AbstractSpectral theory for bounded linear operators is used to develop a general class of approximation methods for the Moore-Penrose generalized inverse of a closed, densely defined linear operator. Issues of convergence and stability are addressed and the methods are modified to provide a stable class of methods for evaluation of unbounded linear operators
The goal of the sparse approximation problem is to approximate a target signal using a linear combin...
The goal of the sparse approximation problem is to approximate a target signal using a linear combin...
In sparse approximation problems, the goal is to find an approximate representation of a target sig...
AbstractSpectral theory for bounded linear operators is used to develop a general class of approxima...
Spectral theory of bounded linear operators teams up with von Neumann’s theory of unbounded operator...
AbstractA general approach is developed for stable evaluation of unbounded operators when the data a...
. In this paper we present a method for solving problems Af = g by constructing an approximative inv...
During the past the convergence analysis for linear statistical inverse problems has mainly focused ...
Inverse problems occur frequently in science and technology, whenever we need to infer causes from e...
AbstractThe spectral theory for unbounded normal operators is used to develop a systematic method of...
Inverse problems occur frequently in science and technology, whenever we need to infer causes from e...
AbstractLet X,Y be normed linear spaces, T∈L(X,Y) be a bounded linear operator from X to Y. One want...
AbstractRichardson's “extrapolation to the limit” idea is applied to the method of regularization fo...
AbstractLet X,Y be normed linear spaces, T∈L(X,Y) be a bounded linear operator from X to Y. One want...
AbstractIn 1956, R. Penrose studied best-approximate solutions of the matrix equation AX = B. He pro...
The goal of the sparse approximation problem is to approximate a target signal using a linear combin...
The goal of the sparse approximation problem is to approximate a target signal using a linear combin...
In sparse approximation problems, the goal is to find an approximate representation of a target sig...
AbstractSpectral theory for bounded linear operators is used to develop a general class of approxima...
Spectral theory of bounded linear operators teams up with von Neumann’s theory of unbounded operator...
AbstractA general approach is developed for stable evaluation of unbounded operators when the data a...
. In this paper we present a method for solving problems Af = g by constructing an approximative inv...
During the past the convergence analysis for linear statistical inverse problems has mainly focused ...
Inverse problems occur frequently in science and technology, whenever we need to infer causes from e...
AbstractThe spectral theory for unbounded normal operators is used to develop a systematic method of...
Inverse problems occur frequently in science and technology, whenever we need to infer causes from e...
AbstractLet X,Y be normed linear spaces, T∈L(X,Y) be a bounded linear operator from X to Y. One want...
AbstractRichardson's “extrapolation to the limit” idea is applied to the method of regularization fo...
AbstractLet X,Y be normed linear spaces, T∈L(X,Y) be a bounded linear operator from X to Y. One want...
AbstractIn 1956, R. Penrose studied best-approximate solutions of the matrix equation AX = B. He pro...
The goal of the sparse approximation problem is to approximate a target signal using a linear combin...
The goal of the sparse approximation problem is to approximate a target signal using a linear combin...
In sparse approximation problems, the goal is to find an approximate representation of a target sig...