Towards an objective feasibility pump for convex MINLPs
Abstract This paper describes a heuristic algorithm for finding good feasible solutions of convex mixed-integer nonlinear programs (MINLPs). The algorithm we propose is a modification of the feasibility pump heuristic, in which we aim at balancing the two goals of quickly obtaining a feasible soluti...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Sharma, Shaurya [verfasserIn] Knudsen, Brage Rugstad [verfasserIn] Grimstad, Bjarne [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2015 |
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| Schlagwörter: |
Mixed-integer programming heuristics |
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| Übergeordnetes Werk: |
Enthalten in: Computational optimization and applications - New York, NY [u.a.] : Springer Science + Business Media B.V., 1992, 63(2015), 3 vom: 28. Sept., Seite 737-753 |
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| Übergeordnetes Werk: |
volume:63 ; year:2015 ; number:3 ; day:28 ; month:09 ; pages:737-753 |
| Links: |
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| DOI / URN: |
10.1007/s10589-015-9792-y |
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| Katalog-ID: |
SPR011551364 |
|---|
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| 245 | 1 | 0 | |a Towards an objective feasibility pump for convex MINLPs |
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| 520 | |a Abstract This paper describes a heuristic algorithm for finding good feasible solutions of convex mixed-integer nonlinear programs (MINLPs). The algorithm we propose is a modification of the feasibility pump heuristic, in which we aim at balancing the two goals of quickly obtaining a feasible solution and preserving quality of the solution with respect to the original objective. The effectiveness and merits of the proposed algorithm are assessed by evaluation of extensive computational results from a set of 146 convex MINLP test problems. We also show how a set of user-defined parameters may be selected to strike a balance between low computation time and high solution quality. | ||
| 650 | 4 | |a Mixed-integer programming heuristics |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Feasibility pump |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Mixed-integer nonlinear programming |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Primal heuristics |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Knudsen, Brage Rugstad |e verfasserin |4 aut | |
| 700 | 1 | |a Grimstad, Bjarne |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Computational optimization and applications |d New York, NY [u.a.] : Springer Science + Business Media B.V., 1992 |g 63(2015), 3 vom: 28. Sept., Seite 737-753 |w (DE-627)266881297 |w (DE-600)1467967-X |x 1573-2894 |7 nnns |
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10.1007/s10589-015-9792-y doi (DE-627)SPR011551364 (SPR)s10589-015-9792-y-e DE-627 ger DE-627 rakwb eng 510 ASE 31.80 bkl 54.80 bkl Sharma, Shaurya verfasserin aut Towards an objective feasibility pump for convex MINLPs 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper describes a heuristic algorithm for finding good feasible solutions of convex mixed-integer nonlinear programs (MINLPs). The algorithm we propose is a modification of the feasibility pump heuristic, in which we aim at balancing the two goals of quickly obtaining a feasible solution and preserving quality of the solution with respect to the original objective. The effectiveness and merits of the proposed algorithm are assessed by evaluation of extensive computational results from a set of 146 convex MINLP test problems. We also show how a set of user-defined parameters may be selected to strike a balance between low computation time and high solution quality. Mixed-integer programming heuristics (dpeaa)DE-He213 Feasibility pump (dpeaa)DE-He213 Mixed-integer nonlinear programming (dpeaa)DE-He213 Primal heuristics (dpeaa)DE-He213 Knudsen, Brage Rugstad verfasserin aut Grimstad, Bjarne verfasserin aut Enthalten in Computational optimization and applications New York, NY [u.a.] : Springer Science + Business Media B.V., 1992 63(2015), 3 vom: 28. Sept., Seite 737-753 (DE-627)266881297 (DE-600)1467967-X 1573-2894 nnns volume:63 year:2015 number:3 day:28 month:09 pages:737-753 https://dx.doi.org/10.1007/s10589-015-9792-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-MAT SSG-OPC-ASE 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_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_4012 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 31.80 ASE 54.80 ASE AR 63 2015 3 28 09 737-753 |
| spelling |
