Article

Bolte, Jérôme, Combettes, Cyrille and Pauwels, Edouard (2024) The iterates of the Frank–Wolfe algorithm may not converge. Mathematics of Operations Research, Vol. 49 (N° 4). pp. 2049-2802.

Bolte, JérômeORCID: https://orcid.org/0000-0002-1676-8407, Pauwels, EdouardORCID: https://orcid.org/0000-0002-8180-075X and Silveti Falls, AntonioORCID: https://orcid.org/0000-0002-8165-6348 (2024) Differentiating nonsmooth solutions to parametric monotone inclusion problems. SIAM Journal on Optimization, Vol. 34 (N° 1). pp. 71-97.

Bolte, Jérôme, Chen, Zheng and Pauwels, Edouard (2020) The multiproximal linearization method for convex composite problems. Mathematical Programming, vol. 182. pp. 1-36.

Bolte, Jérôme and Pauwels, Edouard (2019) Conservative set valued fields, automatic differentiation, stochastic gradient methods and deep learning. Mathematical Programming. pp. 1-33. (In Press)

Bolte, Jérôme and Pauwels, Edouard (2016) Majorization-minimization procedures and convergence of SQP methods for semi-algebraic and tame programs. Mathematics of Operations Research, vol. 41 (n° 2). pp. 442-465.

Book Section

Bolte, Jérôme and Pauwels, Edouard (2020) A mathematical model for automatic differentiation in machine learning. In: Advances in Neural Information Processing Systems 33 (NeurIPS 2020) Larochelle, Hugo, Ranzato, M., Hadsell, R., Balcan, M.F. and Lin, H. (eds.) MIT Press. ISBN 9781713829546 (In Press)

Monograph

Bolte, Jérôme, Le, Tam and Pauwels, Edouard (2022) Subgradient sampling for nonsmooth nonconvex minimization. TSE Working Paper, n. 22-1310, Toulouse

Bolte, Jérôme, Glaudin, Lilian, Pauwels, Edouard and Serrurier, Matthieu (2021) A Hölderian backtracking method for min-max and min-min problems. TSE Working Paper, n. 21-1243, Toulouse

Bolte, Jérôme, Pauwels, Edouard and Rios-Zertuche, Rodolfo (2020) Long term dynamics of the subgradient method for Lipschitz path differentiable functions. TSE Working Paper, n. 20-1110, Toulouse

Bolte, Jérôme and Pauwels, Edouard (2020) Curiosities and counterexamples in smooth convex optimization. TSE Working Paper, n. 20-1080, Toulouse

Bolte, Jérôme, Castera, Camille, Pauwels, Edouard and Févotte, Cédric (2019) An Inertial Newton Algorithm for Deep Learning. TSE Working Paper, n. 19-1043, Toulouse

Conference or Workshop Item

Bolte, Jérôme, Le, Tam, Pauwels, Edouard and Silveti Falls, AntonioORCID: https://orcid.org/0000-0002-8165-6348 (2022) Nonsmooth implicit differentiation for machine learning and optimization. In: NIPS'21: 35th International Conference on Neural Information Processing Systems, 6-14 décembre 2021, En ligne.