| Smooth strongly convex interpolation and exact worst-case performance of first-order methods AB Taylor, JM Hendrickx, F Glineur Mathematical Programming 161 (1-2), 307-345, 2017 | 95 | 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 | 61* | 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 | 35 | 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 | 31 | 2019 |
| 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 | 31 | 2018 |
| 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 | 28* | 2020 |
| Efficient first-order methods for convex minimization: a constructive approach Y Drori, AB Taylor Mathematical Programming 184 (1), 183-220, 2020 | 20 | 2020 |
| Convex interpolation and performance estimation of first-order methods for convex optimization. AB Taylor Catholic University of Louvain, Louvain-la-Neuve, Belgium, 2017 | 20 | 2017 |
| Optimal complexity and certification of Bregman first-order methods RA Dragomir, AB Taylor, A d’Aspremont, J Bolte Mathematical Programming, 1-43, 2021 | 19 | 2021 |
| 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 | 19 | 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 | 19 | 2017 |
| 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 | 12 | 2020 |
| Acceleration methods A d'Aspremont, D Scieur, A Taylor arXiv preprint arXiv:2101.09545, 2021 | 5 | 2021 |
| An optimal gradient method for smooth strongly convex minimization A Taylor, Y Drori arXiv preprint arXiv:2101.09741, 2021 | 4 | 2021 |
| 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 | 4 | 2020 |
| On the oracle complexity of smooth strongly convex minimization Y Drori, A Taylor arXiv preprint arXiv:2101.09740, 2021 | 3 | 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 | 3 | 2020 |
| Convergence of constrained anderson acceleration M Barré, A Taylor, A d'Aspremont arXiv preprint arXiv:2010.15482, 2020 | 2 | 2020 |
| A Continuized View on Nesterov Acceleration R Berthier, F Bach, N Flammarion, P Gaillard, A Taylor arXiv preprint arXiv:2102.06035, 2021 | | 2021 |
François GlineurProfessor, Université catholique de Louvain (UCLouvain) - ICTEAM & CORE - EPLVerified email at uclouvain.be
