Optimizing the Efficiency of First-Order Methods for Decreasing the Gradient of Smooth Convex Functions

Dokl. Akad. Nauk. USSR 269

Nesterov, Y.: A method for unconstrained convex minimization problem with the rate of convergence O ⁡ ( 1 / k 2 ) O(1/k^{2}) · 1983

Earlier work this paper cites.

J. of Complexity 8

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

Earlier work this paper cites.

Optim. Meth. Software 11

Sturm, J.: Using SeDuMi 1.02, A MATLAB toolbox for optimization over symmetric cones · 1999

Earlier work this paper cites.

Kluwer (2004)

Nesterov, Y.: Introductory lectures on convex optimization: A basic course · 2004

Earlier work this paper cites.

In: Proc. of the CACSD Conference (2004)

Löfberg, J.: YALMIP: A toolbox for modeling and optimization in MATLAB · 2004

Earlier work this paper cites.

Mathematical Programming 103

Nesterov, Y.: Smooth minimization of non-smooth functions · 2005

Earlier work this paper cites.

In: V. Blondel, S. Boyd, H. Kimura (eds.) Recent Advances in Learning and Control, Lecture Notes in Control and Information Sciences, pp. 95–110. Springer-Verlag Limited (2008)

Grant, M., Boyd, S.: Graph implementations for nonsmooth convex programs · 2008

Earlier work this paper cites.

SIAM J. Imaging Sci. 2

Beck, A., Teboulle, M.: A fast iterative shrinkage-thresholding algorithm for linear inverse problems · 2009

Earlier work this paper cites.

URL http://www.mathopt.org/?nav=optima_newsletter

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

Earlier work this paper cites.

http://cvxr.com/cvx (2012)

CVX Research Inc.: CVX: Matlab software for disciplined convex programming, version 2.0 · 2012

Earlier work this paper cites.

SIAM J. Optim. 23

Monteiro, R.D.C., Svaiter, B.F.: An accelerated hybrid proximal extragradient method for convex optimization and its implications to second-order methods · 2013

Cited alongside, same era.

Mathematical Programming 145

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

Cited alongside, same era.

Mathematical Programming 159

Kim, D., Fessler, J.A.: Optimized first-order methods for smooth convex minimization · 2016

Cited alongside, same era.

J. Complexity 39

Drori, Y.: The exact information-based complexity of smooth convex minimization · 2016

Cited alongside, same era.

Mathematical Programming 156

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

Cited alongside, same era.

Mathematical Programming 161

Taylor, A.B., Hendrickx, J.M., Glineur, F.: Smooth strongly convex interpolation and exact worst-case performance of first- order methods · 2017

Cited alongside, same era.

SIAM J. Optim. 28

Kim, D., Fessler, J.A.: Another look at the Fast Iterative Shrinkage/Thresholding Algorithm (FISTA) · 2018

Closest in time.

SIAM J. Optim. 28

Kim, D., Fessler, J.A.: Generalizing the optimized gradient method for smooth convex minimization · 2018

Closest in time.

In: Proc. Intl. Conf. on Learning Representations (2018)

Soudry, D., Hoffer, E., Nacson, M.S., Srebro, N.: The implicit bias of gradient descent on separable data · 2018

Closest in time.

J. Optim. Theory Appl. 178

Taylor, A.B., Hendrickx, J.M., Glineur, F.: Exact worst-case convergence rates of the proximal gradient method for composite convex minimization · 2018

Closest in time.

Mathematical Programming (2019)

Carmon, Y., Duchi, J.C., Hinder, O., Sidford, A.: Lower bounds for finding stationary points II: First-order methods · 2019

Closest in time.

In: AISTATS (2019)

Nacson, M.S., Lee, J.D., Gunasekar, S., Savarese, P.H.P., Srebro, N., Soudry, D.: Convergence of gradient descent on separable data · 2019

Closest in time.