Fetching the paper…
Reading the bibliography…
We study the worst-case convergence rates of the proximal gradient method for minimizing the sum of a smooth strongly convex function and a non-smooth convex function whose proximal operator is available.
Optimization Software New York (1987)
Polyak, B.T.: Introduction to Optimization · 1987
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.
Athena Scientific (1999)
Bertsekas, D.P.: Nonlinear Programming, 2nd edn · 1999
Earlier work this paper cites.
Springer Science & Business Media (2004)
Nesterov, Y.: Introductory lectures on convex optimization: A basic course, vol. 87 · 2004
Earlier work this paper cites.
In: Advances in neural information processing systems, pp. 1458–1466 (2011)
Schmidt, M., Le Roux, N., Bach, F.: Convergence rates of inexact proximal-gradient methods for convex optimization · 2011
Earlier work this paper cites.
In: Fixed-point algorithms for inverse problems in science and engineering, pp. 185–212. Springer (2011)
Combettes, P.L., Pesquet, J.C.: Proximal splitting methods in signal processing · 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.
Foundations and Trends in optimization 1
Parikh, N., Boyd, S.: Proximal algorithms · 2013
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
Cited alongside, same era.
Ph.D. thesis, Tel-Aviv University (2014)
Drori, Y.: Contributions to the complexity analysis of optimization algorithms · 2014
Cited alongside, same era.
Mathematical Programming 146
Devolder, O., Glineur, F., Nesterov, Y.: First-order methods of smooth convex optimization with inexact oracle · 2014
Cited alongside, same era.
Optimization Letters 9
Zhang, H., Cheng, L.: Restricted strong convexity and its applications to convergence analysis of gradient-type methods in convex optimization · 2015
Cited alongside, same era.
Foundations and Trends® in Machine Learning 8
Bubeck, S.: Convex optimization: Algorithms and complexity · 2015
Cited alongside, same era.
Applied and Computational Mathematics an International Journal 15
Ryu, E., Boyd, S.: A primer on monotone operator methods · 2016
Cited alongside, same era.
Mathematical Programming 159
Kim, D., Fessler, J.A.: Optimized first-order methods for smooth convex minimization · 2016
Later among the works it cites.
Mathematical Programming 165
Bolte, J., Nguyen, T.P., Peypouquet, J., Suter, B.W.: From error bounds to the complexity of first-order descent methods for convex functions · 2017
Closest in time.
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
Closest in time.
SIAM Journal on Optimization 27
Taylor, A.B., Hendrickx, J.M., Glineur, F.: Exact worst-case performance of first-order methods for composite convex optimization · 2017
Closest in time.
Optimization Letters 11
de Klerk, E., Glineur, F., Taylor, A.B.: On the worst-case complexity of the gradient method with exact line search for smooth strongly convex functions · 2017
Closest in time.
Ph.D. thesis, Université catholique de Louvain (2017)
Taylor, A.B.: Convex interpolation and performance estimation of first-order methods for convex optimization · 2017
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
SIAM Journal on Optimization 26
Lessard, L., Recht, B., Packard, A.: Analysis and design of optimization algorithms via integral quadratic constraints · 2016
Cited alongside, same era.
In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 795–811. Springer (2016)
Karimi, H., Nutini, J., Schmidt, M.: Linear convergence of gradient and proximal-gradient methods under the polyak-łojasiewicz condition · 2016
Cited alongside, same era.
Mathematical Programming pp. 1–39
Necoara, I., Nesterov, Y., Glineur, F.: Linear convergence of first order methods for non-strongly convex optimization
Cited in the paper.
Closest in time.
Journal of Complexity 39
Drori, Y.: The exact information-based complexity of smooth convex minimization · 2017
Closest in time.
In: Proceedings of the 56th IEEE Conference on Decision and Control (CDC 2017) (2017)
Taylor, A., Hendrickx, J., Glineur, F.: Performance estimation toolbox (PESTO): automated worst-case analysis of first-order optimization methods · 2017
Closest in time.