An adaptive penalty-like continuous-time algorithm to constrained distributed convex optimization
This paper considers a nonsmooth constrained distributed convex optimization over multi-agent systems. Each agent in the multi-agent system only has access to the information of its objective function and constraint, and cooperatively minimizes the global objective function, which is composed of the...
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| Vydáno v: | Journal of the Franklin Institute Ročník 359; číslo 8; s. 3692 - 3716 |
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| Hlavní autoři: | , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Elmsford
Elsevier Ltd
01.05.2022
Elsevier Science Ltd |
| Témata: | |
| ISSN: | 0016-0032, 1879-2693, 0016-0032 |
| On-line přístup: | Získat plný text |
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| Abstract | This paper considers a nonsmooth constrained distributed convex optimization over multi-agent systems. Each agent in the multi-agent system only has access to the information of its objective function and constraint, and cooperatively minimizes the global objective function, which is composed of the sum of local objective functions. A novel continuous-time algorithm is proposed to solve the distributed optimization problem and effectively characterize the appropriate gain of the penalty function. It should be noted that the proposed algorithm is based on an adaptive strategy to avoid introducing the primal-dual variables and estimating the related exact penalty parameters. Additional, it is demonstrated that the state solution of the proposed algorithm achieves consensus and converges to an optimal solution of the optimization problem. Finally, numerical simulations are given and the proposed algorithm is applied to solve the optimal placement problem and energy consumption problem. |
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| AbstractList | This paper considers a nonsmooth constrained distributed convex optimization over multi-agent systems. Each agent in the multi-agent system only has access to the information of its objective function and constraint, and cooperatively minimizes the global objective function, which is composed of the sum of local objective functions. A novel continuous-time algorithm is proposed to solve the distributed optimization problem and effectively characterize the appropriate gain of the penalty function. It should be noted that the proposed algorithm is based on an adaptive strategy to avoid introducing the primal-dual variables and estimating the related exact penalty parameters. Additional, it is demonstrated that the state solution of the proposed algorithm achieves consensus and converges to an optimal solution of the optimization problem. Finally, numerical simulations are given and the proposed algorithm is applied to solve the optimal placement problem and energy consumption problem. |
| Author | Qin, Sitian Jia, Wenwen |
| Author_xml | – sequence: 1 givenname: Wenwen surname: Jia fullname: Jia, Wenwen – sequence: 2 givenname: Sitian orcidid: 0000-0002-4543-4940 surname: Qin fullname: Qin, Sitian email: qinsitian@hitwh.edu.cn |
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| Cites_doi | 10.1109/TAC.2016.2628807 10.1109/TCST.2016.2517574 10.1109/TAC.2015.2504962 10.1109/TNNLS.2016.2549566 10.1016/j.neucom.2017.01.021 10.1109/TAC.2016.2610945 10.1016/j.neunet.2019.07.019 10.1016/j.sysconle.2015.06.006 10.1109/TPWRS.2005.857924 10.1109/TCSI.2008.920131 10.1016/j.sysconle.2018.12.005 10.1016/j.jfranklin.2019.01.050 10.1109/TAC.2017.2750103 10.1109/TAC.2018.2884998 10.1109/TAC.2013.2278132 10.1109/TAC.2017.2752001 10.1109/TAC.2019.2902612 10.1109/TCNS.2015.2399191 10.1016/j.jfranklin.2019.06.026 10.1016/j.jfranklin.2018.10.007 10.1109/TAC.2008.2009515 10.1109/TCYB.2018.2883095 10.1109/TAC.2012.2184199 10.1109/TSP.2014.2331615 10.1016/j.automatica.2019.04.004 10.1016/j.jfranklin.2016.07.009 10.1016/j.neucom.2019.10.050 10.1109/TNNLS.2017.2652478 10.1016/j.ijheatmasstransfer.2019.118910 |
