Name
Abstract | ||
|---|---|---|
This paper presents a mathematically rigorous analysis of linearly constrained adaptive filtering algorithms based on the adaptive projected subgradient method. We provide the novel concept of constraint-embedding functions that enables to analyze certain classes of linearly constrained adaptive algorithms in a unified manner. Trajectories of the linearly constrained adaptive filters always lie in the affine constraint set, a translation of a closed subspace. Based on this fact, we translate all the points on the constraint set to its underlying subspace - which we regard as a Hilbert space - thereby making the analysis feasible. Derivations of the linearly constrained adaptive filtering algorithms are finally presented in connection with the analysis. |
Year | Venue | Keywords |
|---|---|---|
2011 | Barcelona | hilbert spaces,adaptive filters,constraint theory,deterministic algorithms,gradient methods,hilbert space,adaptive projected subgradient method,affine constraint set,constraint-embedding function,deterministic analysis,linearly constrained adaptive filtering algorithm,signal processing,convex functions,vectors,algorithm design and analysis |
Field | DocType | ISSN |
Hilbert space,Affine transformation,Signal processing,Mathematical optimization,Adaptive filtering algorithm,Algorithm design,Subspace topology,Convex function,Adaptive filter,Mathematics | Conference | 2076-1465 |
Citations | PageRank | References |
3 | 0.47 | 4 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Masahiro Yukawa | 1 | 272 | 30.44 |
| isao yamada | 2 | 953 | 74.52 |