## Abstract

Reconstructing compressed sensing signals involves solving an optimization problem. An example is Basis Pursuit (BP) [1], which is applicable only in noise-free scenarios. In noisy scenarios, either the Basis Pursuit Denoising (BPDN) [1] or the Noise-Aware BP (NABP) [2] can be used. Consider a distributed scenario where the dictionary matrix and the vector of observations are spread over the nodes of a network. We solve the following open problem: design distributed algorithms that solve BPDN with a column partition, i.e., when each node knows only some columns of the dictionary matrix, and that solve NABP with a row partition, i.e., when each node knows only some rows of the dictionary matrix and the corresponding observations. Our approach manipulates these problems so that a recent general-purpose algorithm for distributed optimization can be applied.

Original language | English |
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Title of host publication | 2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings |

Pages | 629 |

Number of pages | 1 |

ISBN (Electronic) | 9781479902484 |

DOIs | |

Publication status | Published - 2013 |

Event | 1st IEEE Global Conference on Signal and Information Processing 2013 - Austin, United States Duration: 3 Dec 2013 → 5 Dec 2013 |

### Conference

Conference | 1st IEEE Global Conference on Signal and Information Processing 2013 |
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Abbreviated title | GlobalSIP 2013 |

Country | United States |

City | Austin |

Period | 3/12/13 → 5/12/13 |

## Keywords

- Compressed sensing
- Distributed algorithms

## ASJC Scopus subject areas

- Information Systems
- Signal Processing