| Smooth strongly convex interpolation and exact worst-case performance of first-order methods AB Taylor, JM Hendrickx, F Glineur Mathematical Programming 161 (1), 307-345, 2017 | 142 | 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 | 91 | 2017 |
| 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 (7), 1185-1199, 2017 | 57 | 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 | 56 | 2020 |
| 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 (2), 455-476, 2018 | 56 | 2018 |
| 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 | 51 | 2019 |
| Optimal complexity and certification of Bregman first-order methods RA Dragomir, AB Taylor, A d’Aspremont, J Bolte Mathematical Programming 194 (1), 41-83, 2022 | 50 | 2022 |
| Acceleration methods A d'Aspremont, D Scieur, A Taylor Foundations and Trends® in Optimization 5 (1-2), 1-245, 2021 | 49 | 2021 |
| 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 | 44 | 2017 |
| Convex interpolation and performance estimation of first-order methods for convex optimization. AB Taylor Catholic University of Louvain, Louvain-la-Neuve, Belgium, 2017 | 36 | 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 | 35 | 2018 |
| Efficient first-order methods for convex minimization: a constructive approach Y Drori, AB Taylor Mathematical Programming 184 (1), 183-220, 2020 | 34 | 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 | 27 | 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 | 18 | 2020 |
| An optimal gradient method for smooth strongly convex minimization A Taylor, Y Drori Mathematical Programming, 1-38, 2022 | 15* | 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 | 11* | 2021 |
| Principled analyses and design of first-order methods with inexact proximal operators M Barré, A Taylor, F Bach arXiv preprint arXiv:2006.06041, 2020 | 11 | 2020 |
| On the oracle complexity of smooth strongly convex minimization Y Drori, A Taylor Journal of Complexity 68, 101590, 2022 | 9 | 2022 |
| 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 | 8 | 2022 |
| A note on approximate accelerated forward-backward methods with absolute and relative errors, and possibly strongly convex objectives M Barré, A Taylor, F Bach Open Journal of Mathematical Optimization 3, 1-15, 2022 | 8 | 2022 |
François GlineurProfessor, Université catholique de Louvain (UCLouvain) - ICTEAM & CORE - EPLVerified email at uclouvain.be
