Name
Abstract | ||
|---|---|---|
Dual Coordinate Descent (DCD) is one of the most popular optimization methods. The parallelization of DCD is difficult, as DCD is sequential in nature. As such, simultaneously running multiple DCD threads on batches of data elements causes result inaccuracy and slow convergence, due to the concurrent updates of multiple coordinates. Some parallelization methods adopt separable approximate function... |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/TKDE.2020.3000905 | IEEE Transactions on Knowledge and Data Engineering |
Keywords | DocType | Volume |
Optimization,Approximation algorithms,Convergence,Parallel processing,Partitioning algorithms,Scalability,Message systems | Journal | 34 |
Issue | ISSN | Citations |
4 | 1041-4347 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hejun Wu | 1 | 242 | 23.03 |
| Xinchuan Huang | 2 | 0 | 0.34 |
| Qiong Luo | 3 | 3617 | 229.67 |
| Zhongheng Yang | 4 | 0 | 0.34 |