Name
Abstract | ||
|---|---|---|
We present an adaptive version of the greedy least squares method for finding a sparse approximate solution, with fixed support size, to an overdetermined linear system. The information updated at each time moment consists of a partial orthogonal triangularization of the system matrix and of partial scalar products of its columns, among them and with the right hand side. Since allowing arbitrary changes of the solution support at each update leads to high computation costs, we have adopted a neighbor permutation strategy that changes at most a position of the support with a new one. Hence, the number of operations is lower than that of the standard RLS. Numerical comparisons with standard RLS in an adaptive FIR identification problem show that the proposed greedy RLS has faster convergence and smaller stationary error. |
Year | Venue | Keywords |
|---|---|---|
2010 | Aalborg | fir filters,adaptive filters,filtering theory,least squares approximations,recursive filters,adaptive fir identification problem,fixed support size,greedy rls,greedy least squares method,linear system,partial orthogonal triangularization,partial scalar product,recursive least-squares method,sparse approximate solution,sparse filters |
Field | DocType | ISSN |
Convergence (routing),Least squares,Mathematical optimization,Overdetermined system,Linear system,Permutation,Non-linear least squares,Recursive least squares filter,Parameter identification problem,Mathematics | Conference | 2219-5491 |
Citations | PageRank | References |
2 | 0.43 | 7 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bogdan Dumitrescu | 1 | 107 | 22.76 |
| Ioan Tabus | 2 | 276 | 38.23 |