Alonso-Jordá, P.; Ibáñez González, JJ.; Sastre Martinez, J.; Peinado Pinilla, J.; Defez Candel, E. (2017). Efficient and accurate algorithms for computing matrix trigonometric functions. Journal of Computational and Applied Mathematics. 309(1):325-332. https://doi.org/10.1016/j.cam.2016.05.015
Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/82821
Title:
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Efficient and accurate algorithms for computing matrix trigonometric functions
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Author:
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Alonso-Jordá, Pedro
Ibáñez González, Jacinto Javier
Sastre Martinez, Jorge
Peinado Pinilla, Jesús
Defez Candel, Emilio
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UPV Unit:
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Universitat Politècnica de València. Escuela Técnica Superior de Ingenieros de Caminos, Canales y Puertos - Escola Tècnica Superior d'Enginyers de Camins, Canals i Ports
Universitat Politècnica de València. Escola Tècnica Superior d'Enginyeria Informàtica
Universitat Politècnica de València. Escuela Técnica Superior de Ingenieros de Telecomunicación - Escola Tècnica Superior d'Enginyers de Telecomunicació
Universitat Politècnica de València. Departamento de Informática de Sistemas y Computadores - Departament d'Informàtica de Sistemes i Computadors
Universitat Politècnica de València. Instituto Universitario de Matemática Multidisciplinar - Institut Universitari de Matemàtica Multidisciplinària
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Issued date:
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Abstract:
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[EN] Trigonometric matrix functions play a fundamental role in second order differential equations. This work presents an algorithm based on Taylor series for computing the matrix cosine. It uses a backward error analysis ...[+]
[EN] Trigonometric matrix functions play a fundamental role in second order differential equations. This work presents an algorithm based on Taylor series for computing the matrix cosine. It uses a backward error analysis with improved bounds. Numerical experiments show that MATLAB implementations of this algorithm has higher accuracy than other MATLAB implementations of the state of the art in the majority of tests. Furthermore, we have implemented the designed algorithm in language C for general purpose processors, and in CUDA for one and two NVIDIA GPUs. We obtained a very good performance from these implementations thanks to the high computational power of these hardware accelerators and our effort driven to avoid as much communications as possible. All the implemented programs are accessible through the MATLAB environment. (C) 2016 Elsevier B.V. All rights reserved.
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Subjects:
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Matrix cosine
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Matrix sine
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Scaling and squaring method
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Taylor series
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Backward error
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Parallel implementation
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Copyrigths:
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Reserva de todos los derechos
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Source:
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Journal of Computational and Applied Mathematics. (issn:
0377-0427
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DOI:
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10.1016/j.cam.2016.05.015
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Publisher:
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Elsevier
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Publisher version:
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http://dx.doi.org/10.1016/j.cam.2016.05.015
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Conference name:
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International Conference on Mathematical Modeling in Engineering and Human Behavior
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Conference place:
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Valencia, Spain
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Conference date:
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September 09-11, 2015
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Project ID:
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info:eu-repo/grantAgreement/MINECO//TIN2014-59294-P/ES/FUNCIONES DE MATRICES: CALCULO Y APLICACIONES/
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Thanks:
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This work has been supported by Spanish Ministerio de Economía y Competitividad and European Regional Development Fund (ERDF) grant TIN2014-59294-P
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Type:
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Artículo
Comunicación en congreso
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