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We provide a lower bound showing that the $O(1/k)$ convergence rate of the NoLips method (a.k.a.
Bull. Soc. Math. Fr. 93
Moreau, J.J.: Proximité et dualité dans un espace hilbertien · 1965
Earlier work this paper cites.
Rockafellar, R.T.: Convex Analysis (1970)
1970
Earlier work this paper cites.
Nemirovski, A., Yudin, D.B.: Problem Complexity and Method Efficiency in Optimization (1983)
1983
Earlier work this paper cites.
Nesterov, Y.: A Method for Solving a Convex Programming Problem with Convergence Rate O(1/kˆ2) 27
1983
Earlier work this paper cites.
Journal of Optimization Theory and Applications 73
Censor, Y., Zenios, S.A.: Proximal Minimization Algorithm with D-functions · 1992
Earlier work this paper cites.
Mathematics of Operations Research 17
Teboulle, M.: Entropic Proximal Mappings with Applications to Nonlinear Programming · 1992
Earlier work this paper cites.
Mathematics of Operations Research 18
Eckstein, J.: Nonlinear Proximal Point Algorithms Using Bregman Functions, with Applications to Convex Programming · 1993
Earlier work this paper cites.
SIAM Review 38
Vandenberghe, L., Boyd, S.: Semidefinite Programming · 1996
Earlier work this paper cites.
SIAM Journal on Optimization 12
Ben-tal, A., Margalit, T., Nemirovski, A.: The Ordered Subsets Mirror Descent Optimization Method with Applications to Tomography · 2001
Earlier work this paper cites.
Operations Research Letters 31
Beck, A., Teboulle, M.: Mirror Descent and Nonlinear Projected Subgradient Methods for Convex Optimization · 2003
Earlier work this paper cites.
Springer Publishing Company, Inc (2003)
Nesterov, Y.: Introductory Lectures on Convex Optimization: A Basic Course, 1 edn · 2003
Earlier work this paper cites.
In: In Proceedings of the CACSD Conference (2004)
Lofberg, J.: YALMIP : A Toolbox for Modeling and Optimization in MATLAB · 2004
Earlier work this paper cites.
SIAM Journal on Optimization 16
Auslender, A., Teboulle, M.: Interior Gradient and Proximal Methods for Convex and Conic Optimization · 2006
Earlier work this paper cites.
SIAM Journal on Imaging Sciences 2
Beck, A., Teboulle, M.: A Fast Iterative Shrinkage-Thresholding Algorithm · 2009
Earlier work this paper cites.
Inverse Problems (2009)
Bertero, M., Boccaci, P., Desidera, G., Vicidomini, G.: Image Deblurring with Poisson Data: From Cells to Galaxies · 2009
Cited alongside, same era.
In: S.S. Wright, S. Nowozin, S. J. (eds.) Optimization for Machine Learning, pp. 121–147. MIT Press (2010)
Juditsky, A., Nemirovski, A.: First Order Methods for Nonsmooth Convex Large-Scale Optimization , I : General Purpose Methods · 2010
Cited alongside, same era.
Springer Publishing Company, Inc. (2011)
Bauschke, H.H., Combettes, P.L.: Convex Analysis and Monotone Operator Theory in Hilbert Spaces · 2011
Cited alongside, same era.
Lecture Notes (2011)
Bubeck, S.: Introduction to online optimization · 2011
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.
SIAM Journal on Imaging Sciences 25
Bach, F.: Duality Between Subgradient and Conditional Gradient Methods · 2015
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
Later among the works it cites.
arXiv preprint arXiv:1709.03594 (2017)
Woodworth, B., Srebro, N.: Lower Bound for Randomized First Order Convex Optimization · 2017
Later among the works it cites.
SIAM Journal on Optimization 28
Bolte, J., Sabach, S., Teboulle, M., Vaisbourd, Y.: First Order Methods Beyond Convexity and Lipschitz Gradient Continuity with Applications to Quadratic Inverse Problems · 2018
Later among the works it cites.
ArXiv preprint arXiv:1808.03045v1 (2018)
Hanzely, F., Richtarik, P., Xiao, L.: Accelerated Bregman Proximal Gradient Methods for Relatively Smooth Convex Optimization · 2018
Later among the works it cites.
SIAM Journal on Optimization 28
Lu, H., Freund, R.M., Nesterov, Y.: Relatively-Smooth Convex Optimization by First-Order Methods, and Applications · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
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Cited alongside, same era.
Journal of Complexity 31
Guzmán, C., Nemirovski, A.: On Lower Complexity Bounds for Large-Scale Smooth Convex Optimization · 2015
Cited alongside, same era.
In: Advances in Neural Information Processing Systems 28, pp. 2845—-2853 (2015)
Walid, K., Bayen, A., Bartlett, P.L.: Accelerated Mirror Descent In Continuous and Discrete Time · 2015
Cited alongside, same era.
Mathematical Programming 160
Drori, Y., Teboulle, M.: An Optimal Variant of Kelley’s Cutting-Plane Method · 2016
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.
Mathematics of Operations Research 42
Bauschke, H.H., Bolte, J., Teboulle, M.: A Descent Lemma Beyond Lipschitz Gradient Continuity: First-Order Methods Revisited and Applications · 2017
Cited alongside, same era.
Journal of Complexity 39
Drori, Y.: The Exact Information-Based Complexity of Smooth Convex Minimization · 2017
Cited alongside, same era.
CORE Discussion Paper (2018)
Nesterov, Y.: Implementable Tensor Methods in Unconstrained Convex Optimization · 2018
Later among the works it cites.
Mathematical Programming 170
Teboulle, M.: A simplified view of first order methods for optimization · 2018
Later among the works it cites.
Journal of Optimization Theory and Applications 182
Bauschke, H.H., Bolte, J., Chen, J., Teboulle, M., Wang, X.: On Linear Convergence of Non-Euclidean Gradient Methods without Strong Convexity and Lipschitz Gradient Continuity · 2019
Closest in time.
arXiv preprint arXiv:1908.03878 (2019)
Bùi, M.N., Combettes, P.L.: Bregman Forward-Backward Operator Splitting · 2019
Closest in time.
URL http://docs.mosek.com/9.0/toolbox/index.html
Mosek, A.: The MOSEK optimization toolbox for MATLAB manual. Version 9.0. (2019) · 2019
Closest in time.
arXiv preprint arXiv:1904.03537 (2019)
Mukkamala, M.C., Ochs, P., Pock, T., Sabach, S.: Convex-Concave Backtracking for Inertial Bregman Proximal Gradient Algorithms in Non-Convex Optimization · 2019
Closest in time.
Dragomir, R.A., D’Aspremont, A., Bolte, J.: Quartic First-Order Methods for Low Rank Minimization · 2020
Closest in time.
In: Proceedings of the 37th International Conference on Machine Learning, Proceedings of Machine Learning Research , vol. 119, pp. 2658–2667. PMLR (2020)
Drori, Y., Shamir, O.: The complexity of finding stationary points with stochastic gradient descent · 2020
Closest in time.
Mathematical Programming 184
Drori, Y., Taylor, A.B.: Efficient First-Order Methods for Convex Minimization: a Constructive Approach · 2020
Closest in time.