Name
Abstract | ||
|---|---|---|
This paper develops and analyzes an online distributed proximal-gradient method (DPGM) for time-varying composite convex optimization problems. Each node of the network features a local cost that includes a smooth strongly convex function and a non-smooth convex function, both changing over time. By coordinating through a connected communication network, the nodes collaboratively track the trajectory of the minimizers without exchanging their local cost functions. The DPGM is implemented in an online fashion, that is, in a setting where only a limited number of steps are implemented before the function changes. Moreover, the algorithm is analyzed in an inexact scenario, that is, with a source of additive noise, that can represent e.g. communication noise or quantization. It is shown that the tracking error of the online inexact DPGM is upper-bounded by a convergent linear system, guaranteeing convergence within a neighborhood of the optimal solution. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.23919/ECC54610.2021.9654953 | 2021 EUROPEAN CONTROL CONFERENCE (ECC) |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nicola Bastianello | 1 | 1 | 3.40 |
| Ajalloeian Amirhossein | 2 | 0 | 0.34 |
| Dall'Anese Emiliano | 3 | 0 | 0.34 |