Barrier Operators and Associated Gradient-Like Dynamical Systems for Constrained Minimization Problems. - Jérôme Bolte; Marc Teboulle (SIAM J. Control. Optim., 2003)

Hessian Riemannian Gradient Flows in Convex Programming. - Felipe Alvarez; Jérôme Bolte; Olivier Brahic (SIAM J. Control. Optim., 2004)

The [barred L]ojasiewicz Inequality for Nonsmooth Subanalytic Functions with Applications to Subgradient Dynamical Systems. - Jérôme Bolte; Aris Daniilidis; Adrian S. Lewis (SIAM J. Optim., 2007)

Clarke Subgradients of Stratifiable Functions. - Jérôme Bolte; Aris Daniilidis; Adrian S. Lewis; Masahiro Shiota (SIAM J. Optim., 2007)

A Unifying Local Convergence Result for Newton's Method in Riemannian Manifolds. - Felipe Alvarez; Jérôme Bolte; Julien Munier (Found. Comput. Math., 2008)

On the convergence of the proximal algorithm for nonsmooth functions involving analytic features. - Hédy Attouch; Jérôme Bolte (Math. Program., 2009)

Tame functions are semismooth. - Jérôme Bolte; Aris Daniilidis; Adrian S. Lewis (Math. Program., 2009)

Alternating proximal algorithm for blind image recovery. - Jérôme Bolte; Patrick L. Combettes; Jean-Christophe Pesquet (ICIP, 2010)

Proximal Alternating Minimization and Projection Methods for Nonconvex Problems: An Approach Based on the Kurdyka-Lojasiewicz Inequality. - Hédy Attouch; Jérôme Bolte; Patrick Redont; Antoine Soubeyran (Math. Oper. Res., 2010)

Generic Optimality Conditions for Semialgebraic Convex Programs. - Jérôme Bolte; Aris Daniilidis; Adrian S. Lewis (Math. Oper. Res., 2011)

Asymptotic expansions for interior penalty solutions of control constrained linear-quadratic problems. - Felipe Alvarez; Jérôme Bolte; J. Frédéric Bonnans; Francisco J. Silva (Math. Program., 2012)

Convergence of descent methods for semi-algebraic and tame problems: proximal algorithms, forward-backward splitting, and regularized Gauss-Seidel methods. - Hédy Attouch; Jérôme Bolte; Benar Fux Svaiter (Math. Program., 2013)

Proximal alternating linearized minimization for nonconvex and nonsmooth problems. - Jérôme Bolte; Shoham Sabach; Marc Teboulle (Math. Program., 2014)

Definable Zero-Sum Stochastic Games. - Jérôme Bolte; Stéphane Gaubert; Guillaume Vigeral (Math. Oper. Res., 2015)

Majorization-Minimization Procedures and Convergence of SQP Methods for Semi-Algebraic and Tame Programs. - Jérôme Bolte; Edouard Pauwels (Math. Oper. Res., 2016)

A Descent Lemma Beyond Lipschitz Gradient Continuity: First-Order Methods Revisited and Applications. - Heinz H. Bauschke; Jérôme Bolte; Marc Teboulle (Math. Oper. Res., 2017)

From error bounds to the complexity of first-order descent methods for convex functions. - Jérôme Bolte; Trong Phong Nguyen; Juan Peypouquet; Bruce W. Suter (Math. Program., 2017)

Nonconvex Lagrangian-Based Optimization: Monitoring Schemes and Global Convergence. - Jérôme Bolte; Shoham Sabach; Marc Teboulle (Math. Oper. Res., 2018)

Qualification Conditions in Semialgebraic Programming. - Jérôme Bolte; Antoine Hochart; Edouard Pauwels (SIAM J. Optim., 2018)

First Order Methods Beyond Convexity and Lipschitz Gradient Continuity with Applications to Quadratic Inverse Problems. - Jérôme Bolte; Shoham Sabach; Marc Teboulle; Yakov Vaisbourd (SIAM J. Optim., 2018)

Conservative set valued fields, automatic differentiation, stochastic gradient method and deep learning. - Jérôme Bolte; Edouard Pauwels (CoRR, 2019)

On Linear Convergence of Non-Euclidean Gradient Methods without Strong Convexity and Lipschitz Gradient Continuity. - Heinz H. Bauschke; Jérôme Bolte; Jiawei Chen; Marc Teboulle; Xianfu Wang (J. Optim. Theory Appl., 2019)

