MAJORIS is a scientific project funded by the European Research Council under a Starting Grant (2020-2024) and coordinated by Emilie Chouzenoux at Inria Saclay, France.
Mathematical optimization is the key to solving many problems in science, based on the observation that physical systems obey a general principle of least action. While some problems can be solved analytically, many more can only be solved via numerical algorithms. Research in this domain has proved essential over many years. In addition, science in general is changing. Increasingly, in biology, medicine, astronomy, chemistry, physics, large amounts of data are collected by constantly improving signal and image acquisition devices, that must be analyzed by sophisticated optimization tools. In this project, we consider handling optimization problems with large datasets. This means minimizing a cost function with a complex structure and many variables. The computational load for solving these problems is too heavy for even state-of-the-art algorithms. Thus, only relatively rudimentary data processing techniques are often employed, reducing the quality of the results and limiting the outcomes that can be achieved via these novel instruments. New algorithms must be designed with computational scalability, robustness, and versatility in mind.
In this context, Majorization-Minimization (MM) approaches have a crucial role to play. They consist of a class of efficient and effective optimization algorithms that benefit from solid theoretical foundations. The MAJORIS project aims at proposing a breakthrough in MM algorithms, so that they remain efficient when dealing with big data. Several challenging questions concerning algorithm design will be tackled. These include acceleration strategies, convergence analysis with complex costs and inexact schemes. Practical, massively parallel and distributed architecture implementations will be proposed.
In-vivo multiphonic microscopy (Collaboration XLIM, CNRS)
Mass spectrometry (Collaboration IFPEN)
Brain MRI (Collaboration Institut Gustave Roussy)
International journals
E. Chouzenoux and V. Elvira. Sparse Graphical Linear Dynamical Systems, To appear in Journal of Machine Learning Research, 2024. [Article] [Code]
F. Roldan, E. Chouzenoux, and J.-C. Pesquet. Solution of Mismatched Monotone+Lipschitz Inclusion Problems. To appear in SIAM Journal on Optimization, 2024. [Article]
T. Guilmeau, E. Chouzenoux, and V. Elvira. On variational inference and maximum likelihood estimation with the λ-exponential family. Foundations of Data Science, vol. 6, no. 1, pp. 85-123, 2024. [Article]
M. Gharbi, E. Chouzenoux, and J.-C. Pesquet. An Unrolled Half-Quadratic Approach for Sparse Signal Recovery in Spectroscopy. Signal Processing, vol. 218, pp. 109369, May 2024. [Article]
J.-B. Fest and E. Chouzenoux. Convergence Analysis of Block Majorize-Minimize Subspace Approaches. Optimization Letters, vol. 18, pp. 1111–1130, 2024. [Article]
E. Chouzenoux, A. Contreras, J.-C. Pesquet, M. Savanier. Convergence Results for Primal-Dual Algorithms in the Presence of Adjoint Mismatch. SIAM Journal on Imaging Sciences, vol. 16, no. 1, pp. 1-34, 2023. [Article]
Y. Huang, E. Chouzenoux, V. Elvira and J.-C. Pesquet. Efficient Bayes Inference in Neural Networks through Adaptive Importance Sampling. Journal of Franklin Institute, vol. 360, no. 16, pp. 12125-12149, Nov. 2023. [Article][Code]
