| 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 | 274 | 2017 |
| Acceleration methods A d'Aspremont, D Scieur, A Taylor Foundations and Trends® in Optimization 5 (1-2), 1-245, 2021 | 168 | 2021 |
| 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 | 167 | 2017 |
| 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 | 114 | 2020 |
| Optimal complexity and certification of Bregman first-order methods RA Dragomir, AB Taylor, A d’Aspremont, J Bolte Mathematical Programming, 1-43, 2022 | 108 | 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 | 106 | 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 | 104 | 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 | 98 | 2019 |
| 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 | 78 | 2018 |
| 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 | 78 | 2017 |
| Convex interpolation and performance estimation of first-order methods for convex optimization. AB Taylor Catholic University of Louvain, Louvain-la-Neuve, Belgium, 2017 | 68 | 2017 |
| 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 | 67 | 2022 |
| Efficient first-order methods for convex minimization: a constructive approach Y Drori, AB Taylor Mathematical Programming 184 (1), 183-220, 2020 | 65 | 2020 |
| An optimal gradient method for smooth strongly convex minimization A Taylor, Y Drori Mathematical Programming 199 (1), 557-594, 2023 | 64* | 2023 |
| 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 | 58 | 2020 |
| 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 | 53 | 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 | 52 | 2022 |
| PEPit: computer-assisted worst-case analyses of first-order optimization methods in Python B Goujaud, C Moucer, F Glineur, JM Hendrickx, AB Taylor, A Dieuleveut Mathematical Programming Computation 16 (3), 337-367, 2024 | 49 | 2024 |
| 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 | 45* | 2021 |
| Principled analyses and design of first-order methods with inexact proximal operators M Barré, AB Taylor, F Bach Mathematical Programming 201 (1), 185-230, 2023 | 37 | 2023 |
François GlineurProfessor, Université catholique de Louvain (UCLouvain) - ICTEAM / INMA & CORE - EPLVerified email at uclouvain.be