A mathematical model for automatic differentiation in machine learning. - Jérôme Bolte; Edouard Pauwels (NeurIPS, 2020)

Long term dynamics of the subgradient method for Lipschitz path differentiable functions. - Jérôme Bolte; Edouard Pauwels; Rodolfo Rios-Zertuche (CoRR, 2020)

A Hölderian backtracking method for min-max and min-min problems. - Jérôme Bolte; Lilian Glaudin; Edouard Pauwels; Mathieu Serrurier (CoRR, 2020)

The multiproximal linearization method for convex composite problems. - Jérôme Bolte; Zheng Chen 009; Edouard Pauwels (Math. Program., 2020)

Numerical influence of ReLU'(0) on backpropagation. - David Bertoin; Jérôme Bolte; Sébastien Gerchinovitz; Edouard Pauwels (NeurIPS, 2021)

Nonsmooth Implicit Differentiation for Machine-Learning and Optimization. - Jérôme Bolte; Tam Le; Edouard Pauwels; Antonio Silveti-Falls (NeurIPS, 2021)

An Inertial Newton Algorithm for Deep Learning. - Camille Castera; Jérôme Bolte; Cédric Févotte; Edouard Pauwels (J. Mach. Learn. Res., 2021)

Quartic First-Order Methods for Low-Rank Minimization. - Radu-Alexandru Dragomir; Alexandre d'Aspremont; Jérôme Bolte (J. Optim. Theory Appl., 2021)

Conservative set valued fields, automatic differentiation, stochastic gradient methods and deep learning. - Jérôme Bolte; Edouard Pauwels (Math. Program., 2021)

Automatic differentiation of nonsmooth iterative algorithms. - Jérôme Bolte; Edouard Pauwels; Samuel Vaiter (NeurIPS, 2022)

Nonsmooth automatic differentiation: a cheap gradient principle and other complexity results. - Jérôme Bolte; Ryan Boustany; Edouard Pauwels; Béatrice Pesquet-Popescu (CoRR, 2022)

Curiosities and counterexamples in smooth convex optimization. - Jérôme Bolte; Edouard Pauwels (Math. Program., 2022)

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

Second-Order Step-Size Tuning of SGD for Non-Convex Optimization. - Camille Castera; Jérôme Bolte; Cédric Févotte; Edouard Pauwels (Neural Process. Lett., 2022)

On the complexity of nonsmooth automatic differentiation. - Jérôme Bolte; Ryan Boustany; Edouard Pauwels; Béatrice Pesquet-Popescu (ICLR, 2023)

One-step differentiation of iterative algorithms. - Jérôme Bolte; Edouard Pauwels; Samuel Vaiter (NeurIPS, 2023)

The backtrack Hölder gradient method with application to min-max and min-min problems. - Jérôme Bolte; Lilian Glaudin; Edouard Pauwels; Mathieu Serrurier (Open J. Math. Optim., 2023)

Subgradient Sampling for Nonsmooth Nonconvex Minimization. - Jérôme Bolte; Tam Le; Edouard Pauwels (SIAM J. Optim., 2023)

Geometric and computational hardness of bilevel programming. - Jérôme Bolte; Quoc-Tung Le; Edouard Pauwels; Samuel Vaiter (CoRR, 2024)

A second-order-like optimizer with adaptive gradient scaling for deep learning. - Jérôme Bolte; Ryan Boustany; Edouard Pauwels; Andrei I. Purica (CoRR, 2024)

The Iterates of the Frank-Wolfe Algorithm May Not Converge. - Jérôme Bolte; Cyrille W. Combettes; Edouard Pauwels (Math. Oper. Res., 2024)

Differentiating Nonsmooth Solutions to Parametric Monotone Inclusion Problems. - Jérôme Bolte; Edouard Pauwels; Antonio Silveti-Falls (SIAM J. Optim., 2024)

Swarm gradient dynamics for global optimization: the mean-field limit case. - Jérôme Bolte; Laurent Miclo; Stéphane Villeneuve (Math. Program., 2024)

When majority rules, minority loses: bias amplification of gradient descent. - François Bachoc; Jérôme Bolte; Ryan Boustany; Jean-Michel Loubes (CoRR, 2025)

Improving in-context learning with a better scoring function. - Omar Naim; Swarnadeep Bhar; Jérôme Bolte; Nicholas Asher (CoRR, 2025)