Cluster-based distributed augmented Lagrangian algorithm for a class of constrained convex optimization problems
We propose a distributed solution for a constrained convex optimization problem over a network of clustered agents each consisted of a set of subagents. The communication range of the clustered agents is such that they can form a connected undirected graph topology. The total cost in this optimizati...
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| Published in: | Automatica (Oxford) Vol. 129; p. 109608 |
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| Format: | Journal Article |
| Language: | English |
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01.07.2021
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| ISSN: | 0005-1098, 1873-2836 |
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| Abstract | We propose a distributed solution for a constrained convex optimization problem over a network of clustered agents each consisted of a set of subagents. The communication range of the clustered agents is such that they can form a connected undirected graph topology. The total cost in this optimization problem is the sum of the local convex costs of the subagents of each cluster. We seek a minimizer of this cost subject to a set of affine equality constraints, and a set of affine inequality constraints specifying the bounds on the decision variables if such bounds exist. We design our distributed algorithm in a cluster-based framework which results in a significant reduction in communication and computation costs. Our proposed distributed solution is a novel continuous-time algorithm that is linked to the augmented Lagrangian approach. It converges asymptotically when the local cost functions are convex and exponentially when they are strongly convex and have Lipschitz gradients. Moreover, we use an ϵ-exact penalty function to address the inequality constraints and derive an explicit lower bound on the penalty function weight to guarantee convergence to ϵ-neighborhood of the global minimum value of the cost. A numerical example demonstrates our results. |
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| AbstractList | We propose a distributed solution for a constrained convex optimization problem over a network of clustered agents each consisted of a set of subagents. The communication range of the clustered agents is such that they can form a connected undirected graph topology. The total cost in this optimization problem is the sum of the local convex costs of the subagents of each cluster. We seek a minimizer of this cost subject to a set of affine equality constraints, and a set of affine inequality constraints specifying the bounds on the decision variables if such bounds exist. We design our distributed algorithm in a cluster-based framework which results in a significant reduction in communication and computation costs. Our proposed distributed solution is a novel continuous-time algorithm that is linked to the augmented Lagrangian approach. It converges asymptotically when the local cost functions are convex and exponentially when they are strongly convex and have Lipschitz gradients. Moreover, we use an ϵ-exact penalty function to address the inequality constraints and derive an explicit lower bound on the penalty function weight to guarantee convergence to ϵ-neighborhood of the global minimum value of the cost. A numerical example demonstrates our results. |
| ArticleNumber | 109608 |
| Author | Kia, Solmaz S. Moradian, Hossein |
| Author_xml | – sequence: 1 givenname: Hossein surname: Moradian fullname: Moradian, Hossein email: hmoradia@uci.edu – sequence: 2 givenname: Solmaz S. surname: Kia fullname: Kia, Solmaz S. email: solmaz@uci.edu |