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| References | Liu, Yang, Hong (bib0008) 2017; 67 A. Ahmadpour, Hajmohammadi (bib0005) 2021 Yi, Hong, Liu (bib0013) 2015; 83 Li, Ding, Sun, Li (bib0024) 2017; 63 Lu, Li, Xia (bib0009) 2017; 235 Lu, Tang (bib0029) 2012; 57 Yang, Liu, Wang (bib0039) 2018; 29 Chiang (bib0035) 2005; 20 Boyd, Vandenberghe (bib0040) 2004 Le, Chen, Yan, Xi (bib0001) 2017; 56 Li, Zhang, Wang, Zhu, Han (bib0010) 2019; 356 Clarke (bib0033) 1983 Zhou, Zeng, Hong (bib0020) 2019; 64 Jiang, Qin, Xue (bib0030) 2020; 337 W. Li, X. Zeng, S. Liang, Y. Hong, Exponentially convergent algorithm design for constrained distributed optimization via non-smooth approach, arXiv preprint arXiv:2001.00509(2020). Li, Ding, Sun, Li (bib0012) 2018; 63 E. Rasouli, Elmi (bib0006) 2020; 146 Nedic, Ozdaglar (bib0007) 2009; 54 Liang, Zeng, Hong (bib0019) 2018; 63 Cherukuri, Cortes (bib0027) 2015; 2 Yang, Liu, Wang (bib0014) 2017; 62 Lou, Hong, Xie, Shi, Johansson (bib0022) 2016; 61 Deng, Liang, Hong (bib0028) 2017; 11 Jia, Qin, Xue (bib0031) 2019; 119 Gu, Li, Wu (bib0036) 2019; 356 Gharesifard, Corts (bib0034) 2014; 59 Chen, Yang (bib0026) 2019; 124 Liu, Yang, Wang (bib0015) 2017; 28 Zhang, You, Basar (bib0021) 2019; 64 Xue, Bian (bib0042) 2008; 55 Yang, Zhang, Chen (bib0002) 2018; 356 Zhu, Yu, Wen, Chen (bib0017) 2020; 50 Arrow, Hurwicz, Uzawa (bib0011) 1958; 67 Knauer (bib0032) 1993 Towfic, Sayed (bib0023) 2014; 62 Shan, Lin (bib0003) 2018; 355 Aubin, Cellina (bib0038) 1984 Ye, Hu (bib0041) 2016; 24 Fu, He, Huang, Abu-Rub (bib0004) 2016; 353 Liang, Yin (bib0016) 2019; 105 Fiacco, McCormick (bib0018) 1963 Zeng, Yi, Hong (bib0037) 2017; 62 Gu (10.1016/j.jfranklin.2022.03.046_bib0036) 2019; 356 Jia (10.1016/j.jfranklin.2022.03.046_bib0031) 2019; 119 Knauer (10.1016/j.jfranklin.2022.03.046_bib0032) 1993 Yang (10.1016/j.jfranklin.2022.03.046_bib0014) 2017; 62 Liu (10.1016/j.jfranklin.2022.03.046_bib0015) 2017; 28 Lou (10.1016/j.jfranklin.2022.03.046_bib0022) 2016; 61 Zhu (10.1016/j.jfranklin.2022.03.046_bib0017) 2020; 50 Lu (10.1016/j.jfranklin.2022.03.046_bib0009) 2017; 235 Gharesifard (10.1016/j.jfranklin.2022.03.046_bib0034) 2014; 59 Li (10.1016/j.jfranklin.2022.03.046_bib0024) 2017; 63 Aubin (10.1016/j.jfranklin.2022.03.046_bib0038) 1984 Chiang (10.1016/j.jfranklin.2022.03.046_bib0035) 2005; 20 Cherukuri (10.1016/j.jfranklin.2022.03.046_bib0027) 2015; 2 Li (10.1016/j.jfranklin.2022.03.046_bib0010) 2019; 356 Zeng (10.1016/j.jfranklin.2022.03.046_bib0037) 2017; 62 Ye (10.1016/j.jfranklin.2022.03.046_bib0041) 2016; 24 Zhou (10.1016/j.jfranklin.2022.03.046_bib0020) 2019; 64 Deng (10.1016/j.jfranklin.2022.03.046_bib0028) 2017; 11 A. Ahmadpour (10.1016/j.jfranklin.2022.03.046_bib0005) 2021 Clarke (10.1016/j.jfranklin.2022.03.046_bib0033) 1983 Boyd (10.1016/j.jfranklin.2022.03.046_bib0040) 2004 Fu (10.1016/j.jfranklin.2022.03.046_bib0004) 2016; 353 Yang (10.1016/j.jfranklin.2022.03.046_bib0039) 2018; 29 Yang (10.1016/j.jfranklin.2022.03.046_bib0002) 2018; 356 Zhang (10.1016/j.jfranklin.2022.03.046_bib0021) 2019; 64 Jiang (10.1016/j.jfranklin.2022.03.046_bib0030) 2020; 337 Towfic (10.1016/j.jfranklin.2022.03.046_bib0023) 2014; 62 Xue (10.1016/j.jfranklin.2022.03.046_bib0042) 2008; 55 Arrow (10.1016/j.jfranklin.2022.03.046_bib0011) 1958; 67 Li (10.1016/j.jfranklin.2022.03.046_bib0012) 2018; 63 E. Rasouli (10.1016/j.jfranklin.2022.03.046_bib0006) 2020; 146 Nedic (10.1016/j.jfranklin.2022.03.046_bib0007) 2009; 54 10.1016/j.jfranklin.2022.03.046_bib0025 Liang (10.1016/j.jfranklin.2022.03.046_bib0019) 2018; 63 Chen (10.1016/j.jfranklin.2022.03.046_bib0026) 2019; 124 Liu (10.1016/j.jfranklin.2022.03.046_bib0008) 2017; 67 Fiacco (10.1016/j.jfranklin.2022.03.046_sbref0018) 1963 Le (10.1016/j.jfranklin.2022.03.046_bib0001) 2017; 56 Liang (10.1016/j.jfranklin.2022.03.046_bib0016) 2019; 105 Yi (10.1016/j.jfranklin.2022.03.046_bib0013) 2015; 83 Lu (10.1016/j.jfranklin.2022.03.046_bib0029) 2012; 57 Shan (10.1016/j.jfranklin.2022.03.046_bib0003) 2018; 355 |
