Abstract
Primal-dual proximal optimization methods have recently gained much interest for dealing with very large-scale data sets encoutered in many application fields such as machine learning, computer vision and inverse problems [1-3]. In this work, we propose a novel random block-coordinate version of such algorithms allowing us to solve a wide array of convex variational problems. One of the main advantages of the proposed algorithm is its ability to solve composite problems involving large-size matrices without requiring any inversion. In addition, the almost sure convergence to an optimal solution to the problem is guaranteed. We illustrate the good performance of our method on a mesh denoising application.
| Original language | English |
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| Title of host publication | 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) |
| Publisher | IEEE |
| Pages | 3561-3565 |
| Number of pages | 5 |
| ISBN (Electronic) | 9781467369978 |
| DOIs | |
| Publication status | Published - 6 Aug 2015 |
| Event | 40th IEEE International Conference on Acoustics, Speech and Signal Processing 2015 - Brisbane, Australia Duration: 19 Apr 2015 → 24 Apr 2015 |
Conference
| Conference | 40th IEEE International Conference on Acoustics, Speech and Signal Processing 2015 |
|---|---|
| Abbreviated title | ICASSP 2015 |
| Country | Australia |
| City | Brisbane |
| Period | 19/04/15 → 24/04/15 |
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Repetti, A., Chouzenoux, E., & Pesquet, J-C. (2015). A random block-coordinate primal-dual proximal algorithm with application to 3D mesh denoising. In 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 3561-3565). IEEE. https://doi.org/10.1109/ICASSP.2015.7178634