Distributed continuous-time optimization for convex problems with coupling linear inequality constraints

Gespeichert in:

Autor*in:	Khamisov, Oleg O. [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2024

Schlagwörter:	Constraints-coupled optimization Convex optimization Distributed optimization Multi-agent systems

Übergeordnetes Werk:	Enthalten in: Computational management science - Heidelberg : Springer, 2003, 21(2024), 1 vom: Juni, Artikel-ID 21, Seite 1-20
Übergeordnetes Werk:	volume:21 ; year:2024 ; number:1 ; month:06 ; elocationid:21 ; pages:1-20

Links:	Link aufrufen Link aufrufen

DOI / URN:	10.1007/s10287-024-00501-6

Katalog-ID:	1898520674

Internformat


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982			\|2 26 \|1 00 \|x DE-206 \|b In this paper we propose a novel distributed continuous-time algorithm aimed to solve optimization problems with cost function being a sum of local strictly convex multidimensional functions associated to individual agents. Additionally, the problems can have coupled equality and inequality constraints. We prove global asymptotic convergence of the algorithm for a connected graph topology. In order to investigate its practical implementation, we analyze convergence when Euler method is applied to represent discrete-time communication. Finally, we support our results with numerical experiments of the developed approach application for power balancing in New England power system.

Indexfelder

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publishDate	2024
allfields	10.1007/s10287-024-00501-6 doi (DE-627)1898520674 (DE-599)KXP1898520674 DE-627 ger DE-627 rda eng Khamisov, Oleg O. verfasserin aut Distributed continuous-time optimization for convex problems with coupling linear inequality constraints Oleg O. Khamisov 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Constraints-coupled optimization (dpeaa)DE-206 Convex optimization (dpeaa)DE-206 Distributed optimization (dpeaa)DE-206 Multi-agent systems (dpeaa)DE-206 Enthalten in Computational management science Heidelberg : Springer, 2003 21(2024), 1 vom: Juni, Artikel-ID 21, Seite 1-20 (DE-627)363765263 (DE-600)2107564-5 (DE-576)276817729 1619-6988 nnns volume:21 year:2024 number:1 month:06 elocationid:21 pages:1-20 https://link.springer.com/content/pdf/10.1007/s10287-024-00501-6.pdf Verlag lizenzpflichtig https://doi.org/10.1007/s10287-024-00501-6 Resolving-System lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP 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_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_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 21 2024 1 6 21 1-20 26 01 0206 4565677857 x1z 13-08-24 26 00 DE-206 In this paper we propose a novel distributed continuous-time algorithm aimed to solve optimization problems with cost function being a sum of local strictly convex multidimensional functions associated to individual agents. Additionally, the problems can have coupled equality and inequality constraints. We prove global asymptotic convergence of the algorithm for a connected graph topology. In order to investigate its practical implementation, we analyze convergence when Euler method is applied to represent discrete-time communication. Finally, we support our results with numerical experiments of the developed approach application for power balancing in New England power system.
spelling	10.1007/s10287-024-00501-6 doi (DE-627)1898520674 (DE-599)KXP1898520674 DE-627 ger DE-627 rda eng Khamisov, Oleg O. verfasserin aut Distributed continuous-time optimization for convex problems with coupling linear inequality constraints Oleg O. Khamisov 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Constraints-coupled optimization (dpeaa)DE-206 Convex optimization (dpeaa)DE-206 Distributed optimization (dpeaa)DE-206 Multi-agent systems (dpeaa)DE-206 Enthalten in Computational management science Heidelberg : Springer, 2003 21(2024), 1 vom: Juni, Artikel-ID 21, Seite 1-20 (DE-627)363765263 (DE-600)2107564-5 (DE-576)276817729 1619-6988 nnns volume:21 year:2024 number:1 month:06 elocationid:21 pages:1-20 https://link.springer.com/content/pdf/10.1007/s10287-024-00501-6.pdf Verlag lizenzpflichtig https://doi.org/10.1007/s10287-024-00501-6 Resolving-System lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP 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_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_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 21 2024 1 6 21 1-20 26 01 0206 4565677857 x1z 13-08-24 26 00 DE-206 In this paper we propose a novel distributed continuous-time algorithm aimed to solve optimization problems with cost function being a sum of local strictly convex multidimensional functions associated to individual agents. Additionally, the problems can have coupled equality and inequality constraints. We prove global asymptotic convergence of the algorithm for a connected graph topology. In order to investigate its practical implementation, we analyze convergence when Euler method is applied to represent discrete-time communication. Finally, we support our results with numerical experiments of the developed approach application for power balancing in New England power system.