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| Cites_doi | 10.1016/j.automatica.2016.07.003 10.1561/2200000016 10.1109/TNET.2013.2251896 10.1109/TWC.2006.1687734 10.1016/0167-6377(85)90030-6 10.1109/TAC.2015.2449811 10.1016/j.sysconle.2018.10.007 10.1109/TCOMM.2004.831346 10.1016/j.ifacol.2018.12.040 10.1109/TPWRS.2012.2188912 10.1109/TAC.2013.2275667 10.1109/TAC.2014.2363299 10.1109/PESGM.2012.6345156 10.1109/CDC.2011.6160931 10.1109/TAC.2011.2161027 10.23919/ACC.2017.7963458 10.1109/CDC.2018.8619343 10.1016/j.automatica.2015.03.001 10.1016/j.ifacol.2016.05.003 10.1109/CDC.2011.6161503 10.1109/TCNS.2015.2399191 10.1007/BFb0120696 10.1109/CDC.2018.8619760 10.1016/j.sysconle.2017.07.012 10.23919/ACC.2018.8431779 10.1109/CDC.2017.8264654 10.1109/CDC.2018.8619651 10.1016/j.orl.2012.11.009 10.1007/s10957-006-9080-1 10.1016/j.automatica.2016.08.007 10.1109/TSP.2011.2169407 10.1109/CDC.2018.8619512 10.1109/TSG.2017.2684183 10.1109/CDC.2012.6426665 |
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| Keywords | Distributed constrained convex optimization Optimal resource allocation Penalty function methods Augmented Lagrangian Primal–dual solutions |
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| References | Wang, J., & Elia, N. (2011). A control perspective for centralized and distributed convex optimization. In Bullo, Cortés, Martínez (b8) 2009 Kia (b23) 2017; 107 Cherukuri, Cortés (b11) 2016; 74 Kar, S., & Hug, G. (2012). Distributed robust economic dispatch in power systems: A consensus + innovations approach. In Zhang, Y., & Zavlanos, M. M. (2018). A consensus-based distributed augmented Lagrangian method. In FL, USA. WA, USA. Boyd, Parikh, Chu, Peleato, Eckstein (b6) 2010; 3 (pp. 3688–3693). Hawaii, USA. Wood, Wollenberg, Sheble (b39) 2013 Xiao, Johansson, Boyd (b41) 2004; 52 CA, USA. Richter, S., Morari, M., & Jones, C. (2011). Towards computational complexity certification for constrained MPC based on Lagrange relaxation and the fast gradient method. In Cherukuri, Cortés (b12) 2016; 74 Alghunaim, S. A., Yuan, K., & Sayed, A. H. (2018). Dual coupled diffusion for distributed optimization with affine constraints. In Cherukuri, Cortés (b10) 2015; 2 Vaquero, Cortes (b34) 2018; 51 (pp. 1–8). San Diego, CA. Kia, S. S. (2016). Distributed optimal resource allocation over networked systems and use of an epsilon-exact penalty function. In Zhang, Chow (b43) 2012; 27 Madan, Lall (b25) 2006; 5 Wei, Wang, Li, Mei (b38) 2017; 8 Arrow, Hurwicz, Uzawa (b2) 1958 Srivastava, P., & Cortes, J. (2018). Distributed algorithm via continuously differentiable exact penalty method for network optimization. In Jakovetic, Moura, Xavier (b19) 2015; 60 Chen, Lau (b9) 2012; 60 Ding, D., Hu, B., Dhingra, N., & Jovanovic, M. (2018). An exponentially convergent primal-dual algorithm for nonsmooth composite minimization. In Ding, D., & Jovanovic, M. (2018). A primal-dual Laplacian gradient flow dynamics for distributed resource allocation problems. In Bertsekas (b4) 1999 Zholbaryssov, Fooladivanda, Domínguez-García (b45) 2019; 123 Boyd, Vandenberghe (b7) 2004 Bertsekas, Tsitsiklis (b5) 1997 Moradian, Kia (b28) 2020 Haddad, Chellaboina (b18) 2008 (pp. 5223–5229). Orlando, Florida, USA. Bertsekas (b3) 1975; 3 Xiao, Boyd (b40) 2006; 129 Varagnolo, Zanella, Cenedese, Pillonetto, Schenato (b35) 2015; 61 Kia, S. S. (2017a). An augmented Lagrangian distributed algorithm for an in-network optimal resource allocation problem. In Mangasarian, Fromovitz (b27) 1967; 17 Duchi, Agarwal, Wainwright (b16) 2012; 57 WI, USA. Pinar, Zenios (b30) 1994; 4 Yi, Hong, Liu (b42) 2016; 74 Mangasarian (b26) 1985; 4 Patrinos, Bemporad (b29) 2014 Melbourne, Australia. Wachsmuth (b36) 2013; 41 Dominguez-Garcia, A. D., Cady, S. T., & Hadjicostis, C. N. (2012). Decentralized optimal dispatch of distributed energy resources. In Ferragut, Paganini (b17) 2014; 22 Kia, Cortés, Martínez (b24) 2014; 55 