| References_xml | – volume: 146 start-page: 118910 year: 2020 ident: bib0006 article-title: Geometric optimization of a highly conductive insert intruding an annular fin publication-title: Int. J. Heat Mass Transf. – volume: 64 start-page: 4661 year: 2019 end-page: 4667 ident: bib0020 article-title: Adaptive exact penalty design for constrained distributed optimization publication-title: IEEE Trans. Autom. Control – year: 1984 ident: bib0038 publication-title: Differential Inclusions – volume: 67 start-page: 1 year: 2017 end-page: 12 ident: bib0008 article-title: Constrained consensus algorithms with fixed step size for distributed convex optimization over multi-agent networks publication-title: IEEE Trans. Autom. Control – volume: 83 start-page: 45 year: 2015 end-page: 52 ident: bib0013 article-title: Distributed gradient algorithm for constrained optimization with application to load sharing in power systems publication-title: Syst. Control Lett. – year: 1983 ident: bib0033 publication-title: Optimization and Nonsmooth Analysis – volume: 54 start-page: 48 year: 2009 end-page: 61 ident: bib0007 article-title: Distributed subgradient methods for multi-agent optimization publication-title: IEEE Trans. Autom. Control – year: 1963 ident: bib0018 publication-title: Programming Under Nonlinear Constraints by Unconstrained Minimization: A Primal-Dual Method – volume: 337 start-page: 225 year: 2020 end-page: 233 ident: bib0030 article-title: A penalty-like neurodynamic approach to constrained nonsmooth distributed convex optimization publication-title: Neurocomputing – volume: 28 start-page: 1747 year: 2017 end-page: 1758 ident: bib0015 article-title: A collective neurodynamic approach to distributed constrained optimization publication-title: IEEE Trans. Neural Netw. Learn. Syst. – year: 1993 ident: bib0032 publication-title: Algebraic Graph Theory – year: 2004 ident: bib0040 publication-title: Convex Optimization – volume: 119 start-page: 46 year: 2019 end-page: 56 ident: bib0031 article-title: A generalized neural network for distributed nonsmooth optimization with inequality constraint publication-title: Neural Netw. – volume: 356 start-page: 7548 year: 2019 end-page: 7570 ident: bib0036 article-title: An adaptive online learning algorithm for distributed convex optimization with coupled constraints over unbalanced directed graphs publication-title: J. Frankl. Inst. – reference: W. Li, X. Zeng, S. Liang, Y. Hong, Exponentially convergent algorithm design for constrained distributed optimization via non-smooth approach, arXiv preprint arXiv:2001.00509(2020). – volume: 24 start-page: 2048 year: 2016 end-page: 2058 ident: bib0041 article-title: Distributed extremum seeking for constrained networked optimization and its application to energy consumption control in smart grid publication-title: IEEE Trans. Control Syst. Technol. – volume: 11 start-page: 1 year: 2017 end-page: 10 ident: bib0028 article-title: Distributed continuous-time algorithms for resource allocation problems over weight-balanced digraphs publication-title: IEEE Trans. Cybern. – volume: 59 start-page: 781 year: 2014 end-page: 786 ident: bib0034 article-title: Distributed continuous-time convex optimization on weight-balanced digraphs publication-title: IEEE Trans. Autom. Control – volume: 62 start-page: 5227 year: 2017 end-page: 5233 ident: bib0037 article-title: Distributed continuous-time algorithm for constrained convex optimizations via nonsmooth analysis approach publication-title: IEEE Trans. Autom. Control – volume: 56 start-page: 1 year: 2017 end-page: 10 ident: