Name
Abstract | ||
|---|---|---|
In this paper, a stochastic quasi-Newton algorithm for nonconvex stochastic optimization is presented. It is derived from a classical modified BFGS formula. The update formula can be extended to the framework of limited memory scheme. Numerical experiments on some problems in machine learning are given. The results show that the proposed algorithm has great prospects. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.3390/sym14020378 | SYMMETRY-BASEL |
Keywords | DocType | Volume |
nonconvex stochastic optimization, stochastic approximation, quasi-Newton method, damped limited-memory BFGS method, variance reduction | Journal | 14 |
Issue | ISSN | Citations |
2 | 2073-8994 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hanger Liu | 1 | 0 | 0.34 |
| Yan Li | 2 | 25 | 23.95 |
| Maojun Zhang | 3 | 314 | 48.74 |