Name
Title | ||
|---|---|---|
Distributed weighted least squares estimation with fast convergence in large-scale systems |
Abstract | ||
|---|---|---|
We propose a distributed method for weighted least squares estimation. Our method is suitable for large-scale systems, in which each node only estimates a subset of the unknown parameters. As opposed to other works, our goal is to maximize the convergence speed of the distributed algorithm. To this end, we propose a distributed method for estimating the optimal value of certain scaling parameter on which this speed depends. To further speed the convergence, we use a simple preconditioning method, and we bound the difference between the resulting speed, and the fastest theoretically achievable using preconditioning. We include numerical experiments to illustrate the performance of the proposed method. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/CDC.2013.6760744 | Decision and Control |
Keywords | Field | DocType |
convergence,distributed algorithms,large-scale systems,least squares approximations,parameter estimation,convergence speed maximization,distributed algorithm,distributed weighted least squares estimation,fast convergence,large-scale systems,parameter estimation,preconditioning method,scaling parameter | Convergence (routing),Mathematical optimization,Weighted least squares estimation,Computer science,Distributed algorithm,Estimation theory,Non-linear least squares,Scaling | Conference |
ISSN | ISBN | Citations |
0743-1546 | 978-1-4673-5714-2 | 3 |
PageRank | References | Authors |
0.46 | 12 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Damián Marelli | 1 | 164 | 19.58 |
| Minyue Fu | 2 | 1878 | 221.17 |