Performance estimation toolbox (PESTO): Automated worst-case analysis of first-order optimization methods. - Adrien B. Taylor; Julien M. Hendrickx; François Glineur (CDC, 2017)

Smooth strongly convex interpolation and exact worst-case performance of first-order methods. - Adrien B. Taylor; Julien M. Hendrickx; François Glineur (Math. Program., 2017)

On the worst-case complexity of the gradient method with exact line search for smooth strongly convex functions. - Etienne de Klerk; François Glineur; Adrien B. Taylor (Optim. Lett., 2017)

Exact Worst-Case Performance of First-Order Methods for Composite Convex Optimization. - Adrien B. Taylor; Julien M. Hendrickx; François Glineur (SIAM J. Optim., 2017)

Convex interpolation and performance estimation of first-order methods for convex optimization. - Adrien B. Taylor (2017)

Lyapunov Functions for First-Order Methods: Tight Automated Convergence Guarantees. - Adrien B. Taylor; Bryan Van Scoy; Laurent Lessard (ICML, 2018)

Exact Worst-Case Convergence Rates of the Proximal Gradient Method for Composite Convex Minimization. - Adrien B. Taylor; Julien M. Hendrickx; François Glineur (J. Optim. Theory Appl., 2018)

Stochastic first-order methods: non-asymptotic and computer-aided analyses via potential functions. - Adrien B. Taylor; Francis R. Bach (COLT, 2019)

Complexity Guarantees for Polyak Steps with Momentum. - Mathieu Barré; Adrien B. Taylor; Alexandre d'Aspremont (COLT, 2020)

Convergence of Constrained Anderson Acceleration. - Mathieu Barré; Adrien B. Taylor; Alexandre d'Aspremont (CoRR, 2020)

Efficient first-order methods for convex minimization: a constructive approach. - Yoel Drori; Adrien B. Taylor (Math. Program., 2020)

Worst-Case Convergence Analysis of Inexact Gradient and Newton Methods Through Semidefinite Programming Performance Estimation. - Etienne de Klerk; François Glineur; Adrien B. Taylor (SIAM J. Optim., 2020)

Operator Splitting Performance Estimation: Tight Contraction Factors and Optimal Parameter Selection. - Ernest K. Ryu; Adrien B. Taylor; Carolina Bergeling; Pontus Giselsson (SIAM J. Optim., 2020)

Continuized Accelerations of Deterministic and Stochastic Gradient Descents, and of Gossip Algorithms. - Mathieu Even; Raphaël Berthier; Francis R. Bach; Nicolas Flammarion; Hadrien Hendrikx; Pierre Gaillard; Laurent Massoulié; Adrien B. Taylor (NeurIPS, 2021)

An optimal gradient method for smooth (possibly strongly) convex minimization. - Adrien B. Taylor; Yoel Drori (CoRR, 2021)

A Continuized View on Nesterov Acceleration. - Raphaël Berthier; Francis R. Bach; Nicolas Flammarion; Pierre Gaillard; Adrien B. Taylor (CoRR, 2021)

A Continuized View on Nesterov Acceleration for Stochastic Gradient Descent and Randomized Gossip. - Mathieu Even; Raphaël Berthier; Francis R. Bach; Nicolas Flammarion; Pierre Gaillard; Hadrien Hendrikx; Laurent Massoulié; Adrien B. Taylor (CoRR, 2021)

Acceleration Methods. - Alexandre d'Aspremont; Damien Scieur; Adrien B. Taylor (Found. Trends Optim., 2021)

Super-Acceleration with Cyclical Step-sizes. - Baptiste Goujaud; Damien Scieur; Aymeric Dieuleveut; Adrien B. Taylor; Fabian Pedregosa (AISTATS, 2022)

Fast Stochastic Composite Minimization and an Accelerated Frank-Wolfe Algorithm under Parallelization. - Benjamin Dubois-Taine; Francis R. Bach; Quentin Berthet; Adrien B. Taylor (NeurIPS, 2022)

