148. Algorithms for nonconvex optimization on measure spaces.
Invited abstract in session WB-8: Theoretical advances in nonconvex optimization, stream Large scale optimization: methods and algorithms.
Wednesday, 10:30-12:30Room: B100/7007
Authors (first author is the speaker)
| 1. | Annette Dumas
|
| Université Paris Dauphine-PSL | |
| 2. | Clément Royer
|
| LAMSADE, Université Paris Dauphine-PSL |
Abstract
Super-resolution is a common task in medical imaging or microscopy, that consists in reconstructing a signal from a vector of measurements. This typically ill-posed problem can be formulated as the reconstruction of a sparse measure, yielding an optimization problem over a measure space.
In this talk, we focus on super-resolution problems where the measurements correspond to Fourier coefficients up to a given frequency. We compare several algorithms for solving this problem, based on conditional gradient and projected gradient techniques, from both a numerical and theoretical perspective.
Keywords
- Applications of continuous optimization
- Complexity and efficiency of algorithms
Status: accepted
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