10.1007/s10589-015-9792-y doi (DE-627)SPR011551364 (SPR)s10589-015-9792-y-e DE-627 ger DE-627 rakwb eng 510 ASE 31.80 bkl 54.80 bkl Sharma, Shaurya verfasserin aut Towards an objective feasibility pump for convex MINLPs 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper describes a heuristic algorithm for finding good feasible solutions of convex mixed-integer nonlinear programs (MINLPs). The algorithm we propose is a modification of the feasibility pump heuristic, in which we aim at balancing the two goals of quickly obtaining a feasible solution and preserving quality of the solution with respect to the original objective. The effectiveness and merits of the proposed algorithm are assessed by evaluation of extensive computational results from a set of 146 convex MINLP test problems. We also show how a set of user-defined parameters may be selected to strike a balance between low computation time and high solution quality. Mixed-integer programming heuristics (dpeaa)DE-He213 Feasibility pump (dpeaa)DE-He213 Mixed-integer nonlinear programming (dpeaa)DE-He213 Primal heuristics (dpeaa)DE-He213 Knudsen, Brage Rugstad verfasserin aut Grimstad, Bjarne verfasserin aut Enthalten in Computational optimization and applications New York, NY [u.a.] : Springer Science + Business Media B.V., 1992 63(2015), 3 vom: 28. Sept., Seite 737-753 (DE-627)266881297 (DE-600)1467967-X 1573-2894 nnns volume:63 year:2015 number:3 day:28 month:09 pages:737-753 https://dx.doi.org/10.1007/s10589-015-9792-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-MAT SSG-OPC-ASE 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_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_4012 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 31.80 ASE 54.80 ASE AR 63 2015 3 28 09 737-753 |
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Sharma, Shaurya @@aut@@ Knudsen, Brage Rugstad @@aut@@ Grimstad, Bjarne @@aut@@ |
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Sharma, Shaurya |
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510 ASE 31.80 bkl 54.80 bkl Towards an objective feasibility pump for convex MINLPs Mixed-integer programming heuristics (dpeaa)DE-He213 Feasibility pump (dpeaa)DE-He213 Mixed-integer nonlinear programming (dpeaa)DE-He213 Primal heuristics (dpeaa)DE-He213 |
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towards an objective feasibility pump for convex minlps |
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Towards an objective feasibility pump for convex MINLPs |
| abstract |
Abstract This paper describes a heuristic algorithm for finding good feasible solutions of convex mixed-integer nonlinear programs (MINLPs). The algorithm we propose is a modification of the feasibility pump heuristic, in which we aim at balancing the two goals of quickly obtaining a feasible solution and preserving quality of the solution with respect to the original objective. The effectiveness and merits of the proposed algorithm are assessed by evaluation of extensive computational results from a set of 146 convex MINLP test problems. We also show how a set of user-defined parameters may be selected to strike a balance between low computation time and high solution quality. |
| abstractGer |
Abstract This paper describes a heuristic algorithm for finding good feasible solutions of convex mixed-integer nonlinear programs (MINLPs). The algorithm we propose is a modification of the feasibility pump heuristic, in which we aim at balancing the two goals of quickly obtaining a feasible solution and preserving quality of the solution with respect to the original objective. The effectiveness and merits of the proposed algorithm are assessed by evaluation of extensive computational results from a set of 146 convex MINLP test problems. We also show how a set of user-defined parameters may be selected to strike a balance between low computation time and high solution quality. |
| abstract_unstemmed |
Abstract This paper describes a heuristic algorithm for finding good feasible solutions of convex mixed-integer nonlinear programs (MINLPs). The algorithm we propose is a modification of the feasibility pump heuristic, in which we aim at balancing the two goals of quickly obtaining a feasible solution and preserving quality of the solution with respect to the original objective. The effectiveness and merits of the proposed algorithm are assessed by evaluation of extensive computational results from a set of 146 convex MINLP test problems. We also show how a set of user-defined parameters may be selected to strike a balance between low computation time and high solution quality. |
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| title_short |
Towards an objective feasibility pump for convex MINLPs |
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https://dx.doi.org/10.1007/s10589-015-9792-y |
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| author2 |
Knudsen, Brage Rugstad Grimstad, Bjarne |
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Knudsen, Brage Rugstad Grimstad, Bjarne |
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| doi_str |
10.1007/s10589-015-9792-y |
| up_date |
2024-07-03T23:18:19.429Z |
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| score |
7.4009056 |