Rostami, R., Costantini, G., & Görges, D. (2017). ADMM-based distributed model predictive control: Primal and dual approaches. In Haddad (10.1016/j.automatica.2021.109608_b18) 2008 Madan (10.1016/j.automatica.2021.109608_b25) 2006; 5 Wachsmuth (10.1016/j.automatica.2021.109608_b36) 2013; 41 Bertsekas (10.1016/j.automatica.2021.109608_b3) 1975; 3 10.1016/j.automatica.2021.109608_b14 10.1016/j.automatica.2021.109608_b13 10.1016/j.automatica.2021.109608_b33 Mangasarian (10.1016/j.automatica.2021.109608_b26) 1985; 4 Pinar (10.1016/j.automatica.2021.109608_b30) 1994; 4 Arrow (10.1016/j.automatica.2021.109608_b2) 1958 Xiao (10.1016/j.automatica.2021.109608_b41) 2004; 52 Yi (10.1016/j.automatica.2021.109608_b42) 2016; 74 10.1016/j.automatica.2021.109608_b15 10.1016/j.automatica.2021.109608_b37 Cherukuri (10.1016/j.automatica.2021.109608_b11) 2016; 74 Cherukuri (10.1016/j.automatica.2021.109608_b10) 2015; 2 10.1016/j.automatica.2021.109608_b21 10.1016/j.automatica.2021.109608_b20 Boyd (10.1016/j.automatica.2021.109608_b7) 2004 Bertsekas (10.1016/j.automatica.2021.109608_b4) 1999 Boyd (10.1016/j.automatica.2021.109608_b6) 2010; 3 Kia (10.1016/j.automatica.2021.109608_b23) 2017; 107 Zhang (10.1016/j.automatica.2021.109608_b43) 2012; 27 Wei (10.1016/j.automatica.2021.109608_b38) 2017; 8 Moradian (10.1016/j.automatica.2021.109608_b28) 2020 Ferragut (10.1016/j.automatica.2021.109608_b17) 2014; 22 Duchi (10.1016/j.automatica.2021.109608_b16) 2012; 57 Wood (10.1016/j.automatica.2021.109608_b39) 2013 Bullo (10.1016/j.automatica.2021.109608_b8) 2009 10.1016/j.automatica.2021.109608_b22 Patrinos (10.1016/j.automatica.2021.109608_b29) 2014 10.1016/j.automatica.2021.109608_b44 10.1016/j.automatica.2021.109608_b1 Varagnolo (10.1016/j.automatica.2021.109608_b35) 2015; 61 Chen (10.1016/j.automatica.2021.109608_b9) 2012; 60 Cherukuri (10.1016/j.automatica.2021.109608_b12) 2016; 74 10.1016/j.automatica.2021.109608_b32 10.1016/j.automatica.2021.109608_b31 Xiao (10.1016/j.automatica.2021.109608_b40) 2006; 129 Mangasarian (10.1016/j.automatica.2021.109608_b27) 1967; 17 Bertsekas (10.1016/j.automatica.2021.109608_b5) 1997 Vaquero (10.1016/j.automatica.2021.109608_b34) 2018; 51 Zholbaryssov (10.1016/j.automatica.2021.109608_b45) 2019; 123 Jakovetic (10.1016/j.automatica.2021.109608_b19) 2015; 60 Kia (10.1016/j.automatica.2021.109608_b24) 2014; 55 |
| References_xml | – volume: 60 start-page: 443 year: 2012 end-page: 452 ident: b9 article-title: Convergence analysis of saddle point problems in time varying wireless systems – control theoretical approach publication-title: IEEE Transactions on Signal Processing – volume: 4 start-page: 1757 year: 1985 end-page: 1780 ident: b26 article-title: Computable numerical bounds for LAGRANGE multipliers of stationary points of non-convex differentiable non-linear programs publication-title: Operations Research Letters – volume: 8 start-page: 2974 year: 2017 end-page: 2987 ident: b38 article-title: Optimal power flow of radial networks and its variations: A sequential convex optimization approach publication-title: IEEE Transactions on Smart Grid – year: 2009 ident: b8 publication-title: Distributed control of robotic networks – volume: 57 start-page: 592 year: 2012 end-page: 606 ident: b16 article-title: Dual averaging for distributed optimization: Convergence analysis and network scaling publication-title: IEEE Transactions on Automatic Control – year: 2020 ident: b28 article-title: Cluster-based distributed augmented Lagrangian algorithm for a class of constrained convex optimization problems – year: 1997 ident: b5 article-title: Parallel and distributed computation: Numerical methods – reference: . WI, USA. – volume: 22 start-page: 349 year: 2014 end-page: 362 ident: b17 article-title: Network resource allocation for users with multiple connections: fairness and stability