An evaluation of low-cost heuristics for matrix bandwidth and profile reductions
Abstract Hundreds of heuristics have been proposed to resolve the problems of bandwidth and profile reductions since the 1960s. We found 132 heuristics that have been applied to these problems in reviews of the literature. Among them, 14 were selected for which no other simulation or comparison reve...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Gonzaga de Oliveira, Sanderson L. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2016 |
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| Schlagwörter: |
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| Anmerkung: |
© SBMAC - Sociedade Brasileira de Matemática Aplicada e Computacional 2016 |
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| Übergeordnetes Werk: |
Enthalten in: Computational and applied mathematics - Berlin : Springer, 2003, 37(2016), 2 vom: 07. Dez., Seite 1412-1471 |
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| Übergeordnetes Werk: |
volume:37 ; year:2016 ; number:2 ; day:07 ; month:12 ; pages:1412-1471 |
| Links: |
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| DOI / URN: |
10.1007/s40314-016-0394-9 |
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SPR036751855 |
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| 100 | 1 | |a Gonzaga de Oliveira, Sanderson L. |e verfasserin |4 aut | |
| 245 | 1 | 3 | |a An evaluation of low-cost heuristics for matrix bandwidth and profile reductions |
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| 520 | |a Abstract Hundreds of heuristics have been proposed to resolve the problems of bandwidth and profile reductions since the 1960s. We found 132 heuristics that have been applied to these problems in reviews of the literature. Among them, 14 were selected for which no other simulation or comparison revealed that the heuristic could be superseded by any other algorithm in the analyzed articles with respect to bandwidth or profile reduction. We also considered the computational costs of the heuristics during this process. Therefore, these 14 heuristics were selected as potentially being the best low-cost methods to solve the bandwidth and/or profile reduction problems. Results of the 14 selected heuristics are evaluated in this work. For evaluation on the set of test problems, a metric based on the relative percentage distance to the best possible bandwidth or profile is proposed. The most promising heuristics for several application areas are identified. Moreover, it was found that the FNCHC and GPS heuristics showed the best overall results in reducing the bandwidth of symmetric and asymmetric matrices among the evaluated heuristics, respectively. In addition, the NSloan and MPG heuristics showed the best overall results in reducing the profile of symmetric and asymmetric matrices among the heuristics among the evaluated heuristics, respectively. | ||
| 650 | 4 | |a Bandwidth reduction |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Profile reduction |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Combinatorial optimization |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Envelope reduction problem |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Heuristics |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Metaheuristics |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Reordering algorithms |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Sparse matrices |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Reordering algorithms |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Renumbering |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Ordering |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Graph labeling |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Bandwidth minimization |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Bernardes, Júnior A. B. |4 aut | |
| 700 | 1 | |a Chagas, Guilherme O. |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Computational and applied mathematics |d Berlin : Springer, 2003 |g 37(2016), 2 vom: 07. Dez., Seite 1412-1471 |w (DE-627)47617502X |w (DE-600)2171678-X |x 1807-0302 |7 nnns |
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10.1007/s40314-016-0394-9 doi (DE-627)SPR036751855 (SPR)s40314-016-0394-9-e DE-627 ger DE-627 rakwb eng Gonzaga de Oliveira, Sanderson L. verfasserin aut An evaluation of low-cost heuristics for matrix bandwidth and profile reductions 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © SBMAC - Sociedade Brasileira de Matemática Aplicada e Computacional 2016 Abstract Hundreds of heuristics have been proposed to resolve the problems of bandwidth and profile reductions since the 1960s. We found 132 heuristics that have been applied to these problems in reviews of the literature. Among them, 14 were selected for which no other simulation or comparison revealed that the heuristic could be superseded by any other algorithm in the analyzed articles