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 |
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Abbreviated title | ICASSP 2015 |
Country/Territory | Australia |
City | Brisbane |
Period | 19/04/15 → 24/04/15 |