allfields_unstemmed	10.1007/s10287-024-00501-6 doi (DE-627)1898520674 (DE-599)KXP1898520674 DE-627 ger DE-627 rda eng Khamisov, Oleg O. verfasserin aut Distributed continuous-time optimization for convex problems with coupling linear inequality constraints Oleg O. Khamisov 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Constraints-coupled optimization (dpeaa)DE-206 Convex optimization (dpeaa)DE-206 Distributed optimization (dpeaa)DE-206 Multi-agent systems (dpeaa)DE-206 Enthalten in Computational management science Heidelberg : Springer, 2003 21(2024), 1 vom: Juni, Artikel-ID 21, Seite 1-20 (DE-627)363765263 (DE-600)2107564-5 (DE-576)276817729 1619-6988 nnns volume:21 year:2024 number:1 month:06 elocationid:21 pages:1-20 https://link.springer.com/content/pdf/10.1007/s10287-024-00501-6.pdf Verlag lizenzpflichtig https://doi.org/10.1007/s10287-024-00501-6 Resolving-System lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP 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_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_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 21 2024 1 6 21 1-20 26 01 0206 4565677857 x1z 13-08-24 26 00 DE-206 In this paper we propose a novel distributed continuous-time algorithm aimed to solve optimization problems with cost function being a sum of local strictly convex multidimensional functions associated to individual agents. Additionally, the problems can have coupled equality and inequality constraints. We prove global asymptotic convergence of the algorithm for a connected graph topology. In order to investigate its practical implementation, we analyze convergence when Euler method is applied to represent discrete-time communication. Finally, we support our results with numerical experiments of the developed approach application for power balancing in New England power system.
allfieldsGer	10.1007/s10287-024-00501-6 doi (DE-627)1898520674 (DE-599)KXP1898520674 DE-627 ger DE-627 rda eng Khamisov, Oleg O. verfasserin aut Distributed continuous-time optimization for convex problems with coupling linear inequality constraints Oleg O. Khamisov 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Constraints-coupled optimization (dpeaa)DE-206 Convex optimization (dpeaa)DE-206 Distributed optimization (dpeaa)DE-206 Multi-agent systems (dpeaa)DE-206 Enthalten in Computational management science Heidelberg : Springer, 2003 21(2024), 1 vom: Juni, Artikel-ID 21, Seite 1-20 (DE-627)363765263 (DE-600)2107564-5 (DE-576)276817729 1619-6988 nnns volume:21 year:2024 number:1 month:06 elocationid:21 pages:1-20 https://link.springer.com/content/pdf/10.1007/s10287-024-00501-6.pdf Verlag lizenzpflichtig https://doi.org/10.1007/s10287-024-00501-6 Resolving-System lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP 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_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_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 21 2024 1 6 21 1-20 26 01 0206 4565677857 x1z 13-08-24 26 00 DE-206 In this paper we propose a novel distributed continuous-time algorithm aimed to solve optimization problems with cost function being a sum of local strictly convex multidimensional functions associated to individual agents. Additionally, the problems can have coupled equality and inequality constraints. We prove global asymptotic convergence of the algorithm for a connected graph topology. In order to investigate its practical implementation, we analyze convergence when Euler method is applied to represent discrete-time communication. Finally, we support our results with numerical experiments of the developed approach application for power balancing in New England power system.
allfieldsSound	10.1007/s10287-024-00501-6 doi (DE-627)1898520674 (DE-599)KXP1898520674 DE-627 ger DE-627 rda eng Khamisov, Oleg O. verfasserin aut Distributed continuous-time optimization for convex problems with coupling linear inequality constraints Oleg O. Khamisov 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Constraints-coupled optimization (dpeaa)DE-206 Convex optimization (dpeaa)DE-206 Distributed optimization (dpeaa)DE-206 Multi-agent systems (dpeaa)DE-206 Enthalten in Computational management science Heidelberg : Springer, 2003 21(2024), 1 vom: Juni, Artikel-ID 21, Seite 1-20 (DE-627)363765263 (DE-600)2107564-5 (DE-576)276817729 1619-6988 nnns volume:21 year:2024 number:1 month:06 elocationid:21 pages:1-20 https://link.springer.com/content/pdf/10.1007/s10287-024-00501-6.pdf Verlag lizenzpflichtig https://doi.org/10.1007/s10287-024-00501-6 Resolving-System lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP 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_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_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 21 2024 1 6 21 1-20 26 01 0206 4565677857 x1z 13-08-24 26 00 DE-206 In this paper we propose a novel distributed continuous-time algorithm aimed to solve optimization problems with cost function being a sum of local strictly convex multidimensional functions associated to individual agents. Additionally, the problems can have coupled equality and inequality constraints. We prove global asymptotic convergence of the algorithm for a connected graph topology. In order to investigate its practical implementation, we analyze convergence when Euler method is applied to represent discrete-time communication. Finally, we support our results with numerical experiments of the developed approach application for power balancing in New England power system.