M. Savanier, E. Chouzenoux, J.-C. Pesquet, C. Riddell. Deep Unfolding of the DBFB Algorithm with Application to ROI CT Imaging with Limited Angular Density. IEEE Transactions on Computational Imaging, vol. 9, pp. 502-516, 2023. [Article]
M. Chalvidal, E. Chouzenoux, J.B. Fest and C. Lefort. Block Delayed Majorize-Minimize Subspace Algorithm for Large Scale Image Restoration. Inverse Problems, vol. 39, no. 4, pp. 044002, Special Issue on Optimisation and Learning Methods for Inverse Problems in Microscopy, 2023. [Article]
P. Zheng, E. Chouzenoux and L. Duval. PENDANTSS: PEnalized Norm-ratios Disentangling Additive Noise, Trend and Sparse Spikes. IEEE Signal Processing Letters, vol. 30, pp. 215-219, 2023. [Article] [Code]
E. Chouzenoux, S. Martin and J.-C. Pesquet. A Local MM Subspace Method for Solving Constrained Variational Problems in Image Recovery. Journal of Mathematical Imaging and Vision, vol. 65, pp. 253-276, 2023. [Article]
Y. Huang, E. Chouzenoux, and J.-C. Pesquet. Unrolled Variational Bayesian Algorithm for Image Blind Deconvolution. IEEE Transactions on Image Processing, vol. 32, pp. 430-445, Dec. 2022. [Article] [Code]
V. Elvira and E. Chouzenoux. Graphical Inference in Linear-Gaussian State-Space Models. IEEE Transactions on Signal Processing, vol. 70, pp. 4757-4771, Sep. 2022. [Article] [Code]
E. Chouzenoux and J.-B. Fest. SABRINA: A Stochastic Subspace Majorization-Minimization Algorithm. Journal of Optimization Theory and Applications, vol. 195, pp. 919-952, 2022. [Article]
V. Elvira and E. Chouzenoux. Optimized Population Monte Carlo. IEEE Transactions on Signal Processing, vol. 70, pp. 2489-2501, 2022. [Article]
C. Lefort, M. Chalvidal, A. Parenté, V. Blanquet, H. Massias, L. Magnol, and E. Chouzenoux. FAMOUS: a fast instrumental and computational pipeline for multiphoton microscopy applied to 3D imaging of muscle ultrastructure. Journal of Physics D: Applied Physics, vol. 54, no. 27, pp. 274005, 2021. [Article]
E. Chouzenoux, J.-C. Pesquet, C. Riddell, M. Savanier and Y. Trousset. Convergence of Proximal Gradient Algorithm in the Presence of Adjoint Mismatch. Inverse Problems, vol. 33, no. 6, pp. 065009, 2020. [Article]
Conference proceedings
E. Chouzenoux and V. Elvira. Graphical inference in non-Markovian linear-Gaussian state-space models, In Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2024), Seoul, South Corea, April 2024 (invited paper). [Article]
T. Guilmeau, N. Branchini, E. Chouzenoux, V. Elvira. Adaptive importance sampling for heavy-tailed distributions via α-divergence minimization, In Proceedings of 27th International Conference on Artificial Intelligence and Statistics (AISTATS 2024), Valencia, Spain, 2nd-4th May 2024. [Article]
P. Zheng, E. Chouzenoux, L. Duval. Démélange, déconvolution et débruitage conjoints d’un modèle convolutif parcimonieux avec dérive instrumentale, par pénalisation de rapports de normes ou quasi-normes lissées (PENDANTSS), In Proceedings of 29th Colloque Francophone de Traitement du Signal et des Images (GRETSI 2023), Grenoble, France, 28 Aug.-1 Sep. 2023. [Article]
J.-B. Fest, A. Repetti and E. Chouzenoux. A new non-convex framework to improve asymptotical knowledge on generic stochastic gradient descent, In Proceedings of IEEE International Workshop on Machine Learning and Image Processing (MLSP 2023), Rome, Italy 17-20 September 2023.
