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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

Original language	English
Title of host publication	2011 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2011
Pages	289-292
Number of pages	4
DOIs	https://doi.org/10.1109/CAMSAP.2011.6136006
Publication status	Published - 1 Dec 2011
Externally published	Yes
Event	2011 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2011 - San Juan, Puerto Rico Duration: 13 Dec 2011 → 16 Dec 2011

Publication series

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

Conference

Conference	2011 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2011
Country/Territory	Puerto Rico
City	San Juan
Period	13/12/11 → 16/12/11

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Access to Document

10.1109/CAMSAP.2011.6136006

Cite this

Couillet, R., Bianchi, P., & Jakubowicz, J. (2011). Distributed convex stochastic optimization under few constraints in large networks. In 2011 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2011 (pp. 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. pp. 289-292 (2011 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2011).

@inproceedings{4f85dba237414de0998d43fc81a9a7aa,

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",

month = dec,

day = "1",

doi = "10.1109/CAMSAP.2011.6136006",

language = "English",

isbn = "9781457721052",

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

pages = "289--292",

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",

}

Couillet, R, Bianchi, P & Jakubowicz, J 2011, Distributed convex stochastic optimization under few constraints in large networks. in 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, pp. 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).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Distributed convex stochastic optimization under few constraints in large networks

AU - Couillet, Romain

AU - Bianchi, Pascal

AU - Jakubowicz, Jérémie

PY - 2011/12/1

Y1 - 2011/12/1

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.

UR - https://www.scopus.com/pages/publications/84857166046

U2 - 10.1109/CAMSAP.2011.6136006

DO - 10.1109/CAMSAP.2011.6136006

M3 - Conference contribution

AN - SCOPUS:84857166046

SN - 9781457721052

T3 - 2011 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2011

SP - 289

EP - 292

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

T2 - 2011 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2011

Y2 - 13 December 2011 through 16 December 2011

ER -

Couillet R, Bianchi P, Jakubowicz J. Distributed convex stochastic optimization under few constraints in large networks. In 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

Abstract

Publication series

Conference

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this