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Quentin Denoyelle
Researcher at École Polytechnique Fédérale de Lausanne
Publications - 12
Citations - 244
Quentin Denoyelle is an academic researcher from École Polytechnique Fédérale de Lausanne. The author has contributed to research in topics: Frank–Wolfe algorithm & Convex optimization. The author has an hindex of 5, co-authored 11 publications receiving 183 citations. Previous affiliations of Quentin Denoyelle include French Institute for Research in Computer Science and Automation & Paris Dauphine University.
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Support Recovery for Sparse Super-Resolution of Positive Measures
TL;DR: In this paper, the authors studied the recovery properties of the support and amplitudes of the initial Radon measure in the presence of noise as a function of the minimum separation t of the input measure (the minimum distance between two spikes).
Journal ArticleDOI
The Sliding Frank-Wolfe Algorithm and its Application to Super-Resolution Microscopy
TL;DR: In this article, the Sliding Frank-Wolfe (SFC) algorithm is used to solve the SLASSO sparse spikes super-resolution problem, which is a continuous version of the 1-SASSO regularization method.
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Support Recovery for Sparse Deconvolution of Positive Measures.
TL;DR: There exists a unique solution to the BLASSO program with exactly the same number of spikes as the original measure when the noise and the regularization parameter drops to zero faster than $t^{2N-1}$.
Posted Content
The Sliding Frank-Wolfe Algorithm and its Application to Super-Resolution Microscopy
TL;DR: In this paper, the Sliding Frank-Wolfe (SFC) algorithm is used to solve the SLASSO sparse spikes super-resolution problem, which is a continuous version of the 1-SASSO regularization method.
Posted Content
Sparsest Continuous Piecewise-Linear Representation of Data
TL;DR: This work thoroughly describes the form of the solutions of the underlying constrained optimization problem, including the sparsest piecewise-linear solutions, i.e., with the minimum number of knots, and proposes a simple and fast two-step algorithm to reach a sparsmost solution of this constrained problem.