An adaptively preconditioned multi-step matrix splitting iteration for computing PageRank
Abstract The multi-step matrix splitting iteration (MPIO) for computing PageRank is an efficient iterative method by combining the multi-step power method with the inner-outer iterative method. In this paper, with the aim of accelerating the computation of PageRank problems, a new method is proposed...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Wen, Chun [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
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| Übergeordnetes Werk: |
Enthalten in: Numerical algorithms - Bussum : Baltzer, 1991, 92(2022), 2 vom: 30. Juni, Seite 1213-1231 |
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| Übergeordnetes Werk: |
volume:92 ; year:2022 ; number:2 ; day:30 ; month:06 ; pages:1213-1231 |
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| DOI / URN: |
10.1007/s11075-022-01337-4 |
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SPR049053825 |
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| 245 | 1 | 3 | |a An adaptively preconditioned multi-step matrix splitting iteration for computing PageRank |
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| 520 | |a Abstract The multi-step matrix splitting iteration (MPIO) for computing PageRank is an efficient iterative method by combining the multi-step power method with the inner-outer iterative method. In this paper, with the aim of accelerating the computation of PageRank problems, a new method is proposed by preconditioning the MPIO method with an adaptive generalized Arnoldi (GArnoldi) method. The new method is called as an adaptive GArnoldi-MPIO method, whose construction and convergence analysis are discussed in detail. Numerical experiments on several PageRank problems are reported to illustrate the effectiveness of our proposed method. | ||
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| 650 | 4 | |a Multi-step matrix splitting iteration |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Generalized Arnoldi method |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Power method |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a The inner-outer iteration |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Hu, Qian-Ying |4 aut | |
| 700 | 1 | |a Shen, Zhao-Li |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Numerical algorithms |d Bussum : Baltzer, 1991 |g 92(2022), 2 vom: 30. Juni, Seite 1213-1231 |w (DE-627)318468581 |w (DE-600)2002650-X |x 1572-9265 |7 nnns |
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10.1007/s11075-022-01337-4 doi (DE-627)SPR049053825 (SPR)s11075-022-01337-4-e DE-627 ger DE-627 rakwb eng Wen, Chun verfasserin (orcid)0000-0003-0561-5787 aut An adaptively preconditioned multi-step matrix splitting iteration for computing PageRank 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract The multi-step matrix splitting iteration (MPIO) for computing PageRank is an efficient iterative method by combining the multi-step power method with the inner-outer iterative method. In this paper, with the aim of accelerating the computation of PageRank problems, a new method is proposed by preconditioning the MPIO method with an adaptive generalized Arnoldi (GArnoldi) method. The new method is called as an adaptive GArnoldi-MPIO method, whose construction and convergence analysis are discussed in detail. Numerical experiments on several PageRank problems are reported to illustrate the effectiveness of our proposed method. PageRank (dpeaa)DE-He213 Multi-step matrix splitting iteration (dpeaa)DE-He213 Generalized Arnoldi method (dpeaa)DE-He213 Power method (dpeaa)DE-He213 The inner-outer iteration (dpeaa)DE-He213 Hu, Qian-Ying aut Shen, Zhao-Li aut Enthalten in Numerical algorithms Bussum : Baltzer, 1991 92(2022), 2 vom: 30. Juni, Seite 1213-1231 (DE-627)318468581 (DE-600)2002650-X 1572-9265 nnns volume:92 year:2022 number:2 day:30 month:06 pages:1213-1231 https://dx.doi.org/10.1007/s11075-022-01337-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 92 2022 2 30 06 1213-1231 |
| spelling |
10.1007/s11075-022-01337-4 doi (DE-627)SPR049053825 (SPR)s11075-022-01337-4-e DE-627 ger DE-627 rakwb eng Wen, Chun verfasserin (orcid)0000-0003-0561-5787 aut An adaptively preconditioned multi-step matrix splitting iteration for computing PageRank 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract The multi-step matrix splitting iteration (MPIO) for computing PageRank is an efficient iterative method by combining the multi-step power method with the inner-outer iterative method. In this paper, with the aim of accelerating the computation of PageRank problems, a new method is proposed by preconditioning the MPIO method with an adaptive generalized Arnoldi (GArnoldi) method. The new method is called as an adaptive GArnoldi-MPIO method, whose construction and convergence analysis are discussed in detail. Numerical experiments on several PageRank problems are reported to illustrate the effectiveness of our proposed method. PageRank (dpeaa)DE-He213 Multi-step matrix splitting iteration (dpeaa)DE-He213 Generalized Arnoldi method (dpeaa)DE-He213 Power method (dpeaa)DE-He213 The inner-outer iteration (dpeaa)DE-He213 Hu, Qian-Ying aut Shen, Zhao-Li aut Enthalten in Numerical algorithms Bussum : Baltzer, 1991 92(2022), 2 vom: 30. Juni, Seite 1213-1231 (DE-627)318468581 (DE-600)2002650-X 1572-9265 nnns volume:92 year:2022 number:2 day:30 month:06 pages:1213-1231 https://dx.doi.org/10.1007/s11075-022-01337-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 92 2022 2 30 06 1213-1231 |
