TACA< T_value > Class Template Reference
Defines interface for all ACA algorithms and implements classical ACA. More...
#include <TLowRankApx.hh>
Inheritance diagram for TACA< T_value >:
Public Member Functions | |
| virtual std::unique_ptr< TMatrix< value_t > > | build (const TBlockCluster *cl, const TTruncAcc &acc) const |
| virtual std::unique_ptr< TMatrix< value_t > > | build (const TBlockIndexSet &block_is, const TTruncAcc &acc) const |
| virtual bool | has_statistics () const |
| indicate if algorithm provides statistics | |
Detailed Description
template<typename T_value>
class Hpro::TACA< T_value >
Adaptive cross approximation (ACA) is a heuristic for computing a
low rank approximation of a given dense matrix by successively
removing specific pairs of rows and columns (crosses) from the
matrix until the rest is below some threshold (defined by block-wise
accuracy).
Due to the algorithm, only the matrix coefficients in form of a
TCoeffFn are needed, permitting the straightforward adaption of existing
implementations for the construction of H-matrices.
The costs are linear in the dimension of the block and quadratic in the rank.
Member Function Documentation
◆ build() [1/2]
|
virtual |
build low rank matrix for block cluster bct with rank defined by accuracy acc
Reimplemented from TLowRankApx< T_value >.
◆ build() [2/2]
|
virtual |
build low rank matrix for block index set block_is with rank defined by accuracy acc
Implements TLowRankApx< T_value >.