with respect to bandwidth or profile reduction. We also considered the computational costs of the heuristics during this process. Therefore, these 14 heuristics were selected as potentially being the best low-cost methods to solve the bandwidth and/or profile reduction problems. Results of the 14 selected heuristics are evaluated in this work. For evaluation on the set of test problems, a metric based on the relative percentage distance to the best possible bandwidth or profile is proposed. The most promising heuristics for several application areas are identified. Moreover, it was found that the FNCHC and GPS heuristics showed the best overall results in reducing the bandwidth of symmetric and asymmetric matrices among the evaluated heuristics, respectively. In addition, the NSloan and MPG heuristics showed the best overall results in reducing the profile of symmetric and asymmetric matrices among the heuristics among the evaluated heuristics, respectively. Bandwidth reduction (dpeaa)DE-He213 Profile reduction (dpeaa)DE-He213 Combinatorial optimization (dpeaa)DE-He213 Envelope reduction problem (dpeaa)DE-He213 Heuristics (dpeaa)DE-He213 Metaheuristics (dpeaa)DE-He213 Reordering algorithms (dpeaa)DE-He213 Sparse matrices (dpeaa)DE-He213 Reordering algorithms (dpeaa)DE-He213 Renumbering (dpeaa)DE-He213 Ordering (dpeaa)DE-He213 Graph labeling (dpeaa)DE-He213 Bandwidth minimization (dpeaa)DE-He213 Bernardes, Júnior A. B. aut Chagas, Guilherme O. aut Enthalten in Computational and applied mathematics Berlin : Springer, 2003 37(2016), 2 vom: 07. Dez., Seite 1412-1471 (DE-627)47617502X (DE-600)2171678-X 1807-0302 nnns volume:37 year:2016 number:2 day:07 month:12 pages:1412-1471 https://dx.doi.org/10.1007/s40314-016-0394-9 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_65 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_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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 37 2016 2 07 12 1412-1471 |
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10.1007/s40314-016-0394-9 doi (DE-627)SPR036751855 (SPR)s40314-016-0394-9-e DE-627 ger DE-627 rakwb eng Gonzaga de Oliveira, Sanderson L. verfasserin aut An evaluation of low-cost heuristics for matrix bandwidth and profile reductions 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © SBMAC - Sociedade Brasileira de Matemática Aplicada e Computacional 2016 Abstract Hundreds of heuristics have been proposed to resolve the problems of bandwidth and profile reductions since the 1960s. We found 132 heuristics that have been applied to these problems in reviews of the literature. Among them, 14 were selected for which no other simulation or comparison revealed that the heuristic could be superseded by any other algorithm in the analyzed articles with respect to bandwidth or profile reduction. We also considered the computational costs of the heuristics during this process. Therefore, these 14 heuristics were selected as potentially being the best low-cost methods to solve the bandwidth and/or profile reduction problems. Results of the 14 selected heuristics are evaluated in this work. For evaluation on the set of test problems, a metric based on the relative percentage distance to the best possible bandwidth or profile is proposed. The most promising heuristics for several application areas are identified. Moreover, it was found that the FNCHC and GPS heuristics showed the best overall results in reducing the bandwidth of symmetric and asymmetric matrices among the evaluated heuristics, respectively. In addition, the NSloan and MPG heuristics showed the best overall results in reducing the profile of symmetric and asymmetric matrices among the heuristics among the evaluated heuristics, respectively. Bandwidth reduction (dpeaa)DE-He213 Profile reduction (dpeaa)DE-He213 Combinatorial optimization (dpeaa)DE-He213 Envelope reduction problem (dpeaa)DE-He213 Heuristics (dpeaa)DE-He213 Metaheuristics (dpeaa)DE-He213 Reordering algorithms (dpeaa)DE-He213 Sparse matrices (dpeaa)DE-He213 Reordering algorithms (dpeaa)DE-He213 Renumbering (dpeaa)DE-He213 Ordering (dpeaa)DE-He213 Graph labeling (dpeaa)DE-He213 Bandwidth minimization (dpeaa)DE-He213 Bernardes, Júnior A. B. aut Chagas, Guilherme O. aut Enthalten in Computational and applied mathematics Berlin : Springer, 2003 37(2016), 2 vom: 07. Dez., Seite 1412-1471 (DE-627)47617502X (DE-600)2171678-X 1807-0302 nnns volume:37 year:2016 number:2 day:07 month:12 pages:1412-1471 https://dx.doi.org/10.1007/s40314-016-0394-9 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_65 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_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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 37 2016 2 07 12 1412-1471 |
