Name
Abstract | ||
|---|---|---|
In this paper, we analyze the properties of a fixed point of a certain mapping that is implicitly used in each of the regularized dual averaging (RDA) and projection-based RDA (PDA) algorithms. It turns out that, if the loss function has a nonexpansive (1-Lipschltz) gradient such as in the case of a half squared-distance function, RDA converges to a minimizer of the penalized loss function under a restrictive condition. Meanwhile, the fixed point for PDA gives a minimizer of the ‘unpenalized’ loss function. Some simulation studies are also presented to support the theoretical findings. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.23919/APSIPA.2018.8659576 | 2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) |
Keywords | Field | DocType |
Convergence,Handheld computers,Convex functions,Optimization,Measurement,Electrical engineering,Adaptation models | Convergence (routing),Applied mathematics,Convex function,Fixed point,Mathematics | Conference |
ISSN | ISBN | Citations |
2309-9402 | 978-9-8814-7685-2 | 0 |
PageRank | References | Authors |
0.34 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Masahiro Yukawa | 1 | 272 | 30.44 |
| I. Yamada | 2 | 16 | 11.65 |