J 2012

Approximate multiplication in adaptive wavelet methods

FINĚK, Václav and Dana ČERNÁ

Basic information

Original name

Approximate multiplication in adaptive wavelet methods

Authors

FINĚK, Václav (203 Czech Republic, belonging to the institution) and Dana ČERNÁ (203 Czech Republic, belonging to the institution)

Edition

CENTRAL EUROPEAN JOURNAL OF MATHEMATICS, 2012, 1895-1074

Other information

Language

English

Type of outcome

Article in a journal

Field of Study

General mathematics

Country of publisher

Poland

Confidentiality degree

is not subject to a state or trade secret

References:

RIV identification code

RIV/46747885:24510/12:#0001005

Organization

Faculty of Science, Humanities and Education – Technical University of Liberec – Repository

UT WoS

316284500014

Keywords in English

Matrix-vector multiplication

Links

GP201/09/P641, research and development project.
Changed: 10/3/2015 13:50, RNDr. Daniel Jakubík

Abstract

V originále

Cohen, Dahmen and DeVore designed in [Adaptive wavelet methods for elliptic operator equations: convergence rates, Math. Comp., 2001, 70(233), 27–75] and [Adaptive wavelet methods IIbeyond the elliptic case, Found. Comput. Math., 2002, 2(3), 203–245] a general concept for solving operator equations. Its essential steps are: transformation of the variational formulation into the well-conditioned infinite-dimensional l 2-problem, finding the convergent iteration process for the l 2-problem and finally using its finite dimensional approximation which works with an inexact right-hand side and approximate matrix-vector multiplication. In our contribution, we pay attention to approximate matrix-vector multiplication which is enabled by an off-diagonal decay of entries of the wavelet stiffness matrices. We propose a more efficient technique which better utilizes actual decay of matrix and vector entries and we also prove that this multiplication algorithm is asymptotically optimal in the sense that storage and number of floating point operations, needed to resolve the problem with desired accuracy, remain proportional to the problem size when the resolution of the discretization is refined.