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10.1007/s11075-022-01337-4 doi (DE-627)SPR049053825 (SPR)s11075-022-01337-4-e DE-627 ger DE-627 rakwb eng Wen, Chun verfasserin (orcid)0000-0003-0561-5787 aut An adaptively preconditioned multi-step matrix splitting iteration for computing PageRank 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract The multi-step matrix splitting iteration (MPIO) for computing PageRank is an efficient iterative method by combining the multi-step power method with the inner-outer iterative method. In this paper, with the aim of accelerating the computation of PageRank problems, a new method is proposed by preconditioning the MPIO method with an adaptive generalized Arnoldi (GArnoldi) method. The new method is called as an adaptive GArnoldi-MPIO method, whose construction and convergence analysis are discussed in detail. Numerical experiments on several PageRank problems are reported to illustrate the effectiveness of our proposed method. PageRank (dpeaa)DE-He213 Multi-step matrix splitting iteration (dpeaa)DE-He213 Generalized Arnoldi method (dpeaa)DE-He213 Power method (dpeaa)DE-He213 The inner-outer iteration (dpeaa)DE-He213 Hu, Qian-Ying aut Shen, Zhao-Li aut Enthalten in Numerical algorithms Bussum : Baltzer, 1991 92(2022), 2 vom: 30. Juni, Seite 1213-1231 (DE-627)318468581 (DE-600)2002650-X 1572-9265 nnns volume:92 year:2022 number:2 day:30 month:06 pages:1213-1231 https://dx.doi.org/10.1007/s11075-022-01337-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 92 2022 2 30 06 1213-1231 |
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10.1007/s11075-022-01337-4 doi (DE-627)SPR049053825 (SPR)s11075-022-01337-4-e DE-627 ger DE-627 rakwb eng Wen, Chun verfasserin (orcid)0000-0003-0561-5787 aut An adaptively preconditioned multi-step matrix splitting iteration for computing PageRank 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract The multi-step matrix splitting iteration (MPIO) for computing PageRank is an efficient iterative method by combining the multi-step power method with the inner-outer iterative method. In this paper, with the aim of accelerating the computation of PageRank problems, a new method is proposed by preconditioning the MPIO method with an adaptive generalized Arnoldi (GArnoldi) method. The new method is called as an adaptive GArnoldi-MPIO method, whose construction and convergence analysis are discussed in detail. Numerical experiments on several PageRank problems are reported to illustrate the effectiveness of our proposed method. PageRank (dpeaa)DE-He213 Multi-step matrix splitting iteration (dpeaa)DE-He213 Generalized Arnoldi method (dpeaa)DE-He213 Power method (dpeaa)DE-He213 The inner-outer iteration (dpeaa)DE-He213 Hu, Qian-Ying aut Shen, Zhao-Li aut Enthalten in Numerical algorithms Bussum : Baltzer, 1991 92(2022), 2 vom: 30. Juni, Seite 1213-1231 (DE-627)318468581 (DE-600)2002650-X 1572-9265 nnns volume:92 year:2022 number:2 day:30 month:06 pages:1213-1231 https://dx.doi.org/10.1007/s11075-022-01337-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 92 2022 2 30 06 1213-1231 |
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10.1007/s11075-022-01337-4 doi (DE-627)SPR049053825 (SPR)s11075-022-01337-4-e DE-627 ger DE-627 rakwb eng Wen, Chun verfasserin (orcid)0000-0003-0561-5787 aut An adaptively preconditioned multi-step matrix splitting iteration for computing PageRank 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract The multi-step matrix splitting iteration (MPIO) for computing PageRank is an efficient iterative method by combining the multi-step power method with the inner-outer iterative method. In this paper, with the aim of accelerating the computation of PageRank problems, a new method is proposed by preconditioning the MPIO method with an adaptive generalized Arnoldi (GArnoldi) method. The new method is called as an adaptive GArnoldi-MPIO method, whose construction and convergence analysis are discussed in detail. Numerical experiments on several PageRank problems are reported to illustrate the effectiveness of our proposed method. PageRank (dpeaa)DE-He213 Multi-step matrix splitting iteration (dpeaa)DE-He213 Generalized Arnoldi method (dpeaa)DE-He213 Power method (dpeaa)DE-He213 The inner-outer iteration (dpeaa)DE-He213 Hu, Qian-Ying aut Shen, Zhao-Li aut Enthalten in Numerical algorithms Bussum : Baltzer, 1991 92(2022), 2 vom: 30. Juni, Seite 1213-1231 (DE-627)318468581 (DE-600)2002650-X 1572-9265 nnns volume:92 year:2022 number:2 day:30 month:06 pages:1213-1231 https://dx.doi.org/10.1007/s11075-022-01337-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 92 2022 2 30 06 1213-1231 |