publication-title: IEEE/ACM Transactions on Networking – reference: . FL, USA. – reference: Dominguez-Garcia, A. D., Cady, S. T., & Hadjicostis, C. N. (2012). Decentralized optimal dispatch of distributed energy resources. In – volume: 17 start-page: 37 year: 1967 end-page: 47 ident: b27 article-title: The Fritz John necessary optimality conditions in the presence of equality and inequality constraints publication-title: Operations Research Letters – volume: 61 start-page: 994 year: 2015 end-page: 1009 ident: b35 article-title: Newton-Raphson consensus for distributed convex optimization publication-title: IEEE Transactions on Automatic Control – volume: 123 start-page: 47 year: 2019 end-page: 54 ident: b45 article-title: Resilient distributed optimal generation dispatch for lossy ac microgrids publication-title: Systems & Control Letters – reference: (pp. 1–8). San Diego, CA. – reference: . WA, USA. – reference: Zhang, Y., & Zavlanos, M. M. (2018). A consensus-based distributed augmented Lagrangian method. In – reference: Ding, D., & Jovanovic, M. (2018). A primal-dual Laplacian gradient flow dynamics for distributed resource allocation problems. In – volume: 55 start-page: 254 year: 2014 end-page: 264 ident: b24 article-title: Distributed convex optimization via continuous-time coordination algorithms with discrete-time communication publication-title: Automatica – reference: (pp. 5223–5229). Orlando, Florida, USA. – reference: Kia, S. S. (2016). Distributed optimal resource allocation over networked systems and use of an epsilon-exact penalty function. In – reference: Alghunaim, S. A., Yuan, K., & Sayed, A. H. (2018). Dual coupled diffusion for distributed optimization with affine constraints. In – volume: 51 start-page: 230 year: 2018 end-page: 235 ident: b34 article-title: Distributed augmentation-regularization for robust online convex optimization publication-title: IFAC-PapersOnLine – volume: 74 start-page: 259 year: 2016 end-page: 269 ident: b42 article-title: Initialization-free distributed algorithms for optimal resource allocation with feasibility constraints and its application to economic dispatch of power systems publication-title: Automatica – volume: 3 start-page: 1 year: 1975 end-page: 25 ident: b3 article-title: Nondifferentiable optimization via approximation publication-title: Mathematical Programing Study – volume: 2 start-page: 226 year: 2015 end-page: 237 ident: b10 article-title: Distributed generator coordination for initialization and anytime optimization in economic dispatch publication-title: IEEE Transactions on Control of Network Systems – volume: 60 start-page: 922 year: 2015 end-page: 936 ident: b19 article-title: Linear convergence rate of a class of distributed augmented Lagrangian algorithms publication-title: IEEE Transactions on Automatic Control – year: 2004 ident: b7 article-title: Convex optimization – reference: Kia, S. S. (2017a). An augmented Lagrangian distributed algorithm for an in-network optimal resource allocation problem. In – volume: 3 start-page: 1 year: 2010 end-page: 122 ident: b6 article-title: Distributed optimization and statistical learning via the alternating direction method of multipliers publication-title: Foundations and Trends in Machine Learning – reference: Ding, D., Hu, B., Dhingra, N., & Jovanovic, M. (2018). An exponentially convergent primal-dual algorithm for nonsmooth composite minimization. In – reference: Richter, S., Morari, M., & Jones, C. (2011). Towards computational complexity certification for constrained MPC based on Lagrange relaxation and the fast gradient method. In – year: 1958 ident: b2 article-title: Studies in linear and nonlinear programming – year: 1999 ident: b4 article-title: Nonlinear programming – reference: Wang, J., & Elia, N. (2011). A control perspective for centralized and