Last-Iterate Convergence of Optimistic Gradient Method for Monotone Variational Inequalities. - Eduard Gorbunov; Adrien B. Taylor; Gauthier Gidel (NeurIPS, 2022)

PROX-QP: Yet another Quadratic Programming Solver for Robotics and beyond. - Antoine Bambade; Sarah El Kazdadi; Adrien B. Taylor; Justin Carpentier (Robotics: Science and Systems, 2022)

On the oracle complexity of smooth strongly convex minimization. - Yoel Drori; Adrien B. Taylor (J. Complex., 2022)

Optimal complexity and certification of Bregman first-order methods. - Radu-Alexandru Dragomir; Adrien B. Taylor; Alexandre d'Aspremont; Jérôme Bolte (Math. Program., 2022)

Convergence of a Constrained Vector Extrapolation Scheme. - Mathieu Barré; Adrien B. Taylor; Alexandre d'Aspremont (SIAM J. Math. Data Sci., 2022)

A note on approximate accelerated forward-backward methods with absolute and relative errors, and possibly strongly convex objectives. - Mathieu Barré; Adrien B. Taylor; Francis R. Bach (Open J. Math. Optim., 2022)

On Fundamental Proof Structures in First-Order Optimization. - Baptiste Goujaud; Aymeric Dieuleveut; Adrien B. Taylor (CDC, 2023)

Convergence of Proximal Point and Extragradient-Based Methods Beyond Monotonicity: the Case of Negative Comonotonicity. - Eduard Gorbunov; Adrien B. Taylor; Samuel Horváth; Gauthier Gidel (ICML, 2023)

Counter-Examples in First-Order Optimization: A Constructive Approach. - Baptiste Goujaud; Aymeric Dieuleveut; Adrien B. Taylor (IEEE Control. Syst. Lett., 2023)

Principled analyses and design of first-order methods with inexact proximal operators. - Mathieu Barré; Adrien B. Taylor; Francis R. Bach (Math. Program., 2023)

An optimal gradient method for smooth strongly convex minimization. - Adrien B. Taylor; Yoel Drori (Math. Program., 2023)

A Systematic Approach to Lyapunov Analyses of Continuous-Time Models in Convex Optimization. - Céline Moucer; Adrien B. Taylor; Francis R. Bach (SIAM J. Optim., 2023)

Leveraging augmented-Lagrangian techniques for differentiating over infeasible quadratic programs in machine learning. - Antoine Bambade; Fabian Schramm; Adrien B. Taylor; Justin Carpentier (ICLR, 2024)

Short Paper - Quadratic minimization: from conjugate gradient to an adaptive Polyak's momentum method with Polyak step-sizes. - Baptiste Goujaud; Adrien B. Taylor; Aymeric Dieuleveut (Open J. Math. Optim., 2024)

PEPit: computer-assisted worst-case analyses of first-order optimization methods in Python. - Baptiste Goujaud; Céline Moucer; François Glineur; Julien M. Hendrickx; Adrien B. Taylor; Aymeric Dieuleveut (Math. Program. Comput., 2024)

The Surprising Agreement Between Convex Optimization Theory and Learning-Rate Scheduling for Large Model Training. - Fabian Schaipp; Alexander Hägele; Adrien B. Taylor; Umut Simsekli; Francis R. Bach (CoRR, 2025)

Automated tight Lyapunov analysis for first-order methods. - Manu Upadhyaya; Sebastian Banert; Adrien B. Taylor; Pontus Giselsson (Math. Program., 2025)

Tight analyses of first-order methods with error feedback. - Daniel Berg Thomsen; Adrien B. Taylor; Aymeric Dieuleveut (CoRR, 2025)

Augmented Lagrangian methods for infeasible convex optimization problems and diverging proximal-point algorithms. - Roland Andrews; Justin Carpentier; Adrien B. Taylor (CoRR, 2025)