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Wen, Chun |
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Wen, Chun misc PageRank misc Multi-step matrix splitting iteration misc Generalized Arnoldi method misc Power method misc The inner-outer iteration An adaptively preconditioned multi-step matrix splitting iteration for computing PageRank |
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An adaptively preconditioned multi-step matrix splitting iteration for computing PageRank PageRank (dpeaa)DE-He213 Multi-step matrix splitting iteration (dpeaa)DE-He213 Generalized Arnoldi method (dpeaa)DE-He213 Power method (dpeaa)DE-He213 The inner-outer iteration (dpeaa)DE-He213 |
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adaptively preconditioned multi-step matrix splitting iteration for computing pagerank |
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An adaptively preconditioned multi-step matrix splitting iteration for computing PageRank |
| abstract |
Abstract The multi-step matrix splitting iteration (MPIO) for computing PageRank is an efficient iterative method by combining the multi-step power method with the inner-outer iterative method. In this paper, with the aim of accelerating the computation of PageRank problems, a new method is proposed by preconditioning the MPIO method with an adaptive generalized Arnoldi (GArnoldi) method. The new method is called as an adaptive GArnoldi-MPIO method, whose construction and convergence analysis are discussed in detail. Numerical experiments on several PageRank problems are reported to illustrate the effectiveness of our proposed method. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
| abstractGer |
Abstract The multi-step matrix splitting iteration (MPIO) for computing PageRank is an efficient iterative method by combining the multi-step power method with the inner-outer iterative method. In this paper, with the aim of accelerating the computation of PageRank problems, a new method is proposed by preconditioning the MPIO method with an adaptive generalized Arnoldi (GArnoldi) method. The new method is called as an adaptive GArnoldi-MPIO method, whose construction and convergence analysis are discussed in detail. Numerical experiments on several PageRank problems are reported to illustrate the effectiveness of our proposed method. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
| abstract_unstemmed |
Abstract The multi-step matrix splitting iteration (MPIO) for computing PageRank is an efficient iterative method by combining the multi-step power method with the inner-outer iterative method. In this paper, with the aim of accelerating the computation of PageRank problems, a new method is proposed by preconditioning the MPIO method with an adaptive generalized Arnoldi (GArnoldi) method. The new method is called as an adaptive GArnoldi-MPIO method, whose construction and convergence analysis are discussed in detail. Numerical experiments on several PageRank problems are reported to illustrate the effectiveness of our proposed method. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR049053825</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230510060530.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230113s2022 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11075-022-01337-4</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR049053825</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s11075-022-01337-4-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Wen, Chun</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0003-0561-5787</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="3"><subfield code="a">An adaptively preconditioned multi-step matrix splitting iteration for computing PageRank</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract The multi-step matrix splitting iteration (MPIO) for computing PageRank is an efficient iterative method by combining the multi-step power method with the inner-outer iterative method. In this paper, with the aim of accelerating the computation of PageRank problems, a new method is proposed by preconditioning the MPIO method with an adaptive generalized Arnoldi (GArnoldi) method. The new method is called as an adaptive GArnoldi-MPIO method, whose construction and convergence analysis are discussed in detail. 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