distributed convex optimization. In – volume: 74 start-page: 183 year: 2016 end-page: 193 ident: b12 article-title: Initialization-free distributed coordination for economic dispatch under varying loads and generator commitment publication-title: Automatica – year: 2013 ident: b39 article-title: Power generation, operation and control – volume: 27 start-page: 1761 year: 2012 end-page: 1768 ident: b43 article-title: Convergence analysis of the incremental cost consensus algorithm under different communication network topologies in a smart grid publication-title: IEEE Transactions on Power Systems – volume: 74 start-page: 183 year: 2016 end-page: 193 ident: b11 article-title: Initialization-free distributed coordination for economic dispatch under varying loads and generator commitment publication-title: Automatica – reference: (pp. 3688–3693). Hawaii, USA. – volume: 129 start-page: 469 year: 2006 end-page: 488 ident: b40 article-title: Optimal scaling of a gradient method for distributed resource allocation publication-title: Journal of Optimization Theory and Applications – reference: . Melbourne, Australia. – volume: 41 start-page: 78 year: 2013 end-page: 80 ident: b36 article-title: On LICQ and the uniqueness of Lagrange multipliers publication-title: Operations Research Letters – reference: Kar, S., & Hug, G. (2012). Distributed robust economic dispatch in power systems: A consensus + innovations approach. In – volume: 107 start-page: 49 year: 2017 end-page: 57 ident: b23 article-title: Distributed optimal in-network resource allocation algorithm design via a control theoretic approach publication-title: Systems & Control Letters – volume: 52 start-page: 1136 year: 2004 end-page: 1144 ident: b41 article-title: Simultaneous routing and resource allocation via dual decomposition publication-title: IEEE Transactions on Communications – volume: 5 start-page: 2185 year: 2006 end-page: 2193 ident: b25 article-title: Distributed algorithms for maximum lifetime routing in wireless sensor networks publication-title: IEEE Transactions on Wireless Communication – reference: Rostami, R., Costantini, G., & Görges, D. (2017). ADMM-based distributed model predictive control: Primal and dual approaches. In – volume: 4 start-page: 1136 year: 1994 end-page: 1144 ident: b30 article-title: On smoothing exact penalty functions for convex constrained optimization publication-title: IEEE Transactions on Communications – year: 2014 ident: b29 article-title: An accelerated dual gradient-projection algorithm for embedded linear model predictive control publication-title: IEEE Transactions on Automatic Control – reference: Srivastava, P., & Cortes, J. (2018). Distributed algorithm via continuously differentiable exact penalty method for network optimization. In – reference: . CA, USA. – year: 2008 ident: b18 article-title: Nonlinear dynamical systems and control – year: 1999 ident: 10.1016/j.automatica.2021.109608_b4 – volume: 74 start-page: 183 year: 2016 ident: 10.1016/j.automatica.2021.109608_b12 article-title: Initialization-free distributed coordination for economic dispatch under varying loads and generator commitment publication-title: Automatica doi: 10.1016/j.automatica.2016.07.003 – volume: 3 start-page: 1 year: 2010 ident: 10.1016/j.automatica.2021.109608_b6 article-title: Distributed optimization and statistical learning via the alternating direction method of multipliers publication-title: Foundations and Trends in Machine Learning doi: 10.1561/2200000016 – volume: 4 start-page: 1136 issue: 3 year: 1994 ident: 10.1016/j.automatica.2021.109608_b30 article-title: On smoothing exact penalty functions for convex constrained optimization publication-title: IEEE Transactions on Communications – volume: 22 start-page: 349 issue: 2 year: 2014 ident: 10.1016/j.automatica.2021.109608_b17 article-title: