Search Results for author: P. -A. Absil

Found 12 papers, 5 papers with code

Graph-Based Matrix Completion Applied to Weather Data

no code implementations14 Jun 2023 Benoît Loucheur, P. -A. Absil, Michel Journée

Low-rank matrix completion is the task of recovering unknown entries of a matrix by assuming that the true matrix admits a good low-rank approximation.

Low-Rank Matrix Completion

Infeasible Deterministic, Stochastic, and Variance-Reduction Algorithms for Optimization under Orthogonality Constraints

no code implementations29 Mar 2023 Pierre Ablin, Simon Vary, Bin Gao, P. -A. Absil

Finally, our experiments demonstrate the promise of our approach to an array of machine-learning problems that involve orthogonality constraints.

Riemannian optimization

Symplectic eigenvalue problem via trace minimization and Riemannian optimization

2 code implementations7 Jan 2021 Nguyen Thanh Son, P. -A. Absil, Bin Gao, Tatjana Stykel

We address the problem of computing the smallest symplectic eigenvalues and the corresponding eigenvectors of symmetric positive-definite matrices in the sense of Williamson's theorem.

Optimization and Control Spectral Theory 15A15, 15A18, 70G45

A Grassmann Manifold Handbook: Basic Geometry and Computational Aspects

1 code implementation27 Nov 2020 Thomas Bendokat, Ralf Zimmermann, P. -A. Absil

The Grassmann manifold of linear subspaces is important for the mathematical modelling of a multitude of applications, ranging from problems in machine learning, computer vision and image processing to low-rank matrix optimization problems, dynamic low-rank decompositions and model reduction.

Numerical Analysis Numerical Analysis Differential Geometry 15-02, 15A16, 15A18, 15B10, 22E70, 51F25, 53C80, 53Z99

Alternating minimization algorithms for graph regularized tensor completion

1 code implementation28 Aug 2020 Yu Guan, Shuyu Dong, Bin Gao, P. -A. Absil, François Glineur

The usage of graph regularization entails benefits in the learning accuracy of LRTC, but at the same time, induces coupling graph Laplacian terms that hinder the optimization of the tensor completion model.

Assessment of COVID-19 hospitalization forecasts from a simplified SIR model

no code implementations20 Jul 2020 P. -A. Absil, Ousmane Diao, Mouhamadou Diallo

Moreover, we observed that, when the model is trained on a suitable three-week period around the first hospitalization peak for Belgium, it forecasts the subsequent two months with mean absolute percentage error (MAPE) under 4%.

Riemannian Optimization on the Symplectic Stiefel Manifold

2 code implementations26 Jun 2020 Bin Gao, Nguyen Thanh Son, P. -A. Absil, Tatjana Stykel

The symplectic Stiefel manifold, denoted by $\mathrm{Sp}(2p, 2n)$, is the set of linear symplectic maps between the standard symplectic spaces $\mathbb{R}^{2p}$ and $\mathbb{R}^{2n}$.

Optimization and Control Dynamical Systems

On a minimum enclosing ball of a collection of linear subspaces

no code implementations27 Mar 2020 Timothy Marrinan, P. -A. Absil, Nicolas Gillis

By scaling the objective and penalizing the information lost by the rank-$k$ minimax center, we jointly recover an optimal dimension, $k^*$, and a central subspace, $U^* \in$ Gr$(k^*, n)$ at the center of the minimum enclosing ball, that best represents the data.

VIP: Vortex Image Processing package for high-contrast direct imaging

1 code implementation17 May 2017 C. A. Gomez Gonzalez, O. Wertz, O. Absil, V. Christiaens, D. Defrere, D. Mawet, J. Milli, P. -A. Absil, M. Van Droogenbroeck, F. Cantalloube, P. M. Hinz, A. J. Skemer, M. Karlsson, J. Surdej

We present the Vortex Image Processing (VIP) library, a python package dedicated to astronomical high-contrast imaging.

Instrumentation and Methods for Astrophysics

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