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10.1007/s40314-016-0394-9 doi (DE-627)SPR036751855 (SPR)s40314-016-0394-9-e DE-627 ger DE-627 rakwb eng Gonzaga de Oliveira, Sanderson L. verfasserin aut An evaluation of low-cost heuristics for matrix bandwidth and profile reductions 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © SBMAC - Sociedade Brasileira de Matemática Aplicada e Computacional 2016 Abstract Hundreds of heuristics have been proposed to resolve the problems of bandwidth and profile reductions since the 1960s. We found 132 heuristics that have been applied to these problems in reviews of the literature. Among them, 14 were selected for which no other simulation or comparison revealed that the heuristic could be superseded by any other algorithm in the analyzed articles with respect to bandwidth or profile reduction. We also considered the computational costs of the heuristics during this process. Therefore, these 14 heuristics were selected as potentially being the best low-cost methods to solve the bandwidth and/or profile reduction problems. Results of the 14 selected heuristics are evaluated in this work. For evaluation on the set of test problems, a metric based on the relative percentage distance to the best possible bandwidth or profile is proposed. The most promising heuristics for several application areas are identified. Moreover, it was found that the FNCHC and GPS heuristics showed the best overall results in reducing the bandwidth of symmetric and asymmetric matrices among the evaluated heuristics, respectively. In addition, the NSloan and MPG heuristics showed the best overall results in reducing the profile of symmetric and asymmetric matrices among the heuristics among the evaluated heuristics, respectively. Bandwidth reduction (dpeaa)DE-He213 Profile reduction (dpeaa)DE-He213 Combinatorial optimization (dpeaa)DE-He213 Envelope reduction problem (dpeaa)DE-He213 Heuristics (dpeaa)DE-He213 Metaheuristics (dpeaa)DE-He213 Reordering algorithms (dpeaa)DE-He213 Sparse matrices (dpeaa)DE-He213 Reordering algorithms (dpeaa)DE-He213 Renumbering (dpeaa)DE-He213 Ordering (dpeaa)DE-He213 Graph labeling (dpeaa)DE-He213 Bandwidth minimization (dpeaa)DE-He213 Bernardes, Júnior A. B. aut Chagas, Guilherme O. aut Enthalten in Computational and applied mathematics Berlin : Springer, 2003 37(2016), 2 vom: 07. Dez., Seite 1412-1471 (DE-627)47617502X (DE-600)2171678-X 1807-0302 nnns volume:37 year:2016 number:2 day:07 month:12 pages:1412-1471 https://dx.doi.org/10.1007/s40314-016-0394-9 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_65 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_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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 37 2016 2 07 12 1412-1471 |
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10.1007/s40314-016-0394-9 doi (DE-627)SPR036751855 (SPR)s40314-016-0394-9-e DE-627 ger DE-627 rakwb eng Gonzaga de Oliveira, Sanderson L. verfasserin aut An evaluation of low-cost heuristics for matrix bandwidth and profile reductions 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © SBMAC - Sociedade Brasileira de Matemática Aplicada e Computacional 2016 Abstract Hundreds of heuristics have been proposed to resolve the problems of bandwidth and profile reductions since the 1960s. We found 132 heuristics that have been applied to these problems in reviews of the literature. Among them, 14 were selected for which no other simulation or comparison revealed that the heuristic could be superseded by any other algorithm in the analyzed articles with respect to bandwidth or profile reduction. We also considered the computational costs of the heuristics during this process. Therefore, these 14 heuristics were selected as potentially being the best low-cost methods to solve the bandwidth and/or profile reduction problems. Results of the 14 selected heuristics are evaluated in this work. For evaluation on the set of test problems, a metric based on the relative percentage distance to the best possible bandwidth or profile is proposed. The most promising heuristics for several application areas are identified. Moreover, it was found that the FNCHC and GPS heuristics showed the best overall results in reducing the bandwidth of symmetric and asymmetric matrices among the evaluated heuristics, respectively. In addition, the NSloan and MPG heuristics showed the best overall results in reducing the profile of symmetric and asymmetric matrices among the heuristics among the evaluated heuristics, respectively. Bandwidth reduction (dpeaa)DE-He213 Profile reduction (dpeaa)DE-He213 Combinatorial optimization (dpeaa)DE-He213 Envelope reduction problem (dpeaa)DE-He213 Heuristics (dpeaa)DE-He213 Metaheuristics (dpeaa)DE-He213 Reordering algorithms (dpeaa)DE-He213 Sparse matrices (dpeaa)DE-He213 Reordering algorithms (dpeaa)DE-He213 Renumbering (dpeaa)DE-He213 Ordering (dpeaa)DE-He213 Graph labeling (dpeaa)DE-He213 Bandwidth minimization (dpeaa)DE-He213 Bernardes, Júnior A. B. aut Chagas, Guilherme O. aut Enthalten in Computational and applied mathematics Berlin : Springer, 2003 37(2016), 2 vom: 07. Dez., Seite 1412-1471 (DE-627)47617502X (DE-600)2171678-X 1807-0302 nnns volume:37 year:2016 number:2 day:07 month:12 pages:1412-1471 https://dx.doi.org/10.1007/s40314-016-0394-9 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_65 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_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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 37 2016 2 07 12 1412-1471 |