Network resource allocation for users with multiple connections: fairness and stability publication-title: IEEE/ACM Transactions on Networking doi: 10.1109/TNET.2013.2251896 – volume: 5 start-page: 2185 issue: 8 year: 2006 ident: 10.1016/j.automatica.2021.109608_b25 article-title: Distributed algorithms for maximum lifetime routing in wireless sensor networks publication-title: IEEE Transactions on Wireless Communication doi: 10.1109/TWC.2006.1687734 – volume: 4 start-page: 1757 issue: 2 year: 1985 ident: 10.1016/j.automatica.2021.109608_b26 article-title: Computable numerical bounds for LAGRANGE multipliers of stationary points of non-convex differentiable non-linear programs publication-title: Operations Research Letters doi: 10.1016/0167-6377(85)90030-6 – volume: 61 start-page: 994 issue: 4 year: 2015 ident: 10.1016/j.automatica.2021.109608_b35 article-title: Newton-Raphson consensus for distributed convex optimization publication-title: IEEE Transactions on Automatic Control doi: 10.1109/TAC.2015.2449811 – volume: 123 start-page: 47 year: 2019 ident: 10.1016/j.automatica.2021.109608_b45 article-title: Resilient distributed optimal generation dispatch for lossy ac microgrids publication-title: Systems & Control Letters doi: 10.1016/j.sysconle.2018.10.007 – volume: 52 start-page: 1136 issue: 7 year: 2004 ident: 10.1016/j.automatica.2021.109608_b41 article-title: Simultaneous routing and resource allocation via dual decomposition publication-title: IEEE Transactions on Communications doi: 10.1109/TCOMM.2004.831346 – volume: 17 start-page: 37 year: 1967 ident: 10.1016/j.automatica.2021.109608_b27 article-title: The Fritz John necessary optimality conditions in the presence of equality and inequality constraints publication-title: Operations Research Letters – volume: 51 start-page: 230 issue: 23 year: 2018 ident: 10.1016/j.automatica.2021.109608_b34 article-title: Distributed augmentation-regularization for robust online convex optimization publication-title: IFAC-PapersOnLine doi: 10.1016/j.ifacol.2018.12.040 – volume: 27 start-page: 1761 issue: 4 year: 2012 ident: 10.1016/j.automatica.2021.109608_b43 article-title: Convergence analysis of the incremental cost consensus algorithm under different communication network topologies in a smart grid publication-title: IEEE Transactions on Power Systems doi: 10.1109/TPWRS.2012.2188912 – year: 2009 ident: 10.1016/j.automatica.2021.109608_b8 – year: 2014 ident: 10.1016/j.automatica.2021.109608_b29 article-title: An accelerated dual gradient-projection algorithm for embedded linear model predictive control publication-title: IEEE Transactions on Automatic Control doi: 10.1109/TAC.2013.2275667 – volume: 60 start-page: 922 issue: 4 year: 2015 ident: 10.1016/j.automatica.2021.109608_b19 article-title: Linear convergence rate of a class of distributed augmented Lagrangian algorithms publication-title: IEEE Transactions on Automatic Control doi: 10.1109/TAC.2014.2363299 – ident: 10.1016/j.automatica.2021.109608_b20 doi: 10.1109/PESGM.2012.6345156 – ident: 10.1016/j.automatica.2021.109608_b31 doi: 10.1109/CDC.2011.6160931 – volume: 57 start-page: 592 issue: 3 year: 2012 ident: 10.1016/j.automatica.2021.109608_b16 article-title: Dual averaging for distributed optimization: Convergence analysis and network scaling publication-title: IEEE Transactions on Automatic Control doi: 10.1109/TAC.2011.2161027 – ident: 10.1016/j.automatica.2021.109608_b22 doi: 10.23919/ACC.2017.7963458 – ident: 10.1016/j.automatica.2021.109608_b1 doi: 10.1109/CDC.2018.8619343 – volume: 55 start-page: 254 year: 2014 ident: 10.1016/j.automatica.2021.109608_b24 article-title: Distributed convex optimization via continuous-time coordination algorithms with discrete-time communication publication-title: Automatica doi: 10.1016/j.automatica.2015.03.001 – ident: 