Distributed Computational Framework for Large-Scale Stochastic Convex Optimization
This paper presents a distributed computational framework for stochastic convex optimization problems using the so-called scenario approach. Such a problem arises, for example, in a large-scale network of interconnected linear systems with local and common uncertainties. Due to the large number of r...
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| Vydáno v: | Energies (Basel) Ročník 14; číslo 1; s. 23 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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01.01.2021
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| ISSN: | 1996-1073, 1996-1073 |
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| Abstract | This paper presents a distributed computational framework for stochastic convex optimization problems using the so-called scenario approach. Such a problem arises, for example, in a large-scale network of interconnected linear systems with local and common uncertainties. Due to the large number of required scenarios to approximate the stochasticity of these problems, the stochastic optimization involves formulating a large-scale scenario program, which is in general computationally demanding. We present two novel ideas in this paper to address this issue. We first develop a technique to decompose the large-scale scenario program into distributed scenario programs that exchange a certain number of scenarios with each other to compute local decisions using the alternating direction method of multipliers (ADMM). We show the exactness of the decomposition with a-priori probabilistic guarantees for the desired level of constraint fulfillment for both local and common uncertainty sources. As our second contribution, we develop a so-called soft communication scheme based on a set parametrization technique together with the notion of probabilistically reliable sets to reduce the required communication between the subproblems. We show how to incorporate the probabilistic reliability notion into existing results and provide new guarantees for the desired level of constraint violations. Two different simulation studies of two types of interconnected network, namely dynamically coupled and coupling constraints, are presented to illustrate advantages of the proposed distributed framework. |
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| AbstractList | This paper presents a distributed computational framework for stochastic convex optimization problems using the so-called scenario approach. Such a problem arises, for example, in a large-scale network of interconnected linear systems with local and common uncertainties. Due to the large number of required scenarios to approximate the stochasticity of these problems, the stochastic optimization involves formulating a large-scale scenario program, which is in general computationally demanding. We present two novel ideas in this paper to address this issue. We first develop a technique to decompose the large-scale scenario program into distributed scenario programs that exchange a certain number of scenarios with each other to compute local decisions using the alternating direction method of multipliers (ADMM). We show the exactness of the decomposition with a-priori probabilistic guarantees for the desired level of constraint fulfillment for both local and common uncertainty sources. As our second contribution, we develop a so-called soft communication scheme based on a set parametrization technique together with the notion of probabilistically reliable sets to reduce the required communication between the subproblems. We show how to incorporate the probabilistic reliability notion into existing results and provide new guarantees for the desired level of constraint violations. Two different simulation studies of two types of interconnected network, namely dynamically coupled and coupling constraints, are presented to illustrate advantages of the proposed distributed framework. |
| Author | Keviczky, Tamás Rostampour, Vahab |
| Author_xml | – sequence: 1 givenname: Vahab orcidid: 0000-0002-3756-3849 surname: Rostampour fullname: Rostampour, Vahab – sequence: 2 givenname: Tamás surname: Keviczky fullname: Keviczky, Tamás |
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| Cites_doi | 10.1109/TSG.2018.2834150 10.1109/TAC.2013.2254641 10.1109/TSP.2014.2304432 10.1109/TAC.2016.2612822 10.1109/ACC.2015.7170771 10.1109/CDC.2007.4434064 10.1016/j.automatica.2014.10.035 10.1109/CDC.2012.6426462 10.1109/TAC.2014.2303232 10.1016/j.automatica.2013.02.060 10.1016/j.arcontrol.2004.01.003 10.1080/00207170701491070 10.1561/2200000016 10.1109/TEC.2007.914174 10.1016/j.automatica.2011.09.048 10.1109/CoASE.2014.6899461 10.23919/ACC.2017.7963798 10.1109/TAC.2006.875041 10.1137/07069821X 10.23919/ECC.2013.6669266 10.1007/s00211-014-0673-6 10.1109/TAC.2010.2086553 10.1109/CDC.2017.8264043 10.1016/j.automatica.2007.04.027 10.1007/BFb0109870 10.1109/CDC.2015.7402994 |
