Smooth strongly convex interpolation and exact worst-case performance of first-order methods AB Taylor, JM Hendrickx, F Glineur Mathematical Programming 161, 307-345, 2017 | 239 | 2017 |
Exact worst-case performance of first-order methods for composite convex optimization AB Taylor, JM Hendrickx, F Glineur SIAM Journal on Optimization 27 (3), 1283-1313, 2017 | 149 | 2017 |
Acceleration methods A d'Aspremont, D Scieur, A Taylor Foundations and Trends® in Optimization 5 (1-2), 1-245, 2021 | 137 | 2021 |
Operator splitting performance estimation: Tight contraction factors and optimal parameter selection EK Ryu, AB Taylor, C Bergeling, P Giselsson SIAM Journal on Optimization 30 (3), 2251-2271, 2020 | 104 | 2020 |
Optimal complexity and certification of Bregman first-order methods RA Dragomir, AB Taylor, A d’Aspremont, J Bolte Mathematical Programming, 1-43, 2022 | 96 | 2022 |
Exact worst-case convergence rates of the proximal gradient method for composite convex minimization AB Taylor, JM Hendrickx, F Glineur Journal of Optimization Theory and Applications 178, 455-476, 2018 | 91 | 2018 |
On the worst-case complexity of the gradient method with exact line search for smooth strongly convex functions E De Klerk, F Glineur, AB Taylor Optimization Letters 11, 1185-1199, 2017 | 89 | 2017 |
Stochastic first-order methods: non-asymptotic and computer-aided analyses via potential functions A Taylor, F Bach Proceedings of the Thirty-Second Conference on Learning Theory (COLT), 2019 | 86 | 2019 |
Performance estimation toolbox (PESTO): Automated worst-case analysis of first-order optimization methods AB Taylor, JM Hendrickx, F Glineur 2017 IEEE 56th Annual Conference on Decision and Control (CDC), 1278-1283, 2017 | 72 | 2017 |
Lyapunov functions for first-order methods: Tight automated convergence guarantees A Taylor, B Van Scoy, L Lessard International Conference on Machine Learning (ICML) 80, 4897--4906, 2018 | 68 | 2018 |
Convex interpolation and performance estimation of first-order methods for convex optimization. AB Taylor Catholic University of Louvain, Louvain-la-Neuve, Belgium, 2017 | 60 | 2017 |
An optimal gradient method for smooth strongly convex minimization A Taylor, Y Drori Mathematical Programming 199 (1), 557-594, 2023 | 55* | 2023 |
Efficient first-order methods for convex minimization: a constructive approach Y Drori, AB Taylor Mathematical Programming 184 (1), 183-220, 2020 | 54 | 2020 |
Worst-case convergence analysis of inexact gradient and Newton methods through semidefinite programming performance estimation E De Klerk, F Glineur, AB Taylor SIAM Journal on Optimization 30 (3), 2053-2082, 2020 | 48 | 2020 |
Prox-qp: Yet another quadratic programming solver for robotics and beyond A Bambade, S El-Kazdadi, A Taylor, J Carpentier RSS 2022-Robotics: Science and Systems, 2022 | 46 | 2022 |
Complexity Guarantees for Polyak Steps with Momentum M Barré, A Taylor, A d'Aspremont Proceedings of the Thirty-Third Conference on Learning Theory (COLT), 2020 | 45 | 2020 |
Last-iterate convergence of optimistic gradient method for monotone variational inequalities E Gorbunov, A Taylor, G Gidel Advances in Neural Information Processing Systems 35, 2022 | 40 | 2022 |
Continuized accelerations of deterministic and stochastic gradient descents, and of gossip algorithms M Even, R Berthier, F Bach, N Flammarion, H Hendrikx, P Gaillard, ... Advances in Neural Information Processing Systems 34, 28054-28066, 2021 | 36* | 2021 |
PEPit: computer-assisted worst-case analyses of first-order optimization methods in Python B Goujaud, C Moucer, F Glineur, J Hendrickx, A Taylor, A Dieuleveut arXiv preprint arXiv:2201.04040, 2022 | 33 | 2022 |
Super-acceleration with cyclical step-sizes B Goujaud, D Scieur, A Dieuleveut, AB Taylor, F Pedregosa International Conference on Artificial Intelligence and Statistics, 3028-3065, 2022 | 30 | 2022 |