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10.1007/s40314-016-0394-9 doi (DE-627)SPR036751855 (SPR)s40314-016-0394-9-e DE-627 ger DE-627 rakwb eng Gonzaga de Oliveira, Sanderson L. verfasserin aut An evaluation of low-cost heuristics for matrix bandwidth and profile reductions 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © SBMAC - Sociedade Brasileira de Matemática Aplicada e Computacional 2016 Abstract Hundreds of heuristics have been proposed to resolve the problems of bandwidth and profile reductions since the 1960s. We found 132 heuristics that have been applied to these problems in reviews of the literature. Among them, 14 were selected for which no other simulation or comparison revealed that the heuristic could be superseded by any other algorithm in the analyzed articles with respect to bandwidth or profile reduction. We also considered the computational costs of the heuristics during this process. Therefore, these 14 heuristics were selected as potentially being the best low-cost methods to solve the bandwidth and/or profile reduction problems. Results of the 14 selected heuristics are evaluated in this work. For evaluation on the set of test problems, a metric based on the relative percentage distance to the best possible bandwidth or profile is proposed. The most promising heuristics for several application areas are identified. Moreover, it was found that the FNCHC and GPS heuristics showed the best overall results in reducing the bandwidth of symmetric and asymmetric matrices among the evaluated heuristics, respectively. In addition, the NSloan and MPG heuristics showed the best overall results in reducing the profile of symmetric and asymmetric matrices among the heuristics among the evaluated heuristics, respectively. Bandwidth reduction (dpeaa)DE-He213 Profile reduction (dpeaa)DE-He213 Combinatorial optimization (dpeaa)DE-He213 Envelope reduction problem (dpeaa)DE-He213 Heuristics (dpeaa)DE-He213 Metaheuristics (dpeaa)DE-He213 Reordering algorithms (dpeaa)DE-He213 Sparse matrices (dpeaa)DE-He213 Reordering algorithms (dpeaa)DE-He213 Renumbering (dpeaa)DE-He213 Ordering (dpeaa)DE-He213 Graph labeling (dpeaa)DE-He213 Bandwidth minimization (dpeaa)DE-He213 Bernardes, Júnior A. B. aut Chagas, Guilherme O. aut Enthalten in Computational and applied mathematics Berlin : Springer, 2003 37(2016), 2 vom: 07. Dez., Seite 1412-1471 (DE-627)47617502X (DE-600)2171678-X 1807-0302 nnns volume:37 year:2016 number:2 day:07 month:12 pages:1412-1471 https://dx.doi.org/10.1007/s40314-016-0394-9 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_65 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_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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 37 2016 2 07 12 1412-1471 |
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Gonzaga de Oliveira, Sanderson L. @@aut@@ Bernardes, Júnior A. B. @@aut@@ Chagas, Guilherme O. @@aut@@ |
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Gonzaga de Oliveira, Sanderson L. |
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Gonzaga de Oliveira, Sanderson L. misc Bandwidth reduction misc Profile reduction misc Combinatorial optimization misc Envelope reduction problem misc Heuristics misc Metaheuristics misc Reordering algorithms misc Sparse matrices misc Renumbering misc Ordering misc Graph labeling misc Bandwidth minimization An evaluation of low-cost heuristics for matrix bandwidth and profile reductions |
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An evaluation of low-cost heuristics for matrix bandwidth and profile reductions Bandwidth reduction (dpeaa)DE-He213 Profile reduction (dpeaa)DE-He213 Combinatorial optimization (dpeaa)DE-He213 Envelope reduction problem (dpeaa)DE-He213 Heuristics (dpeaa)DE-He213 Metaheuristics (dpeaa)DE-He213 Reordering algorithms (dpeaa)DE-He213 Sparse matrices (dpeaa)DE-He213 Renumbering (dpeaa)DE-He213 Ordering (dpeaa)DE-He213 Graph labeling (dpeaa)DE-He213 Bandwidth minimization (dpeaa)DE-He213 |
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misc Bandwidth reduction misc Profile reduction misc Combinatorial optimization misc Envelope reduction problem misc Heuristics misc Metaheuristics misc Reordering algorithms misc Sparse matrices misc Renumbering misc Ordering misc Graph labeling misc Bandwidth minimization |
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evaluation of low-cost heuristics for matrix bandwidth and profile reductions |
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An evaluation of low-cost heuristics for matrix bandwidth and profile reductions |
