Optimal and parameter-free gradient minimization methods for convex and nonconvex optimization

Wiley-Interscience Series in Discrete Mathematics. John Wiley, XV (1983)

Nemirovski, A., Yudin, D.: Problem complexity and method efficiency in optimization · 1983

Earlier work this paper cites.

SIAM Journal on Optimization 2

Güler, O.: New proximal point algorithms for convex minimization · 1992

Earlier work this paper cites.

Journal of Complexity 8

Nemirovski, A.S.: Information-based complexity of linear operator equations · 1992

Earlier work this paper cites.

Kluwer Academic Publishers, Massachusetts (2004)

Nesterov, Y.E.: Introductory Lectures on Convex Optimization: A Basic Course · 2004

Earlier work this paper cites.

Optima. Mathematical Optimization Society Newsletter (88), 10–11 (2012)

Nesterov, Y.: How to make the gradients small · 2012

Earlier work this paper cites.

Mathematical programming 140

Nesterov, Y.: Gradient methods for minimizing composite functions · 2013

Earlier work this paper cites.

Mathematical Programming 145

Drori, Y., Teboulle, M.: Performance of first-order methods for smooth convex minimization: a novel approach · 2014

Earlier work this paper cites.

In: International Conference on Machine Learning, pp. 73–81. PMLR (2014)

Lin, Q., Xiao, L.: An adaptive accelerated proximal gradient method and its homotopy continuation for sparse optimization · 2014

Earlier work this paper cites.

Mathematical Programming 149

Lan, G.: Bundle-level type methods uniformly optimal for smooth and nonsmooth convex optimization · 2015

Earlier work this paper cites.

Mathematical Programming 152

Nesterov, Y.E.: Universal gradient methods for convex optimization problems · 2015

Earlier work this paper cites.

Mathematical Programming 156

Ghadimi, S., Lan, G.: Accelerated gradient methods for nonconvex nonlinear and stochastic programming · 2016

Cited alongside, same era.

Advances in Neural Information Processing Systems 31

Allen-Zhu, Z.: How to make the gradients small stochastically: Even faster convex and nonconvex sgd · 2018

Cited alongside, same era.

Springer (2018)

Nesterov, Y.: Lectures on convex optimization, vol. 137 · 2018

Cited alongside, same era.

Computational Optimization and Applications 73

Chen, Y., Lan, G., Ouyang, Y., Zhang, W.: Fast bundle-level methods for unconstrained and ball-constrained convex optimization · 2019

Cited alongside, same era.

In: Conference on Learning Theory, pp. 1319–1345. PMLR (2019)

Foster, D.J., Sekhari, A., Shamir, O., Srebro, N., Sridharan, K., Woodworth, B.: The complexity of making the gradient small in stochastic convex optimization · 2019

Cited alongside, same era.

SIAM Journal on Optimization 29

Lan, G., Yang, Y.: Accelerated stochastic algorithms for nonconvex finite-sum and multiblock optimization · 2019

Journal of optimization theory and applications 188

Kim, D., Fessler, J.A.: Optimizing the efficiency of first-order methods for decreasing the gradient of smooth convex functions · 2021

Later among the works it cites.

Advances in Neural Information Processing Systems 34

Lee, J., Park, C., Ryu, E.: A geometric structure of acceleration and its role in making gradients small fast · 2021

Later among the works it cites.

Optimization Methods and Software 36

Nesterov, Y., Gasnikov, A., Guminov, S., Dvurechensky, P.: Primal–dual accelerated gradient methods with small-dimensional relaxation oracle · 2021

Later among the works it cites.

SIAM Journal on Optimization 32

Diakonikolas, J., Wang, P.: Potential function-based framework for minimizing gradients in convex and min-max optimization · 2022

Later among the works it cites.

foundations of computational mathematics 22

Renegar, J., Grimmer, B.: A simple nearly optimal restart scheme for speeding up first-order methods · 2022

Later among the works it cites.

arXiv preprint arXiv:2310.10082 (2023)