Toolbox Sparse Optmization
This toolbox contains the implementation of what I consider to be fundamental algorithms
for non-smooth convex optimization of structured functions. These algorithms might not be the fasted
(although they certainly are quite efficient), but they all have a simple implementation in term
of black boxes (gradient and proximal mappings, given as callbacks). However, you should have
some knowledge about what is a gradient operator and a proximal mapping in order to be able
to use this toolbox on your own problems. I suggest you have a look at the
"suggested readings" for some more information about all this.
Citation pour cette source
Gabriel Peyre (2026). Toolbox Sparse Optmization (https://fr.mathworks.com/matlabcentral/fileexchange/16204-toolbox-sparse-optmization), MATLAB Central File Exchange. Extrait(e) le .
Compatibilité avec les versions de MATLAB
Plateformes compatibles
Windows macOS LinuxCatégories
- Signal Processing > Signal Processing Toolbox > Transforms, Correlation, and Modeling > Correlation and Convolution >
Tags
Remerciements
A inspiré : CoSaMP and OMP for sparse recovery
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| Version | Publié le | Notes de version | |
|---|---|---|---|
| 1.5.0.0 | Totally changed the toolbox to contain only optimization codes. |
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| 1.3.0.0 | Modified license.
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| 1.2.0.0 | Update of Licence |
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| 1.1.0.0 | BSD Licence |
