization and continuous bandits , Advances in Neural Information Processing Systems 33 (2020), pp. 9017–9027
2020
Cited alongside, same era.
M.S. Alkousa, A.V. Gasnikov, D.M. Dvinskikh, D.A. Kovalev, and F.S. Stonyakin, Accelerated methods for saddle-point problem , Computational Mathematics and Mathematical Physics 60 (2020), pp. 1787–1809
2020
Cited alongside, same era.
A. Beznosikov, E. Gorbunov, and A. Gasnikov, Derivative-free method for composite optimization with applications to decentralized distributed optimization , IFAC-PapersOnLine 53 (2020), pp. 4038–4043
2020
Cited alongside, same era.
2020
Cited alongside, same era.
2021
Closest in time.
2021
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2021
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2021
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E. Gorbunov, P. Dvurechensky, and A. Gasnikov, An accelerated method for derivative-free smooth stochastic convex optimization , SIAM J. Optim. (2022)
2022
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E. Gorbunov, A. Rogozin, A. Beznosikov, D. Dvinskikh, and A. Gasnikov, Recent theoretical advances in decentralized distributed convex optimization , in High-Dimensional Optimization and Probability: With a View Towards Data Science , Springer, 2022, pp. 253–325
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