Solving large systems arising from fractional model by preconditioned methods

Solving large systems arising from fractional model by preconditioned methods

This study develops and analyzes preconditioned Krylov subspace methods for solving discretization of the time-independent space-fractional models. First we apply a shifted Grunwald formulas to obtain a stable finite difference approximation to fractional advection-diffusion equations. Then, we app...

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Main Authors: Reza Khoshsiar Ghaziani, Mojtaba Fardi, Mehdi Ghasemi
Format: บทความวารสาร
Language:English
Published: Science Faculty of Chiang Mai University 2019
Online Access:http://it.science.cmu.ac.th/ejournal/dl.php?journal_id=8503
http://cmuir.cmu.ac.th/jspui/handle/6653943832/64001
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Institution: Chiang Mai University
Language: English
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spelling th-cmuir.6653943832-640012019-05-07T09:59:42Z Solving large systems arising from fractional model by preconditioned methods Reza Khoshsiar Ghaziani Mojtaba Fardi Mehdi Ghasemi This study develops and analyzes preconditioned Krylov subspace methods for solving discretization of the time-independent space-fractional models. First we apply a shifted Grunwald formulas to obtain a stable finite difference approximation to fractional advection-diffusion equations. Then, we apply two preconditioned iterative methods, namely, the preconditioned generalized minimal residual (preconditioned GMRES) method and the preconditioned conjugate gradient for normal residual (preconditioned CGN) method, to solve the corresponding discritized systems. We make comparisons between the preconditioners commonly used in the parallelization of the preconditioned Krylov subspace methods. The results suggest that preconditioning technique is a promising candidate for solving large-scale linear systems arising from fractional models. 2019-05-07T09:59:42Z 2019-05-07T09:59:42Z 2017 บทความวารสาร 0125-2526 http://it.science.cmu.ac.th/ejournal/dl.php?journal_id=8503 http://cmuir.cmu.ac.th/jspui/handle/6653943832/64001 Eng Science Faculty of Chiang Mai University
institution Chiang Mai University
building Chiang Mai University Library
country Thailand
collection CMU Intellectual Repository
language English
description This study develops and analyzes preconditioned Krylov subspace methods for solving discretization of the time-independent space-fractional models. First we apply a shifted Grunwald formulas to obtain a stable finite difference approximation to fractional advection-diffusion equations. Then, we apply two preconditioned iterative methods, namely, the preconditioned generalized minimal residual (preconditioned GMRES) method and the preconditioned conjugate gradient for normal residual (preconditioned CGN) method, to solve the corresponding discritized systems. We make comparisons between the preconditioners commonly used in the parallelization of the preconditioned Krylov subspace methods. The results suggest that preconditioning technique is a promising candidate for solving large-scale linear systems arising from fractional models.
format บทความวารสาร
author Reza Khoshsiar Ghaziani
Mojtaba Fardi
Mehdi Ghasemi
spellingShingle Reza Khoshsiar Ghaziani
Mojtaba Fardi
Mehdi Ghasemi
Solving large systems arising from fractional model by preconditioned methods
author_facet Reza Khoshsiar Ghaziani
Mojtaba Fardi
Mehdi Ghasemi
author_sort Reza Khoshsiar Ghaziani
title Solving large systems arising from fractional model by preconditioned methods
title_short Solving large systems arising from fractional model by preconditioned methods
title_full Solving large systems arising from fractional model by preconditioned methods
title_fullStr Solving large systems arising from fractional model by preconditioned methods
title_full_unstemmed Solving large systems arising from fractional model by preconditioned methods
title_sort solving large systems arising from fractional model by preconditioned methods
publisher Science Faculty of Chiang Mai University
publishDate 2019
url http://it.science.cmu.ac.th/ejournal/dl.php?journal_id=8503
http://cmuir.cmu.ac.th/jspui/handle/6653943832/64001
_version_ 1681426000153411584

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