language	English
source	Enthalten in Computational management science 21(2024), 1 vom: Juni, Artikel-ID 21, Seite 1-20 volume:21 year:2024 number:1 month:06 elocationid:21 pages:1-20
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author	Khamisov, Oleg O.
spellingShingle	Khamisov, Oleg O. misc Constraints-coupled optimization misc Convex optimization misc Distributed optimization misc Multi-agent systems Distributed continuous-time optimization for convex problems with coupling linear inequality constraints
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topic_title	26 00 DE-206 In this paper we propose a novel distributed continuous-time algorithm aimed to solve optimization problems with cost function being a sum of local strictly convex multidimensional functions associated to individual agents. Additionally, the problems can have coupled equality and inequality constraints. We prove global asymptotic convergence of the algorithm for a connected graph topology. In order to investigate its practical implementation, we analyze convergence when Euler method is applied to represent discrete-time communication. Finally, we support our results with numerical experiments of the developed approach application for power balancing in New England power system Distributed continuous-time optimization for convex problems with coupling linear inequality constraints Oleg O. Khamisov Constraints-coupled optimization (dpeaa)DE-206 Convex optimization (dpeaa)DE-206 Distributed optimization (dpeaa)DE-206 Multi-agent systems (dpeaa)DE-206
topic	misc Constraints-coupled optimization misc Convex optimization misc Distributed optimization misc Multi-agent systems
topic_unstemmed	misc Constraints-coupled optimization misc Convex optimization misc Distributed optimization misc Multi-agent systems
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title	Distributed continuous-time optimization for convex problems with coupling linear inequality constraints
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title_full	Distributed continuous-time optimization for convex problems with coupling linear inequality constraints Oleg O. Khamisov
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title_sort	distributed continuous-time optimization for convex problems with coupling linear inequality constraints
title_auth	Distributed continuous-time optimization for convex problems with coupling linear inequality constraints
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title_short	Distributed continuous-time optimization for convex problems with coupling linear inequality constraints
url	https://link.springer.com/content/pdf/10.1007/s10287-024-00501-6.pdf https://doi.org/10.1007/s10287-024-00501-6
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code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4393</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">21</subfield><subfield code="j">2024</subfield><subfield code="e">1</subfield><subfield code="c">6</subfield><subfield code="i">21</subfield><subfield code="h">1-20</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">26</subfield><subfield code="1">01</subfield><subfield code="x">0206</subfield><subfield code="b">4565677857</subfield><subfield code="y">x1z</subfield><subfield code="z">13-08-24</subfield></datafield><datafield tag="982" ind1=" " ind2=" "><subfield code="2">26</subfield><subfield code="1">00</subfield><subfield code="x">DE-206</subfield><subfield code="b">In this paper we propose a novel distributed continuous-time algorithm aimed to solve optimization problems with cost function being a sum of local strictly convex multidimensional functions associated to individual agents. Additionally, the problems can have coupled equality and inequality constraints. We prove global asymptotic convergence of the algorithm for a connected graph topology. In order to investigate its practical implementation, we analyze convergence when Euler method is applied to represent discrete-time communication. Finally, we support our results with numerical experiments of the developed approach application for power balancing in New England power system.</subfield></datafield></record></collection>
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Distributed continuous-time optimization for convex problems with coupling linear inequality constraints

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