Name
Abstract | ||
|---|---|---|
We propose a novel stochastic-optimization framework based on the regularized dual averaging (RDA) method. The proposed approach differs from the previous studies of RDA in three major aspects. First, the squared-distance loss function to a “random” closed convex set is employed for stability. Second, a sparsity-promoting metric (used implicitly by a certain proportionate-type adaptive filtering a... |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TSP.2019.2908901 | IEEE Transactions on Signal Processing |
Keywords | Field | DocType |
Measurement,Signal processing algorithms,Optimization,Handheld computers,Convergence,Estimation,Geometry | Convergence (routing),Stochastic optimization,Mathematical optimization,Orthographic projection,Regression,Algorithm,Convex set,Synthetic data,Regularization (mathematics),Smoothness,Mathematics | Journal |
Volume | Issue | ISSN |
67 | 10 | 1053-587X |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Asahi Ushio | 1 | 0 | 2.03 |
| Masahiro Yukawa | 2 | 272 | 30.44 |