Distributed convex stochastic optimization under few constraints in large networks

Romain Couillet; Pascal Bianchi; Jérémie Jakubowicz

doi:10.1109/CAMSAP.2011.6136006

Distributed convex stochastic optimization under few constraints in large networks

Romain Couillet
, Pascal Bianchi
, Jérémie Jakubowicz

CentraleSup lec and Inria Saclay
CNRS LTCI

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collection › Contribution à une conférence › Revue par des pairs

3 Citations (Scopus)

Résumé

This article introduces a distributed convex optimization algorithm in a constrained multi-agent system composed by a large number of nodes. We focus on the case where each agent seeks to optimize its own local parameter under few coupling equality and inequality constraints. The objective function is of the power flow type and can be decoupled as a sum of elementary functions, each of which assumed (imperfectly) known by only one node. Under these assumptions, a cost-efficient decentralized iterative solution based on Lagrangian duality is derived, which is provably converging. This new approach alleviates several limitations of algorithms proposed in the stochastic optimization literature. Applications are proposed to decentralized power flow optimization in smart grids.

langue originale	Anglais
titre	2011 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2011
Pages	289-292
Nombre de pages	4
Les DOIs	https://doi.org/10.1109/CAMSAP.2011.6136006
état	Publié - 1 déc. 2011
Modification externe	Oui
Evénement	2011 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2011 - San Juan, Puerto Rico Durée: 13 déc. 2011 → 16 déc. 2011

Série de publications

Nom	2011 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2011

Une conférence

Une conférence	2011 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2011
Pays/Territoire	Puerto Rico
La ville	San Juan
période	13/12/11 → 16/12/11

SDG des Nations Unies

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Accès au document

10.1109/CAMSAP.2011.6136006

Contient cette citation

Couillet, R., Bianchi, P., & Jakubowicz, J. (2011). Distributed convex stochastic optimization under few constraints in large networks. Dans 2011 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2011 (p. 289-292). Article 6136006 (2011 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2011). https://doi.org/10.1109/CAMSAP.2011.6136006

Couillet, Romain ; Bianchi, Pascal ; Jakubowicz, Jérémie. / Distributed convex stochastic optimization under few constraints in large networks. 2011 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2011. 2011. p. 289-292 (2011 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2011).

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title = "Distributed convex stochastic optimization under few constraints in large networks",

abstract = "This article introduces a distributed convex optimization algorithm in a constrained multi-agent system composed by a large number of nodes. We focus on the case where each agent seeks to optimize its own local parameter under few coupling equality and inequality constraints. The objective function is of the power flow type and can be decoupled as a sum of elementary functions, each of which assumed (imperfectly) known by only one node. Under these assumptions, a cost-efficient decentralized iterative solution based on Lagrangian duality is derived, which is provably converging. This new approach alleviates several limitations of algorithms proposed in the stochastic optimization literature. Applications are proposed to decentralized power flow optimization in smart grids.",

author = "Romain Couillet and Pascal Bianchi and J{\'e}r{\'e}mie Jakubowicz",

year = "2011",

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day = "1",

doi = "10.1109/CAMSAP.2011.6136006",

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series = "2011 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2011",

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booktitle = "2011 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2011",

note = "2011 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2011 ; Conference date: 13-12-2011 Through 16-12-2011",

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Couillet, R, Bianchi, P & Jakubowicz, J 2011, Distributed convex stochastic optimization under few constraints in large networks. Dans 2011 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2011., 6136006, 2011 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2011, p. 289-292, 2011 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2011, San Juan, Puerto Rico, 13/12/11. https://doi.org/10.1109/CAMSAP.2011.6136006

Distributed convex stochastic optimization under few constraints in large networks. / Couillet, Romain; Bianchi, Pascal; Jakubowicz, Jérémie.
2011 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2011. 2011. p. 289-292 6136006 (2011 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2011).

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collection › Contribution à une conférence › Revue par des pairs

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N2 - This article introduces a distributed convex optimization algorithm in a constrained multi-agent system composed by a large number of nodes. We focus on the case where each agent seeks to optimize its own local parameter under few coupling equality and inequality constraints. The objective function is of the power flow type and can be decoupled as a sum of elementary functions, each of which assumed (imperfectly) known by only one node. Under these assumptions, a cost-efficient decentralized iterative solution based on Lagrangian duality is derived, which is provably converging. This new approach alleviates several limitations of algorithms proposed in the stochastic optimization literature. Applications are proposed to decentralized power flow optimization in smart grids.

AB - This article introduces a distributed convex optimization algorithm in a constrained multi-agent system composed by a large number of nodes. We focus on the case where each agent seeks to optimize its own local parameter under few coupling equality and inequality constraints. The objective function is of the power flow type and can be decoupled as a sum of elementary functions, each of which assumed (imperfectly) known by only one node. Under these assumptions, a cost-efficient decentralized iterative solution based on Lagrangian duality is derived, which is provably converging. This new approach alleviates several limitations of algorithms proposed in the stochastic optimization literature. Applications are proposed to decentralized power flow optimization in smart grids.

U2 - 10.1109/CAMSAP.2011.6136006

DO - 10.1109/CAMSAP.2011.6136006

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SN - 9781457721052

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Couillet R, Bianchi P, Jakubowicz J. Distributed convex stochastic optimization under few constraints in large networks. Dans 2011 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2011. 2011. p. 289-292. 6136006. (2011 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2011). doi: 10.1109/CAMSAP.2011.6136006

Distributed convex stochastic optimization under few constraints in large networks

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