10.1016/j.automatica.2021.109608_b21 doi: 10.1016/j.ifacol.2016.05.003 – ident: 10.1016/j.automatica.2021.109608_b37 doi: 10.1109/CDC.2011.6161503 – volume: 2 start-page: 226 issue: 3 year: 2015 ident: 10.1016/j.automatica.2021.109608_b10 article-title: Distributed generator coordination for initialization and anytime optimization in economic dispatch publication-title: IEEE Transactions on Control of Network Systems doi: 10.1109/TCNS.2015.2399191 – volume: 74 start-page: 183 year: 2016 ident: 10.1016/j.automatica.2021.109608_b11 article-title: Initialization-free distributed coordination for economic dispatch under varying loads and generator commitment publication-title: Automatica doi: 10.1016/j.automatica.2016.07.003 – year: 2013 ident: 10.1016/j.automatica.2021.109608_b39 – volume: 3 start-page: 1 year: 1975 ident: 10.1016/j.automatica.2021.109608_b3 article-title: Nondifferentiable optimization via approximation publication-title: Mathematical Programing Study doi: 10.1007/BFb0120696 – ident: 10.1016/j.automatica.2021.109608_b13 doi: 10.1109/CDC.2018.8619760 – year: 2008 ident: 10.1016/j.automatica.2021.109608_b18 – volume: 107 start-page: 49 year: 2017 ident: 10.1016/j.automatica.2021.109608_b23 article-title: Distributed optimal in-network resource allocation algorithm design via a control theoretic approach publication-title: Systems & Control Letters doi: 10.1016/j.sysconle.2017.07.012 – year: 1997 ident: 10.1016/j.automatica.2021.109608_b5 – ident: 10.1016/j.automatica.2021.109608_b14 doi: 10.23919/ACC.2018.8431779 – ident: 10.1016/j.automatica.2021.109608_b32 doi: 10.1109/CDC.2017.8264654 – ident: 10.1016/j.automatica.2021.109608_b33 doi: 10.1109/CDC.2018.8619651 – volume: 41 start-page: 78 issue: 1 year: 2013 ident: 10.1016/j.automatica.2021.109608_b36 article-title: On LICQ and the uniqueness of Lagrange multipliers publication-title: Operations Research Letters doi: 10.1016/j.orl.2012.11.009 – volume: 129 start-page: 469 issue: 3 year: 2006 ident: 10.1016/j.automatica.2021.109608_b40 article-title: Optimal scaling of a gradient method for distributed resource allocation publication-title: Journal of Optimization Theory and Applications doi: 10.1007/s10957-006-9080-1 – volume: 74 start-page: 259 year: 2016 ident: 10.1016/j.automatica.2021.109608_b42 article-title: Initialization-free distributed algorithms for optimal resource allocation with feasibility constraints and its application to economic dispatch of power systems publication-title: Automatica doi: 10.1016/j.automatica.2016.08.007 – volume: 60 start-page: 443 issue: 1 year: 2012 ident: 10.1016/j.automatica.2021.109608_b9 article-title: Convergence analysis of saddle point problems in time varying wireless systems – control theoretical approach publication-title: IEEE Transactions on Signal Processing doi: 10.1109/TSP.2011.2169407 – year: 2020 ident: 10.1016/j.automatica.2021.109608_b28 – ident: 10.1016/j.automatica.2021.109608_b44 doi: 10.1109/CDC.2018.8619512 – year: 1958 ident: 10.1016/j.automatica.2021.109608_b2 – year: 2004 ident: 10.1016/j.automatica.2021.109608_b7 – volume: 8 start-page: 2974 issue: 6 year: 2017 ident: 10.1016/j.automatica.2021.109608_b38 article-title: Optimal power flow of radial networks and its variations: A sequential convex optimization approach publication-title: IEEE Transactions on Smart Grid doi: 10.1109/TSG.2017.2684183 – ident: 10.1016/j.automatica.2021.109608_b15 doi: 10.1109/CDC.2012.6426665 |
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| SubjectTerms | Augmented Lagrangian Distributed constrained convex optimization Optimal resource allocation Penalty function methods Primal–dual solutions |
| Title | Cluster-based distributed augmented Lagrangian algorithm for a class of constrained convex optimization problems |
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