The class of H-matrices allows an approximate matrix arithmetic with almost linear complexity. In the present paper, we apply the H-matrix technique combined with the Kronecker tensor-product approximation (cf. [2, 20]) to represent the inverse of a discrete elliptic operator in a hypercube (0, 1)d ∈ R d in the case of a high spatial dimension d. In this data-sparse format, we also represent the operator exponential, the fractional power of an elliptic operator as well as the solution operator of the matrix Lyapunov-Sylvester equation. The complexity of our approximations can be estimated by O(dn logq n), where N = nd is the discrete problem size
AbstractMany of today’s most efficient numerical methods are based on multilevel decompositions: The...
A recurring theme in attempts to break the curse of dimensionality in the numerical approximations o...
This paper introduces the hierarchical interpolative factorization for integral equa-tions (HIF-IE) ...
A class of hierarchical matrices (H-matrices) allows the data-sparse approximation to integral and m...
Dedicated to Prof. I. Gavrilyuk on the occasion of his 60-th birthday. In the present paper we analy...
In this article we introduce new methods for the analysis of high dimensional data in tensor formats...
summary:We give a short introduction to a method for the data-sparse approximation of matrices resul...
Hierarchical matrices provide a data-sparse way to approximate fully populated matrices. In this pap...
Abstract. A recurring theme in attempts to break the curse of dimensionality in the numerical approx...
Abstract. A recurring theme in attempts to break the curse of dimensionality in the numerical approx...
A recurring theme in attempts to break the curse of dimensionality in the numerical approximation of...
AbstractIn a preceding paper (Hackbusch, Computing 62 (1999) 89–108), a class of matrices (H-matrice...
Zusammenfassung in deutscher SpracheIn this thesis, we analyze the following multilevel aspects in e...
The coming century is surely the century of high dimensional data. With the rapid growth of computat...
In previous papers hierarchical matrices were introduced which are data-sparse and allow an approxim...
AbstractMany of today’s most efficient numerical methods are based on multilevel decompositions: The...
A recurring theme in attempts to break the curse of dimensionality in the numerical approximations o...
This paper introduces the hierarchical interpolative factorization for integral equa-tions (HIF-IE) ...
A class of hierarchical matrices (H-matrices) allows the data-sparse approximation to integral and m...
Dedicated to Prof. I. Gavrilyuk on the occasion of his 60-th birthday. In the present paper we analy...
In this article we introduce new methods for the analysis of high dimensional data in tensor formats...
summary:We give a short introduction to a method for the data-sparse approximation of matrices resul...
Hierarchical matrices provide a data-sparse way to approximate fully populated matrices. In this pap...
Abstract. A recurring theme in attempts to break the curse of dimensionality in the numerical approx...
Abstract. A recurring theme in attempts to break the curse of dimensionality in the numerical approx...
A recurring theme in attempts to break the curse of dimensionality in the numerical approximation of...
AbstractIn a preceding paper (Hackbusch, Computing 62 (1999) 89–108), a class of matrices (H-matrice...
Zusammenfassung in deutscher SpracheIn this thesis, we analyze the following multilevel aspects in e...
The coming century is surely the century of high dimensional data. With the rapid growth of computat...
In previous papers hierarchical matrices were introduced which are data-sparse and allow an approxim...
AbstractMany of today’s most efficient numerical methods are based on multilevel decompositions: The...
A recurring theme in attempts to break the curse of dimensionality in the numerical approximations o...
This paper introduces the hierarchical interpolative factorization for integral equa-tions (HIF-IE) ...