bib0001 article-title: A neurodynamic approach to distributed optimization with globally coupled constraints publication-title: IEEE Trans. Cybern. – volume: 356 start-page: 3733 year: 2019 end-page: 3761 ident: bib0010 article-title: Distributed consensus-based multi-agent convex optimization via gradient tracking technique publication-title: J. Frankl. Inst. – volume: 63 start-page: 1434 year: 2018 end-page: 1441 ident: bib0012 article-title: Distributed adaptive convex optimization on directed graphs via continuous-time algorithms publication-title: IEEE Trans. Autom. Control – volume: 235 start-page: 255 year: 2017 end-page: 263 ident: bib0009 article-title: Distributed optimization of first-order discrete-time multi-agent systems with event-triggered communication publication-title: Neurocomputing – volume: 355 start-page: 8957 year: 2018 end-page: 8970 ident: bib0003 article-title: Average-consensus tracking of multi-agent systems with additional interconnecting agents publication-title: J. Frankl. Inst. – volume: 105 start-page: 298 year: 2019 end-page: 306 ident: bib0016 article-title: Exponential convergence of distributed primal–dual convex optimization algorithm without strong convexity publication-title: Automatica – volume: 20 start-page: 1690 year: 2005 end-page: 1699 ident: bib0035 article-title: Improved genetic algorithm for power economic dispatch of units with valve-point effects and multiple fuels publication-title: IEEE Trans. Power Syst. – volume: 62 start-page: 3924 year: 2014 end-page: 3938 ident: bib0023 article-title: Adaptive penalty-based distributed stochastic convex optimization publication-title: IEEE Trans. Signal Process. – volume: 64 start-page: 2352 year: 2019 end-page: 2367 ident: bib0021 article-title: Distributed discrete-time optimization in multiagent networks using only sign of relative state publication-title: IEEE Trans. Autom. Control – volume: 63 start-page: 1753 year: 2018 end-page: 1759 ident: bib0019 article-title: Distributed nonsmooth optimization with coupled inequality constraints via modified lagrangian function publication-title: IEEE Trans. Autom. Control – volume: 67 year: 1958 ident: bib0011 article-title: Studies in linear and non-linear programming publication-title: Am. Math. Mon. – volume: 124 start-page: 60 year: 2019 end-page: 67 ident: bib0026 article-title: Distributed constrained optimization for multi-agent networks with nonsmooth objective functions publication-title: Syst. Control Lett. – volume: 63 start-page: 1434 year: 2017 end-page: 1441 ident: bib0024 article-title: Distributed adaptive convex optimization on directed graphs via continuous-time algorithms publication-title: IEEE Trans. Autom. Control – volume: 57 start-page: 2348 year: 2012 end-page: 2354 ident: bib0029 article-title: Zero-gradient-sum algorithms for distributed convex optimization: the continuous-time case publication-title: IEEE Trans. Autom. Control – volume: 356 start-page: 209 year: 2018 end-page: 236 ident: bib0002 article-title: Adaptive distributed convex optimization for multi-agent and its application in flocking behavior publication-title: J. Frankl. Inst. – start-page: 1 year: 2021 end-page: 15 ident: bib0005 article-title: Performance evaluation and optimization of flattened microchannel heat sinks for the electronic cooling application publication-title: J. Therm. Anal. Calorim. – volume: 29 start-page: 981 year: 2018 end-page: 992 ident: bib0039 article-title: A collaborative neurodynamic approach to multiple-objective distributed optimization publication-title: IEEE Trans. Neural Netw. Learn. Syst. – volume: 2 start-page: 226 year: 2015 end-page: 237 ident: bib0027 article-title: Distributed generator coordination for initialization and anytime optimization in economic dispatch publication-title: IEEE Trans. Control Netw. Syst. – volume: 55 start-page: 2378 year: 2008 