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...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2015 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/27519 |
| Acceso en línea: | https://hdl.handle.net/2117/27519 https://dx.doi.org/10.1109/TAP.2014.2386306 |
| Access Level: | acceso abierto |
| Palabra clave: | Convergencia (Telecommunication) Adaptive cross approximation (ACA) computational electromagnetics method of moments ADAPTIVE CROSS APPROXIMATION ALGORITHM Convergència (Telecomunicació) Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Radiocomunicació i exploració electromagnètica::Antenes i agrupacions d'antenes |
| Sumario: | 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. |
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