Stochastic Estimation of the Frobenius Norm in the ACA Convergence Criterion

The adaptive cross approximation (ACA) algorithm has been used in many fast Integral Equation solvers for electromagnetic Radiation and Scattering problems. It efficiently computes a low rank approximation to the interaction matrix between mutually distant parts of a scattering object. The ACA is an...

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Bibliographic Details
Published in:IEEE transactions on antennas and propagation 2015-03, Vol.63 (3), p.1155-1158
Main Authors: Heldring, A., Ubeda, E., Rius, J. M.
Format: Article
Language:English
Subjects:
ADAPTIVE CROSS APPROXIMATION
Adaptive cross approximation (ACA)
ALGORITHM
Antenes i agrupacions d'antenes
Antennas
Approximation
Approximation algorithms
Approximation methods
computational electromagnetics
Convergence
Convergencia (Telecommunication)
Convergència (Telecomunicació)
Criteria
Enginyeria de la telecomunicació
Estimation
Kernel
Mathematical analysis
method of moments
Norms
Radiocomunicació i exploració electromagnètica
Scattering
Solvers
Stochasticity
Àrees temàtiques de la UPC
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Summary:The adaptive cross approximation (ACA) algorithm has been used in many fast Integral Equation solvers for electromagnetic Radiation and Scattering problems. It efficiently computes a low rank approximation to the interaction matrix between mutually distant parts of a scattering object. The ACA is an iterative algorithm that needs an accurate and efficient convergence criterion. The evaluation of this criterion may consume a considerable part of the computational resources. This communication presents an efficient new way to evaluate the convergence criterion, using a stochastic approach.
ISSN:0018-926X
1558-2221
DOI:10.1109/TAP.2014.2386306