end-page: 2391 ident: bib0042 article-title: Subgradient-based neural networks for nonsmooth convex optimization problems publication-title: IEEE Trans. Circuits Syst. I Regul. Pap. – volume: 353 start-page: 3966 year: 2016 end-page: 3984 ident: bib0004 article-title: A distributed continuous time consensus algorithm for maximize social welfare in micro grid publication-title: J. Frankl. Inst. – volume: 62 start-page: 3461 year: 2017 end-page: 3467 ident: bib0014 article-title: A multi-agent system with a proportional-integral protocol for distributed constrained optimization publication-title: IEEE Trans. Autom. Control – volume: 61 start-page: 2920 year: 2016 end-page: 2935 ident: bib0022 article-title: Nash equilibrium computation in subnetwork zero-sum games with switching communications publication-title: IEEE Trans. Autom. Control – volume: 50 start-page: 191 year: 2020 end-page: 213 ident: bib0017 article-title: Projected primal-dual dynamics for distributed constrained nonsmooth convex optimization publication-title: IEEE Trans. Cybern. – volume: 62 start-page: 5227 issue: 10 year: 2017 ident: 10.1016/j.jfranklin.2022.03.046_bib0037 article-title: Distributed continuous-time algorithm for constrained convex optimizations via nonsmooth analysis approach publication-title: IEEE Trans. Autom. Control doi: 10.1109/TAC.2016.2628807 – volume: 24 start-page: 2048 issue: 6 year: 2016 ident: 10.1016/j.jfranklin.2022.03.046_bib0041 article-title: Distributed extremum seeking for constrained networked optimization and its application to energy consumption control in smart grid publication-title: IEEE Trans. Control Syst. Technol. doi: 10.1109/TCST.2016.2517574 – volume: 61 start-page: 2920 issue: 10 year: 2016 ident: 10.1016/j.jfranklin.2022.03.046_bib0022 article-title: Nash equilibrium computation in subnetwork zero-sum games with switching communications publication-title: IEEE Trans. Autom. Control doi: 10.1109/TAC.2015.2504962 – volume: 28 start-page: 1747 issue: 8 year: 2017 ident: 10.1016/j.jfranklin.2022.03.046_bib0015 article-title: A collective neurodynamic approach to distributed constrained optimization publication-title: IEEE Trans. Neural Netw. Learn. Syst. doi: 10.1109/TNNLS.2016.2549566 – volume: 235 start-page: 255 year: 2017 ident: 10.1016/j.jfranklin.2022.03.046_bib0009 article-title: Distributed optimization of first-order discrete-time multi-agent systems with event-triggered communication publication-title: Neurocomputing doi: 10.1016/j.neucom.2017.01.021 – volume: 62 start-page: 3461 issue: 7 year: 2017 ident: 10.1016/j.jfranklin.2022.03.046_bib0014 article-title: A multi-agent system with a proportional-integral protocol for distributed constrained optimization publication-title: IEEE Trans. Autom. Control doi: 10.1109/TAC.2016.2610945 – year: 2004 ident: 10.1016/j.jfranklin.2022.03.046_bib0040 – volume: 67 issue: 2 year: 1958 ident: 10.1016/j.jfranklin.2022.03.046_bib0011 article-title: Studies in linear and non-linear programming publication-title: Am. Math. Mon. – volume: 119 start-page: 46 year: 2019 ident: 10.1016/j.jfranklin.2022.03.046_bib0031 article-title: A generalized neural network for distributed nonsmooth optimization with inequality constraint publication-title: Neural Netw. doi: 10.1016/j.neunet.2019.07.019 – volume: 56 start-page: 1 issue: 99 year: 2017 ident: 10.1016/j.jfranklin.2022.03.046_bib0001 article-title: A neurodynamic approach to distributed optimization with globally coupled constraints publication-title: IEEE Trans. Cybern. – volume: 83 start-page: 45 issue: 711 year: 2015 ident: 10.1016/j.jfranklin.2022.03.046_bib0013 article-title: Distributed gradient algorithm for constrained optimization with application to load sharing in power systems publication-title: Syst. Control Lett. doi: 10.1016/j.sysconle.2015.06.006 – volume: 20 start-page: 1690 issue: 4 year: 2005 ident: 