C. de Valle, E. Centofanti, E. Chouzenoux, J.-C. Pesquet. Stability of Unfolded Forward-Backward to Perturbations in Observed Data, In Proceedings of the 31st European Signal Processing Conference (EUSIPCO 2023), Helsinki, Finland, September 2023 (invited paper). [Article]
V. Elvira, E. Chouzenoux, J. Cerda, G. Camps-Valls Graphs in State-Space Models for Granger Causality in Climate Science, When Causal Inference Meets Statistical Analysis, Paris, France, April 2023 (invited paper). [Article]
C. Cosserat, B. Gabrielson, E. Chouzenoux, J.-C. Pesquet, T. Adali. A proximal approach to IVA-G with convergence guarantees, In Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2023), Rhodes, Greece, 4-10th June 2023. [Article]
T. Guilmeau, E. Chouzenoux, V. Elvira. Adaptive simulated annealing through alternating Renyi divergence minimization, In Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2023), Rhodes, Greece, 4-10th June 2023. [Article]
C. Rossignol, F. Sureau, E. Chouzenoux, C. Comtat, J.-C. Pesquet. A Bregman Majorization-Minimization Framework for PET Image Reconstruction. In Proceedings of the 29th IEEE International Conference on Image Processing (ICIP 2022), Bordeaux, France, 16-19 October 2022. [pdf]
T. Guilmeau, E. Chouzenoux and V. Elvira. Proximal-based adaptive simulated annealing for global optimization, In Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2022), May 2022, Singapore, China. [pdf]
M. Gharbi, E. Chouzenoux, J.-C. Pesquet and L. Duval. GPU-based Implementations of MM Algorithms. Application to Spectroscopy Signal Restoration. In Proceedings of the 29th European Signal Processing Conference (EUSIPCO 2021), Dublin, Ireland (virtual), August 23-27 2021. [pdf]
J.-B. Fest and E. Chouzenoux. Stochastic Majorize-Minimize Subspace Algorithm with Application to Binary Classification. In Proceedings of the 29th European Signal Processing Conference (EUSIPCO 2021), Dublin, Ireland (virtual), August 23-27 2021. [pdf]
S. Martin, E. Chouzenoux and J.-C. Pesquet. A Penalized Subspace Strategy for Solving Large-Scale Constrained Optimization Problems. In Proceedings of the 29th European Signal Processing Conference (EUSIPCO 2021), Dublin, Ireland (virtual), August 23-27 2021. [pdf]
T. Guilmeau, E. Chouzenoux and V. Elvira. Simulated annealing: a review and a new scheme, In Proceedings of IEEE Statistical Signal Processing Workshop (SSP 2021), Rio de Janeiro, Brazil (virtual), 11-14th July 2021. [pdf]
M. Savanier, E. Chouzenoux, J.C. Pesquet, C. Riddell and Y. Trousset. Proximal Gradient Algorithm in the Presence of Adjoint Mismatch. In Proceedings of the 28th European Signal Processing Conference (EUSIPCO 2020), January 18-22 2021. [pdf]
M. Chalvidal and E. Chouzenoux. Block Distributed 3MG Algorithm and its Application to 3D Image Restoration. In Proceedings of the 27th IEEE International Conference on Image Processing (ICIP 2020), Virtual Conference, October 25-28 2020. [pdf] [code]
E. Chouzenoux and V. Elvira. GraphEM: EM Algorithm for Blind Kalman Filtering under Graphical Sparsity Constraints>. In Proceedings of the 45th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2020), Virtual Conference, May 4-8 2020. [pdf]
Technical reports
T. Guilmeau, E. Chouzenoux, and V. Elvira. A divergence-based condition to ensure quantile improvement in black-box global optimization, 2024. [Article]
M. Vu, E. Chouzenoux, J.-C. Pesquet, I. Ben-Ayed. Aggregated f-average Neural Network for Interpretable Ensembling, 2023. [Article]
E. Chouzenoux, J.-B. Fest and A. Repetti. A Kurdyka-Lojasiewicz property for stochastic optimization algorithms in a non-convex setting, 2023. [Article]
T. Guilmeau, E. Chouzenoux and V. Elvira. Regularized Rényi divergence minimization through Bregman proximal gradient algorithms, 2022. [pdf]
E. Chouzenoux, C. Della Valle and J.-C. Pesquet. Inversion of Integral Models: a Neural Network Approach. Submitted, 2021. [Article]
Post-doctoral fellow:
Andrès Contreras
PhD students:
Jean-Baptiste Fest
Mathieu Vu
Thomas Guilmeau
Clément Cosserat
Research engineers:
Claire Rossignol
Prof. Tulay Adali
University of Baltimore, USA – Oct 2021 to May 2022
Dr. Audrey Repetti
Heriot-Watt University, Edinburgh, UK – April 2022 to July 2022
Dr. Giorgia Franchini
UNIMORE, Modena, Italy – September 2023
Prof. Hamid Krim
North Carolina State University, Raleigh, US – May-June 2023
MAJORIS is equipped with a NVIDIA DGX-1 server for high performance deep learning training.
Interview of the project PI available here.