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| References | ref_13 ref_12 ref_10 ref_32 ref_31 Calafiore (ref_33) 2006; 51 Calafiore (ref_11) 2013; 49 ref_19 ref_18 Lavaei (ref_4) 2008; 44 ref_16 Cannon (ref_6) 2011; 56 ref_15 Hokayem (ref_9) 2012; 48 Margellos (ref_14) 2014; 59 Kouvaritakis (ref_1) 2004; 28 Boyd (ref_29) 2011; 3 Shi (ref_27) 2014; 62 He (ref_30) 2015; 130 ref_25 ref_24 ref_23 Richards (ref_5) 2007; 80 ref_20 Schildbach (ref_22) 2014; 50 ref_3 ref_2 Riverso (ref_7) 2013; 58 ref_28 ref_26 Campi (ref_17) 2008; 19 Dai (ref_8) 2016; 62 Papaefthymiou (ref_21) 2008; 23 |
| References_xml | – ident: ref_26 doi: 10.1109/TSG.2018.2834150 – ident: ref_24 – volume: 58 start-page: 2608 year: 2013 ident: ref_7 article-title: Plug-and-play decentralized model predictive control for linear systems publication-title: IEEE Trans. Autom. Control doi: 10.1109/TAC.2013.2254641 – volume: 62 start-page: 1750 year: 2014 ident: ref_27 article-title: On the Linear Convergence of the ADMM in Decentralized Consensus Optimization publication-title: IEEE Trans. Signal Process. doi: 10.1109/TSP.2014.2304432 – ident: ref_16 – volume: 62 start-page: 3474 year: 2016 ident: ref_8 article-title: Distributed Stochastic MPC of Linear Systems with Additive Uncertainty and Coupled Probabilistic Constraints publication-title: IEEE Trans. Autom. Control doi: 10.1109/TAC.2016.2612822 – ident: ref_12 doi: 10.1109/ACC.2015.7170771 – ident: ref_2 doi: 10.1109/CDC.2007.4434064 – volume: 50 start-page: 3009 year: 2014 ident: ref_22 article-title: The scenario approach for stochastic model predictive control with bounds on closed-loop constraint violations publication-title: Automatica doi: 10.1016/j.automatica.2014.10.035 – ident: ref_10 doi: 10.1109/CDC.2012.6426462 – volume: 59 start-page: 2258 year: 2014 ident: ref_14 article-title: On the road between robust optimization and the scenario approach for chance constrained optimization problems publication-title: IEEE Trans. Autom. Control doi: 10.1109/TAC.2014.2303232 – ident: ref_23 – volume: 49 start-page: 1861 year: 2013 ident: ref_11 article-title: Stochastic model predictive control of LPV systems via scenario optimization publication-title: Automatica doi: 10.1016/j.automatica.2013.02.060 – volume: 28 start-page: 23 year: 2004 ident: ref_1 article-title: Recent developments in stochastic MPC and sustainable development publication-title: Annu. Rev. Control doi: 10.1016/j.arcontrol.2004.01.003 – volume: 80 start-page: 1517 year: 2007 ident: ref_5 article-title: Robust distributed model predictive control publication-title: Int. J. Control doi: 10.1080/00207170701491070 – volume: 3 start-page: 1 year: 2011 ident: ref_29 article-title: Distributed optimization and statistical learning via the alternating direction method of multipliers publication-title: Found. Trends Mach. Learn. doi: 10.1561/2200000016 – volume: 23 start-page: 234 year: 2008 ident: ref_21 article-title: MCMC for wind power simulation publication-title: IEEE Trans. Energy Convers. doi: 10.1109/TEC.2007.914174 – volume: 48 start-page: 77 year: 2012 ident: ref_9 article-title: Stochastic receding horizon control with output feedback and bounded controls publication-title: Automatica doi: 10.1016/j.automatica.2011.09.048 – ident: ref_18 doi: 10.1109/CoASE.2014.6899461 – ident: ref_32 doi: 10.23919/ACC.2017.7963798 – volume: 51 start-page: 742 year: 2006 ident: ref_33 article-title: The scenario approach to robust control design publication-title: IEEE Trans. Autom. Control doi: 10.1109/TAC.2006.875041 – volume: 19 start-page: 1211 year: 2008 ident: ref_17 article-title: The exact feasibility of randomized solutions of uncertain convex programs publication-title: SIAM J. Optim. doi: 10.1137/07069821X – ident: ref_25 – ident: ref_31 – ident: ref_20 doi: 10.23919/ECC.2013.6669266 – volume: 130 start-page: 567 year: 2015 ident: ref_30 article-title: On non-ergodic convergence rate of Douglas–Rachford alternating direction method of multipliers publication-title: Numerische Mathematik doi: 10.1007/s00211-014-0673-6 – volume: 56 start-page: 194 year: 2011 ident: ref_6 article-title: Stochastic tubes in model predictive control with probabilistic constraints publication-title: IEEE Trans. Autom. Control doi: 10.1109/TAC.2010.2086553 – ident: ref_28 doi: 10.1109/CDC.2017.8264043 – ident: ref_15 – ident: ref_19 – volume: 44 start-page: 141 year: 2008 ident: ref_4 article-title: Control of continuous-time LTI systems by means of structurally constrained controllers publication-title: Automatica doi: 10.1016/j.automatica.2007.04.027 – ident: ref_3 doi: 10.1007/BFb0109870 – ident: ref_13 doi: 10.1109/CDC.2015.7402994 |
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| SubjectTerms | decentralized scenario program distributed computation distributed scenario program distributed stochastic systems scenario convex program stochastic optimization |
| Title | Distributed Computational Framework for Large-Scale Stochastic Convex Optimization |
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