Name
Abstract | ||
|---|---|---|
Newton's method is a widely applicable and empirically efficient method for finding the solution to a set of equations. The recently developed algebraic geometry analyses provide information-theoretic bounds on the sampling rate to ensure the existence of a unique completion. A remained open question from these works is to retrieve the sampled data when the sampling rate is very close to the menti... |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TSP.2019.2899315 | IEEE Transactions on Signal Processing |
Keywords | Field | DocType |
Matrix decomposition,Newton method,Sensors,Minimization methods,Manifolds,Signal processing algorithms | Convergence (routing),Applied mathematics,Rank factorization,Mathematical optimization,Matrix completion,Tensor,Polynomial,Matrix (mathematics),Matrix decomposition,Mathematics,Newton's method | Journal |
Volume | Issue | ISSN |
67 | 7 | 1053-587X |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ashraphijuo, M. | 1 | 21 | 6.82 |
| Xiaodong Wang | 2 | 3958 | 310.41 |
| Junwei Zhang | 3 | 46 | 9.28 |