10.1016/j.jfranklin.2022.03.046_bib0035 article-title: Improved genetic algorithm for power economic dispatch of units with valve-point effects and multiple fuels publication-title: IEEE Trans. Power Syst. doi: 10.1109/TPWRS.2005.857924 – volume: 55 start-page: 2378 issue: 8 year: 2008 ident: 10.1016/j.jfranklin.2022.03.046_bib0042 article-title: Subgradient-based neural networks for nonsmooth convex optimization problems publication-title: IEEE Trans. Circuits Syst. I Regul. Pap. doi: 10.1109/TCSI.2008.920131 – volume: 356 start-page: 209 issue: 2 year: 2018 ident: 10.1016/j.jfranklin.2022.03.046_bib0002 article-title: Adaptive distributed convex optimization for multi-agent and its application in flocking behavior publication-title: J. Frankl. Inst. – volume: 124 start-page: 60 year: 2019 ident: 10.1016/j.jfranklin.2022.03.046_bib0026 article-title: Distributed constrained optimization for multi-agent networks with nonsmooth objective functions publication-title: Syst. Control Lett. doi: 10.1016/j.sysconle.2018.12.005 – volume: 356 start-page: 3733 issue: 6 year: 2019 ident: 10.1016/j.jfranklin.2022.03.046_bib0010 article-title: Distributed consensus-based multi-agent convex optimization via gradient tracking technique publication-title: J. Frankl. Inst. doi: 10.1016/j.jfranklin.2019.01.050 – volume: 63 start-page: 1434 issue: 5 year: 2018 ident: 10.1016/j.jfranklin.2022.03.046_bib0012 article-title: Distributed adaptive convex optimization on directed graphs via continuous-time algorithms publication-title: IEEE Trans. Autom. Control doi: 10.1109/TAC.2017.2750103 – year: 1983 ident: 10.1016/j.jfranklin.2022.03.046_bib0033 – volume: 64 start-page: 2352 issue: 6 year: 2019 ident: 10.1016/j.jfranklin.2022.03.046_bib0021 article-title: Distributed discrete-time optimization in multiagent networks using only sign of relative state publication-title: IEEE Trans. Autom. Control doi: 10.1109/TAC.2018.2884998 – year: 1993 ident: 10.1016/j.jfranklin.2022.03.046_bib0032 – volume: 59 start-page: 781 issue: 3 year: 2014 ident: 10.1016/j.jfranklin.2022.03.046_bib0034 article-title: Distributed continuous-time convex optimization on weight-balanced digraphs publication-title: IEEE Trans. Autom. Control doi: 10.1109/TAC.2013.2278132 – volume: 63 start-page: 1753 issue: 6 year: 2018 ident: 10.1016/j.jfranklin.2022.03.046_bib0019 article-title: Distributed nonsmooth optimization with coupled inequality constraints via modified lagrangian function publication-title: IEEE Trans. Autom. Control doi: 10.1109/TAC.2017.2752001 – volume: 64 start-page: 4661 issue: 11 year: 2019 ident: 10.1016/j.jfranklin.2022.03.046_bib0020 article-title: Adaptive exact penalty design for constrained distributed optimization publication-title: IEEE Trans. Autom. Control doi: 10.1109/TAC.2019.2902612 – ident: 10.1016/j.jfranklin.2022.03.046_bib0025 – volume: 2 start-page: 226 issue: 3 year: 2015 ident: 10.1016/j.jfranklin.2022.03.046_bib0027 article-title: Distributed generator coordination for initialization and anytime optimization in economic dispatch publication-title: IEEE Trans. Control Netw. Syst. doi: 10.1109/TCNS.2015.2399191 – year: 1984 ident: 10.1016/j.jfranklin.2022.03.046_bib0038 – volume: 356 start-page: 7548 issue: 13 year: 2019 ident: 10.1016/j.jfranklin.2022.03.046_bib0036 article-title: An adaptive online learning algorithm for distributed convex optimization with coupled constraints over unbalanced directed graphs publication-title: J. Frankl. Inst. doi: 10.1016/j.jfranklin.2019.06.026 – volume: 355 start-page: 8957 issue: 17 year: 2018 ident: 10.1016/j.jfranklin.2022.03.046_bib0003 article-title: Average-consensus tracking of multi-agent systems with additional interconnecting agents publication-title: J. Frankl. Inst. doi: 10.1016/j.jfranklin.2018.10.007 – start-page: 1 year: 2021 ident: 10.1016/j.jfranklin.2022.03.046_bib0005 article-title: Performance