Preprints

    [1] R. Gribonval, T. Mary & E. Riccietti, Optimal quantization of rank-one matrices in floating-point arithmetic-with applications to butterfly factorizations, preprint, 2023. [pdf]
    [2] G. Lauga, E. Riccietti, N. Pustelnik & P. Gonçalves, IML FISTA: A Multilevel Framework for Inexact and Inertial Forward-Backward. Application to Image Restoration, preprint, 2023. [pdf]
    [3] E. Riccietti, V. Mercier, S. Gratton & P. Boudier, Multilevel physics informed neural networks (MPINNS), openreview preprint, 2022. [pdf]
    [4] S. Gratton, V. Mercier, E. Riccietti & Ph.L. Toint, A Block-Coordinate Approach of Multi-level Optimization with an Application to Physics-Informed Neural Networks, arXiv preprint, 2023. [pdf]

Proceedings

    [1] Q.T. Le, E. Riccietti & R. Gribonval, Does a sparse ReLU network training problem always admit an optimum?, NeurIPS | 2023, 2023. [pdf]
    [2] R. Gribonval, T. Mary & E. Riccietti, Scaling is all you need: quantization of butterfly matrix products via optimal rank-one quantization, GRETSI 2023, 2023. [pdf]
    [3] L. Zheng, G. Puy, E. Riccietti, Perez. P. & R. Gribonval, Factorisation butterfly d’une matrice permuté par partitionnement spectral alterné, GRETSI 2023, 2023. [pdf]
    [4] G. Lauga, E. Riccietti, N. Pustelnik & P. Gonçalves, Méthodes multi-niveaux pour la restauration d'images hyperspectrales, GRETSI 2023, 2023. [pdf]
    [5] E.Riccietti. G. Lauga, Multilevel FISTA for Image Restoration, ICASSP, 2023. [pdf]
    [6] L. Zheng, G. Puy, E. Riccietti, P. Pérez & R. Gribonval, Self-supervised learning with rotation-invariant kernels, ICLR, 2023. [pdf]
    [7] E.Riccietti. G. Lauga, Méthodes proximales multi-niveaux pour la restauration d'images, Colloque GRETSI, 2022. [pdf]
    [8] QT. Le, L. Zheng, E. Riccietti & R. Gribonval, Fast Learning of Fast Transforms, with Guarantees in ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 3348-3352, 2022. [pdf] [doi]
    [9] E. Riccietti, S. Bellavia & S. Sello, Numerical Methods for Optimization Problems Arising in Energetic Districts in Progress in Industrial Mathematics at ECMI 2016, pages 35-42, Springer International Publishing, 2016. [pdf] [doi]

Journal Articles

    [1] A. Gonon, N. Brisebarre, R. Gribonval & E. Riccietti, Approximation speed of quantized vs. unquantized ReLU neural networks and beyond, IEEE Transactions on Information Theory, to appear, 2023. [pdf]
    [2] L. Zheng, E. Riccietti & R. Gribonval, Efficient Identification of Butterfly Sparse Matrix Factorizations, SIAM Journal on Mathematics of Data Science, 2023. [pdf] [doi]
    [3] QT. Le, E. Riccietti & R. Gribonval, Spurious Valleys, NP-hardness, and Tractability of Sparse Matrix Factorization With Fixed Support, SIAM Journal on Matrix Analysis and Applications, 2023 (to appear). [pdf]
    [4] A.L. Custodio, Y. Diouane, R. Garmanjani & E. Riccietti, Worst-case complexity bounds of directional direct-search methods for multiobjective optimization, Journal of Optimization Theory and Applications, 188(1):1-21, 2020. [pdf] [doi]
    [5] H. Calandra, S. Gratton, E. Riccietti & X. Vasseur, On iterative solution of the extended normal equations, SIAM Journal on Matrix Analysis and Applications, 41(4):1571-1589, 2020. [pdf] [doi]
    [6] H. Calandra, S. Gratton, E. Riccietti & X. Vasseur, On a multilevel Levenberg-Marquardt method for the training of artificial neural networks and its application to the solution of partial differential equations, Optimization Methods and Software, 2020. [pdf] [doi]
    [7] H. Calandra, S. Gratton, E. Riccietti & X. Vasseur, On high-order multilevel optimization strategies, SIAM Journal on Optimization, 31(1):307-330, 2020. [pdf] [doi]
    [8] S. Bellavia, M. Donatelli & E. Riccietti, An inexact non stationary Tikhonov procedure for large-scale nonlinear ill-posed problems, Inverse Problems, 36(9), 2020. [pdf] [doi]
    [9] S. Bellavia, S. Gratton & E. Riccietti, A Levenberg-Marquardt method for large nonlinear least-squares problems with noisy functions and gradients, Numerische Mathematik, 140(3):791-825, Springer, 2018. [pdf] [doi]
    [10] S. Bellavia & E. Riccietti, On an elliptical trust-region procedure for ill-posed nonlinear least squares problems, Journal of Optimization Theory and Applications, 158(3):824-859, 2018. [pdf] [doi]
    [11] E. Riccietti, S. Bellavia & S. Sello, Sequential Linear Programming and Particle Swarm Optimization for the optimization of energy districts, Engineering Optimization, pages 1-17, Taylor & Francis, 2018. [pdf] [doi]
    [12] E. Riccietti, J. Bellucci, M. Checcucci, M. Marconcini & A. Arnone, Support Vector Machine classification applied to the parametric design of centrifugal pumps, Engineering Optimization, pages 1-21, Taylor & Francis, 2017. [pdf] [doi]
    [13] S. Bellavia, B. Morini & E. Riccietti, On an adaptive regularization for ill-posed nonlinear systems and its trust-region implementation, Computational Optimization and Applications, 64(1):1-30, Springer, 2016. [pdf] [doi]

Thesis

    [1] E. Riccietti, Numerical methods for optimization problems: an application to energetic districts Master thesis, 2014. [pdf]
    [2] E. Riccietti, Levenberg-Marquardt methods for the solution of noisy nonlinear least squares problems PhD thesis, 2018. [pdf]