Full Text:   <762>

CLC number: O224

On-line Access: 2024-08-27

Received: 2023-10-17

Revision Accepted: 2024-05-08

Crosschecked: 2023-02-02

Cited: 0

Clicked: 1517

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Hongzhe LIU

https://orcid.org/0000-0001-9021-4755

-   Go to

Article info.
Open peer comments

Frontiers of Information Technology & Electronic Engineering  2023 Vol.24 No.9 P.1253-1260

http://doi.org/10.1631/FITEE.2200596


Distributed optimization based on improved push-sum framework for optimization problem with multiple local constraints and its application in smart grid


Author(s):  Qian XU, Chutian YU, Xiang YUAN, Mengli WEI, Hongzhe LIU

Affiliation(s):  State Grid Zhejiang Economic Research Institute, Hangzhou 310008, China; more

Corresponding email(s):   xuqianhangzhou@163.com, yuchutianhangzhou@163.com, yuanxianghangzhou@163.com, wengli33wei@gmail.com, 15150650205@163.com

Key Words:  Distributed optimization, Nonidentical constraints, Improved push-sum framework


Qian XU, Chutian YU, Xiang YUAN, Mengli WEI, Hongzhe LIU. Distributed optimization based on improved push-sum framework for optimization problem with multiple local constraints and its application in smart grid[J]. Frontiers of Information Technology & Electronic Engineering, 2023, 24(9): 1253-1260.

@article{title="Distributed optimization based on improved push-sum framework for optimization problem with multiple local constraints and its application in smart grid",
author="Qian XU, Chutian YU, Xiang YUAN, Mengli WEI, Hongzhe LIU",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="24",
number="9",
pages="1253-1260",
year="2023",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2200596"
}

%0 Journal Article
%T Distributed optimization based on improved push-sum framework for optimization problem with multiple local constraints and its application in smart grid
%A Qian XU
%A Chutian YU
%A Xiang YUAN
%A Mengli WEI
%A Hongzhe LIU
%J Frontiers of Information Technology & Electronic Engineering
%V 24
%N 9
%P 1253-1260
%@ 2095-9184
%D 2023
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2200596

TY - JOUR
T1 - Distributed optimization based on improved push-sum framework for optimization problem with multiple local constraints and its application in smart grid
A1 - Qian XU
A1 - Chutian YU
A1 - Xiang YUAN
A1 - Mengli WEI
A1 - Hongzhe LIU
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 24
IS - 9
SP - 1253
EP - 1260
%@ 2095-9184
Y1 - 2023
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2200596


Abstract: 
In this paper, the optimization problem subject to N nonidentical closed convex set constraints is studied. The aim is to design a corresponding distributed optimization algorithm over the fixed unbalanced graph to solve the considered problem. To this end, with the push-sum framework improved, the distributed optimization algorithm is newly designed, and its strict convergence analysis is given under the assumption that the involved graph is strongly connected. Finally, simulation results support the good performance of the proposed algorithm.

带多局部约束的改进push-sum框架分布式优化及其在智能电网中的应用

徐谦1,俞楚天1,袁翔1,韦梦立2,刘洪喆2
1国网浙江省电力有限公司经济技术研究院,中国浙江省杭州市,310008
2北京隐山科技有限公司,中国北京市,100871
摘要:本文研究了带N个非一致闭凸集约束的分布式优化问题,目的是在固定的不平衡图上设计一个相应的分布式优化算法解决该问题。为此,在改进的push-sum框架下,本文设计了新的分布式优化算法,并在强连通图的假设下给出了其严格的收敛分析。最后,仿真结果证明了所提算法的良好性能。

关键词:分布式优化;非一致约束;改进push-sum框架

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

[1]Gharesifard B, Cortés J, 2014. Distributed continuous-time convex optimization on weight-balanced digraphs. IEEE Trans Autom Contr, 59(3):781-786.

[2]Kia SS, Cortés J, Martínez S, 2015. Distributed convex optimization via continuous-time coordination algorithms with discrete-time communication. Automatica, 55:254-264.

[3]Liu HZ, Zheng WX, Yu W, 2021. Distributed discrete-time algorithms for convex optimization with general local constraints on weight-unbalanced digraph. IEEE Trans Contr Netw Syst, 8(1):51-64.

[4]Liu QS, Wang J, 2015. A second-order multi-agent network for bound-constrained distributed optimization. IEEE Trans Autom Contr, 60(12):3310-3315.

[5]Mai VS, Abed EH, 2019. Distributed optimization over directed graphs with row stochasticity and constraint regularity. Automatica, 102:94-104.

[6]Nedić A, Ozdaglar A, 2009. Distributed subgradient methods for multi-agent optimization. IEEE Trans Autom Contr, 54(1):48-61.

[7]Nedić A, Olshevsky A, 2015. Distributed optimization over time-varying directed graphs. IEEE Trans Autom Contr, 60(3):601-615.

[8]Nedić A, Ozdaglar A, Parrilo PA, 2010. Constrained consensus and optimization in multi-agent networks. IEEE Trans Autom Contr, 55(4):922-938.

[9]Pu S, Shi W, Xu JM, et al., 2018. A push-pull gradient method for distributed optimization in networks. IEEE Conf on Decision and Control, p.3385-3390.

[10]Pu S, Shi W, Xu J, et al., 2021. Push pull gradient methods for distributed optimization in networks. IEEE Trans Autom Contr, 66(1):1-16.

[11]Qu GN, Li N, 2018. Harnessing smoothness to accelerate distributed optimization. IEEE Trans Contr Netw Syst, 5(3):1245-1260.

[12]Wang J, Elia N, 2011. A control perspective for centralized and distributed convex optimization. Proc 50th IEEE Conf on Decision and Control and European Control Conf, p.3800-3805.

[13]Xi CG, Xin R, Khan UA, 2018. ADD-OPT: accelerated distributed directed optimization. IEEE Trans Autom Contr, 63(5):1329-1339.

[14]Yang SF, Liu QS, Wang J, 2017. A multi-agent system with a proportional-integral protocol for distributed constrained optimization. IEEE Trans Autom Contr, 62(7):3461-3467.

[15]Yu WW, Liu HZ, Zheng WX, et al., 2021. Distributed discrete-time convex optimization with nonidentical local constraints over time-varying unbalanced directed graphs. Automatica, 134:109899.

[16]Yuan DM, Xu SY, Zhao HY, 2011. Distributed primal-dual subgradient method for multiagent optimization via consensus algorithms. IEEE Trans Syst Man Cybern Part B Cybern, 41(6):1715-1724.

[17]Zhu MH, Martínez S, 2012. On distributed convex optimization under inequality and equality constraints. IEEE Trans Autom Contr, 57(1):151-164.

[18]Zhu YN, Yu WW, Wen GH, et al., 2019a. Continuous-time coordination algorithm for distributed convex optimization over weight–unbalanced directed networks. IEEE Trans Circ Syst II Express Briefs, 66(7):1202-1206.

[19]Zhu YN, Yu WW, Wen GH, et al., 2019b. Continuous-time distributed subgradient algorithm for convex optimization with general constraints. IEEE Trans Autom Contr, 64(4):1694-1701.

[20]Zimmermann J, Tatarenko T, Willert V, et al., 2020. Projected push-sum gradient descent-ascent for convex optimization with application to economic dispatch problems. Proc 59th IEEE Conf on Decision and Control, p.542-547.

Open peer comments: Debate/Discuss/Question/Opinion

<1>

Please provide your name, email address and a comment





Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - 2024 Journal of Zhejiang University-SCIENCE