evaluation and optimization of flattened microchannel heat sinks for the electronic cooling application publication-title: J. Therm. Anal. Calorim. – volume: 54 start-page: 48 issue: 1 year: 2009 ident: 10.1016/j.jfranklin.2022.03.046_bib0007 article-title: Distributed subgradient methods for multi-agent optimization publication-title: IEEE Trans. Autom. Control doi: 10.1109/TAC.2008.2009515 – volume: 63 start-page: 1434 issue: 5 year: 2017 ident: 10.1016/j.jfranklin.2022.03.046_bib0024 article-title: Distributed adaptive convex optimization on directed graphs via continuous-time algorithms publication-title: IEEE Trans. Autom. Control doi: 10.1109/TAC.2017.2750103 – volume: 50 start-page: 191 issue: 4 year: 2020 ident: 10.1016/j.jfranklin.2022.03.046_bib0017 article-title: Projected primal-dual dynamics for distributed constrained nonsmooth convex optimization publication-title: IEEE Trans. Cybern. doi: 10.1109/TCYB.2018.2883095 – volume: 57 start-page: 2348 issue: 9 year: 2012 ident: 10.1016/j.jfranklin.2022.03.046_bib0029 article-title: Zero-gradient-sum algorithms for distributed convex optimization: the continuous-time case publication-title: IEEE Trans. Autom. Control doi: 10.1109/TAC.2012.2184199 – volume: 11 start-page: 1 issue: 99 year: 2017 ident: 10.1016/j.jfranklin.2022.03.046_bib0028 article-title: Distributed continuous-time algorithms for resource allocation problems over weight-balanced digraphs publication-title: IEEE Trans. Cybern. – volume: 62 start-page: 3924 issue: 15 year: 2014 ident: 10.1016/j.jfranklin.2022.03.046_bib0023 article-title: Adaptive penalty-based distributed stochastic convex optimization publication-title: IEEE Trans. Signal Process. doi: 10.1109/TSP.2014.2331615 – volume: 105 start-page: 298 year: 2019 ident: 10.1016/j.jfranklin.2022.03.046_bib0016 article-title: Exponential convergence of distributed primal–dual convex optimization algorithm without strong convexity publication-title: Automatica doi: 10.1016/j.automatica.2019.04.004 – volume: 353 start-page: 3966 issue: 15 year: 2016 ident: 10.1016/j.jfranklin.2022.03.046_bib0004 article-title: A distributed continuous time consensus algorithm for maximize social welfare in micro grid publication-title: J. Frankl. Inst. doi: 10.1016/j.jfranklin.2016.07.009 – year: 1963 ident: 10.1016/j.jfranklin.2022.03.046_sbref0018 – volume: 337 start-page: 225 issue: 6 year: 2020 ident: 10.1016/j.jfranklin.2022.03.046_bib0030 article-title: A penalty-like neurodynamic approach to constrained nonsmooth distributed convex optimization publication-title: Neurocomputing doi: 10.1016/j.neucom.2019.10.050 – volume: 29 start-page: 981 issue: 4 year: 2018 ident: 10.1016/j.jfranklin.2022.03.046_bib0039 article-title: A collaborative neurodynamic approach to multiple-objective distributed optimization publication-title: IEEE Trans. Neural Netw. Learn. Syst. doi: 10.1109/TNNLS.2017.2652478 – volume: 67 start-page: 1 issue: 99 year: 2017 ident: 10.1016/j.jfranklin.2022.03.046_bib0008 article-title: Constrained consensus algorithms with fixed step size for distributed convex optimization over multi-agent networks publication-title: IEEE Trans. Autom. Control – volume: 146 start-page: 118910 year: 2020 ident: 10.1016/j.jfranklin.2022.03.046_bib0006 article-title: Geometric optimization of a highly conductive insert intruding an annular fin publication-title: Int. J. Heat Mass Transf. doi: 10.1016/j.ijheatmasstransfer.2019.118910 |
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| SubjectTerms | Adaptive algorithms Algorithms Computational geometry Constraints Continuity (mathematics) Convex analysis Convexity Energy consumption Multiagent systems Numerical analysis Optimization Penalty function |
| Title | An adaptive penalty-like continuous-time algorithm to constrained distributed convex optimization |
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