2 citations · 6 across the 9 of their papers we have counts for
4 papers · 1 filter
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…
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…
Low-rank plus sparse trajectory decomposition for direct exoplanet imaging
Simon Vary, Hazan Daglayan, Laurent Jacques +1
We propose a direct imaging method for the detection of exoplanets based on a combined low-rank plus structured sparse model. For this task, we develop a dictionary of possible eff…