786 citations · 2.1k across the 40 of their papers we have counts for
7 papers · 1 filter
Rank Estimation for Third-Order Tensor Completion in the Tensor-Train Format
Charlotte Vermeylen, Guillaume Olikier, P. -A. Absil +1
We propose a numerical method to obtain an adequate value for the upper bound on the rank for the tensor completion problem on the variety of third-order tensors of bounded tensor-…
Graph-Based Matrix Completion Applied to Weather Data
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. Sometimes additional informa…
A Riemannian Proximal Newton Method
Wutao Si, P. -A. Absil, Wen Huang +2
In recent years, the proximal gradient method and its variants have been generalized to Riemannian manifolds for solving optimization problems with an additively separable structur…
Direct Exoplanet Detection Using L1 Norm Low-Rank Approximation
Hazan Daglayan, Simon Vary, Valentin Leplat +2
We propose to use low-rank matrix approximation using the component-wise L1-norm for direct imaging of exoplanets. Exoplanet detection by direct imaging is a challenging task for t…
First-order optimization on stratified sets
Guillaume Olikier, Kyle A. Gallivan, P. -A. Absil
We consider the problem of minimizing a differentiable function with locally Lipschitz continuous gradient on a stratified set and present a first-order algorithm designed to find…
Infeasible Deterministic, Stochastic, and Variance-Reduction Algorithms for Optimization under Orthogonality Constraints
Pierre Ablin, Simon Vary, Bin Gao +1
Orthogonality constraints naturally appear in many machine learning problems, from principal component analysis to robust neural network training. They are usually solved using Rie…