Adrien Taylor
Adrien Taylor
Inria - ENS Paris
Verified email at - Homepage
Cited by
Cited by
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
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
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
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
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
Acceleration methods
A d'Aspremont, D Scieur, A Taylor
Foundations and Trends® in Optimization 5 (1-2), 1-245, 2021
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
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
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
Convex interpolation and performance estimation of first-order methods for convex optimization.
AB Taylor
Catholic University of Louvain, Louvain-la-Neuve, Belgium, 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
Efficient first-order methods for convex minimization: a constructive approach
Y Drori, AB Taylor
Mathematical Programming 184 (1), 183-220, 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
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
An optimal gradient method for smooth strongly convex minimization
A Taylor, Y Drori
Mathematical Programming, 1-38, 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
Principled analyses and design of first-order methods with inexact proximal operators
M Barré, A Taylor, F Bach
arXiv preprint arXiv:2006.06041, 2020
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
On the oracle complexity of smooth strongly convex minimization
Y Drori, A Taylor
Journal of Complexity 68, 101590, 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
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