Jérôme Bolte

Jérôme Bolte, Edouard Pauwels, and Quoc Tung Le, “Geometric and computational hardness of bilevel programming”, Mathematical Programming, vol. 215, January 2026, p. 539–574.

Jérôme Bolte, Tam Le, Eric Moulines, and Edouard Pauwels, “Inexact subgradient methods for semialgebraic functions”, Mathematical Programming, 2026, forthcoming.

Jérôme Bolte, Cyrille Combettes, and Edouard Pauwels, “The Iterates of the Frank–Wolfe Algorithm May Not Converge”, Mathematics of Operations Research, vol. 49, n. 4, November 2024, revised June 10, 2026, pp. 2049–2802.

Jérôme Bolte, Laurent Miclo, and Stéphane Villeneuve, “Swarm gradient dynamics for global optimization: the mean-field limit case”, Mathematical Programming, vol. 205, May 2024, revised June 10, 2026, p. 661–701.

Jérôme Bolte, Edouard Pauwels, and Antonio Silveti-Falls, “Differentiating Nonsmooth Solutions to Parametric Monotone Inclusion Problems”, SIAM Journal on Optimization, vol. 34, n. 1, 2024, revised June 10, 2026, pp. 71–97.

Jérôme Bolte, Lilian Glaudin, Edouard Pauwels, and Matthieu Serrurier, “The backtrack Hölder gradient method with application to min-max and min-min problems”, Open Journal of Mathematical Optimization, vol. 4, n. 8, 2023, 17 pages.

Jérôme Bolte, Edouard Pauwels, and Samuel Vaiter, “Automatic differentiation of nonsmooth iterative algorithms”, in Advances in Neural Information Processing Systems 35, A. Oh, S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, and K. Cho (eds.), 2023, pp. 77089–77103.

Jérôme Bolte, Edouard Pauwels, and Samuel Vaiter, “One-step differentiation of iterative algorithms”, in Advances in Neural Information Processing Systems 36, A. Oh, T. Naumann, A. Globerson, K. Saenko, M. Hardt, and Sydney Levine (eds.), 2023, pp. 77089–77103.

Radu Dragomir, Adrien B. Taylor, Jérôme Bolte, and Alexandre D'Aspremont, “Optimal complexity and certification of Bregman first-order methods”, Mathematical Programming, vol. 194, July 2022, pp. 41–83.

Jérôme Bolte, David Bertoin, Sebastien Gerchinovitz, and Edouard Pauwels, “Numerical influence of ReLU’(0) on backpropagation”, in Advances in Neural Information Processing Systems 34, M. Ranzato, A. Beygelzimer, Y. Dauphin, P.S. Liang, and J. Wortman Vaughan (eds.), 2021, pp. 468–479.

Jérôme Bolte, Tam Le, Edouard Pauwels, and Antonio Silveti-Falls, “Nonsmooth Implicit Differentiation for Machine Learning and Optimization”, in Advances in Neural Information Processing Systems 34, M. Ranzato, A. Beygelzimer, Y. Dauphin, P.S. Liang, and J. Wortman Vaughan (eds.), 2021, revised June 10, 2026, pp. 13537–13549.

Jérôme Bolte, Zheng Chen, and Edouard Pauwels, “The multiproximal linearization method for convex composite problems”, Mathematical Programming, vol. 182, July 2020, pp. 1–36.

Jérôme Bolte, and Edouard Pauwels, “Conservative set valued fields, automatic differentiation, stochastic gradient methods and deep learning”, Mathematical Programming, April 2020, pp. 1–33.

Jérôme Bolte, and Edouard Pauwels, “A mathematical model for automatic differentiation in machine learning”, in Advances in Neural Information Processing Systems, Hugo Larochelle, M. Ranzato, R. Hadsell, M.F. Balcan, and H. Lin (eds.), vol. 33, 2020.

Jérôme Bolte, Shoham Sabach, and Marc Teboulle, “Nonconvex Lagrangian-based optimization: Monitoring schemes and global convergence”, Mathematics of Operations Research, vol. 43, n. 4, November 2018, pp. 1051–1404.

Adrien Blanchet, and Jérôme Bolte, “A family of functional inequalities: Lojasiewicz inequalities and displacement convex functions”, Journal of Functional Analysis, vol. 25, n. 7, October 2018, pp. 1650–1673.

Jérôme Bolte, Shoham Sabach, Marc Teboulle, and Y. Vaisbourd, “First order methods beyond convexity and Lipschitz gradient continuity with applications to quadratic inverse problems”, SIAM Journal on Optimization, vol. 28, n. 3, 2018, pp. 2131–2151.

Jérôme Bolte, Antoine Hochart, and Edouard Pauwels, “Qualification conditions in semi-algebraic programming”, SIAM Journal on Optimization, vol. 28, n. 2, 2018, pp. 1867–1891.

Jérôme Bolte, Trong-Phong Nguyen, Juan Peypouquet, and Bruce W. Suter, “From error bounds to the complexity of first-order descent methods for convex functions”, Mathematical Programming, vol. 165, n. 2, October 2017, pp. 471–507.

Catherine Bobtcheff, Jérôme Bolte, and Thomas Mariotti, “Researcher's Dilemma”, The Review of Economic Studies, vol. 84, n. 3, July 2017, pp. 969–1014.