Fetching the paper…
Reading the bibliography…
In this paper, we prove new complexity bounds for zeroth-order methods in non-convex optimization with inexact observations of the objective function values.
The Computer Journal 3
Rosenbrock, H.H.: An automatic method for finding the greatest or least value of a function · 1960
Earlier work this paper cites.
Ann. Math. Statist. 38
Fabian, V.: Stochastic approximation of minima with improved asymptotic speed · 1967
Earlier work this paper cites.
Dover Books on Mathematics. Dover Publications (1973)
Brent, R.: Algorithms for Minimization Without Derivatives · 1973
Earlier work this paper cites.
Ekonomika i matematicheskie metody 20
Kim, K., Nesterov, Y., Skokov, V., Cherkasskii, B.: Effektivnii algoritm vychisleniya proisvodnyh i ekstremalnye zadachi (efficient algorithm for calculation of derivatives and extreme problems) · 1984
Earlier work this paper cites.
John Wiley & Sons, Inc., New York, NY, USA (2003)
Spall, J.C.: Introduction to Stochastic Search and Optimization, 1 edn · 2003
Earlier work this paper cites.
Society for Industrial and Applied Mathematics (2009)
Conn, A.R., Scheinberg, K., Vicente, L.N.: Introduction to Derivative-Free Optimization · 2009
Earlier work this paper cites.
SIAM Journal on Optimization 23
Ghadimi, S., Lan, G.: Stochastic first- and zeroth-order methods for nonconvex stochastic programming · 2013
Earlier work this paper cites.
Mathematical Programming 155
Saeed Ghadimi, G.L., Zhang, H.: Mini-batch stochastic approximation methods for nonconvex stochastic composite optimization · 2013
Cited alongside, same era.
Mathematical Programming 152
Nesterov, Y.: Universal gradient methods for convex optimization problems · 2015
Cited alongside, same era.
Foundations of Computational Mathematics 17
Nesterov, Y., Spokoiny, V.: Random gradient-free minimization of convex functions · 2015
Cited alongside, same era.
Dvurechensky, P.: Gradient method with inexact oracle for composite non-convex optimization · 2017
Cited alongside, same era.
Baydin, A.G., Pearlmutter, B.A., Radul, A.A., Siskind, J.M.: Automatic differentiation in machine learning: a survey · 2018
Cited alongside, same era.
MIT press (2018)
Sutton, R.S., Barto, A.G.: Reinforcement learning: An introduction · 2018
Later among the works it cites.
Berahas, A.S., Cao, L., Choromanski, K., Scheinberg, K.: A theoretical and empirical comparison of gradient approximations in derivative-free optimization · 2019
Later among the works it cites.
Berahas, A.S., Cao, L., Scheinberg, K.: Global convergence rate analysis of a generic line search algorithm with noise · 2019
Later among the works it cites.
Acta Numerica 28
Larson, J., Menickelly, M., Wild, S.M.: Derivative-free optimization methods · 2019
Later among the works it cites.
Bolte, J., Glaudin, L., Pauwels, E., Serrurier, M.: A hölderian backtracking method for min-max and min-min problems · 2020
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Advances in Neural Information Processing Systems 31
Liu, S., Kailkhura, B., Chen, P.Y., Ting, P., Chang, S., Amini, L.: Zeroth-order stochastic variance reduction for nonconvex optimization · 2018
Cited alongside, same era.
Proceedings of the AAAI Conference on Artificial Intelligence 34
Wang, J., Liu, Y., Li, B.: Reinforcement learning with perturbed rewards · 2020
Closest in time.