| abstract |
Abstract Hundreds of heuristics have been proposed to resolve the problems of bandwidth and profile reductions since the 1960s. We found 132 heuristics that have been applied to these problems in reviews of the literature. Among them, 14 were selected for which no other simulation or comparison revealed that the heuristic could be superseded by any other algorithm in the analyzed articles with respect to bandwidth or profile reduction. We also considered the computational costs of the heuristics during this process. Therefore, these 14 heuristics were selected as potentially being the best low-cost methods to solve the bandwidth and/or profile reduction problems. Results of the 14 selected heuristics are evaluated in this work. For evaluation on the set of test problems, a metric based on the relative percentage distance to the best possible bandwidth or profile is proposed. The most promising heuristics for several application areas are identified. Moreover, it was found that the FNCHC and GPS heuristics showed the best overall results in reducing the bandwidth of symmetric and asymmetric matrices among the evaluated heuristics, respectively. In addition, the NSloan and MPG heuristics showed the best overall results in reducing the profile of symmetric and asymmetric matrices among the heuristics among the evaluated heuristics, respectively. © SBMAC - Sociedade Brasileira de Matemática Aplicada e Computacional 2016 |
| abstractGer |
Abstract Hundreds of heuristics have been proposed to resolve the problems of bandwidth and profile reductions since the 1960s. We found 132 heuristics that have been applied to these problems in reviews of the literature. Among them, 14 were selected for which no other simulation or comparison revealed that the heuristic could be superseded by any other algorithm in the analyzed articles with respect to bandwidth or profile reduction. We also considered the computational costs of the heuristics during this process. Therefore, these 14 heuristics were selected as potentially being the best low-cost methods to solve the bandwidth and/or profile reduction problems. Results of the 14 selected heuristics are evaluated in this work. For evaluation on the set of test problems, a metric based on the relative percentage distance to the best possible bandwidth or profile is proposed. The most promising heuristics for several application areas are identified. Moreover, it was found that the FNCHC and GPS heuristics showed the best overall results in reducing the bandwidth of symmetric and asymmetric matrices among the evaluated heuristics, respectively. In addition, the NSloan and MPG heuristics showed the best overall results in reducing the profile of symmetric and asymmetric matrices among the heuristics among the evaluated heuristics, respectively. © SBMAC - Sociedade Brasileira de Matemática Aplicada e Computacional 2016 |
| abstract_unstemmed |
Abstract Hundreds of heuristics have been proposed to resolve the problems of bandwidth and profile reductions since the 1960s. We found 132 heuristics that have been applied to these problems in reviews of the literature. Among them, 14 were selected for which no other simulation or comparison revealed that the heuristic could be superseded by any other algorithm in the analyzed articles with respect to bandwidth or profile reduction. We also considered the computational costs of the heuristics during this process. Therefore, these 14 heuristics were selected as potentially being the best low-cost methods to solve the bandwidth and/or profile reduction problems. Results of the 14 selected heuristics are evaluated in this work. For evaluation on the set of test problems, a metric based on the relative percentage distance to the best possible bandwidth or profile is proposed. The most promising heuristics for several application areas are identified. Moreover, it was found that the FNCHC and GPS heuristics showed the best overall results in reducing the bandwidth of symmetric and asymmetric matrices among the evaluated heuristics, respectively. In addition, the NSloan and MPG heuristics showed the best overall results in reducing the profile of symmetric and asymmetric matrices among the heuristics among the evaluated heuristics, respectively. © SBMAC - Sociedade Brasileira de Matemática Aplicada e Computacional 2016 |
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| title_short |
An evaluation of low-cost heuristics for matrix bandwidth and profile reductions |
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https://dx.doi.org/10.1007/s40314-016-0394-9 |
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Bernardes, Júnior A. B. Chagas, Guilherme O. |
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Bernardes, Júnior A. B. Chagas, Guilherme O. |
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2024-07-03T19:26:55.425Z |
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| score |
7.402323 |