Distributed Subgradient Algorithm for Multi-Agent Convex Optimization with Global Inequality and Equality Constraints

Li Xiao; Junjie Bao; Xi Shi

doi:doi:10.11648/j.acm.20160505.15

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Distributed Subgradient Algorithm for Multi-Agent Convex Optimization with Global Inequality and Equality Constraints

Li Xiao, Junjie Bao, Xi Shi

Published in Applied and Computational Mathematics (Volume 5, Issue 5)

Received: 7 August 2016 Accepted: 5 October 2016 Published: 27 October 2016

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Abstract

In this paper, we present an improved subgradient algorithm for solving a general multi-agent convex optimization problem in a distributed way, where the agents are to jointly minimize a global objective function subject to a global inequality constraint, a global equality constraint and a global constraint set. The global objective function is a combination of local agent objective functions and the global constraint set is the intersection of each agent local constraint set. Our motivation comes from networking applications where dual and primal-dual subgradient methods have attracted much attention in the design of decentralized network protocols. Our main focus is on constrained problems where the local constraint sets are identical. Thus, we propose a distributed primal-dual subgradient algorithm, which is based on the description of the primal-dual optimal solutions as the saddle points of the penalty functions. We show that, the algorithm can be implemented over networks with changing topologies but satisfying a standard connectivity property, and allow the agents to asymptotically converge to optimal solution with optimal value of the optimization problem under the Slater’s condition.

Published in	Applied and Computational Mathematics (Volume 5, Issue 5)
DOI	10.11648/j.acm.20160505.15
Page(s)	213-229
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Consensus, Saddle Point, Distributed Optimization, Subgradient Algorithm

References

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APA Style

Li Xiao, Junjie Bao, Xi Shi. (2016). Distributed Subgradient Algorithm for Multi-Agent Convex Optimization with Global Inequality and Equality Constraints. Applied and Computational Mathematics, 5(5), 213-229. https://doi.org/10.11648/j.acm.20160505.15

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ACS Style

Li Xiao; Junjie Bao; Xi Shi. Distributed Subgradient Algorithm for Multi-Agent Convex Optimization with Global Inequality and Equality Constraints. Appl. Comput. Math. 2016, 5(5), 213-229. doi: 10.11648/j.acm.20160505.15

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AMA Style

Li Xiao, Junjie Bao, Xi Shi. Distributed Subgradient Algorithm for Multi-Agent Convex Optimization with Global Inequality and Equality Constraints. Appl Comput Math. 2016;5(5):213-229. doi: 10.11648/j.acm.20160505.15

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@article{10.11648/j.acm.20160505.15,
  author = {Li Xiao and Junjie Bao and Xi Shi},
  title = {Distributed Subgradient Algorithm for Multi-Agent Convex Optimization with Global Inequality and Equality Constraints},
  journal = {Applied and Computational Mathematics},
  volume = {5},
  number = {5},
  pages = {213-229},
  doi = {10.11648/j.acm.20160505.15},
  url = {https://doi.org/10.11648/j.acm.20160505.15},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.acm.20160505.15},
  abstract = {In this paper, we present an improved subgradient algorithm for solving a general multi-agent convex optimization problem in a distributed way, where the agents are to jointly minimize a global objective function subject to a global inequality constraint, a global equality constraint and a global constraint set. The global objective function is a combination of local agent objective functions and the global constraint set is the intersection of each agent local constraint set. Our motivation comes from networking applications where dual and primal-dual subgradient methods have attracted much attention in the design of decentralized network protocols. Our main focus is on constrained problems where the local constraint sets are identical. Thus, we propose a distributed primal-dual subgradient algorithm, which is based on the description of the primal-dual optimal solutions as the saddle points of the penalty functions. We show that, the algorithm can be implemented over networks with changing topologies but satisfying a standard connectivity property, and allow the agents to asymptotically converge to optimal solution with optimal value of the optimization problem under the Slater’s condition.},
 year = {2016}
}

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TY  - JOUR
T1  - Distributed Subgradient Algorithm for Multi-Agent Convex Optimization with Global Inequality and Equality Constraints
AU  - Li Xiao
AU  - Junjie Bao
AU  - Xi Shi
Y1  - 2016/10/27
PY  - 2016
N1  - https://doi.org/10.11648/j.acm.20160505.15
DO  - 10.11648/j.acm.20160505.15
T2  - Applied and Computational Mathematics
JF  - Applied and Computational Mathematics
JO  - Applied and Computational Mathematics
SP  - 213
EP  - 229
PB  - Science Publishing Group
SN  - 2328-5613
UR  - https://doi.org/10.11648/j.acm.20160505.15
AB  - In this paper, we present an improved subgradient algorithm for solving a general multi-agent convex optimization problem in a distributed way, where the agents are to jointly minimize a global objective function subject to a global inequality constraint, a global equality constraint and a global constraint set. The global objective function is a combination of local agent objective functions and the global constraint set is the intersection of each agent local constraint set. Our motivation comes from networking applications where dual and primal-dual subgradient methods have attracted much attention in the design of decentralized network protocols. Our main focus is on constrained problems where the local constraint sets are identical. Thus, we propose a distributed primal-dual subgradient algorithm, which is based on the description of the primal-dual optimal solutions as the saddle points of the penalty functions. We show that, the algorithm can be implemented over networks with changing topologies but satisfying a standard connectivity property, and allow the agents to asymptotically converge to optimal solution with optimal value of the optimization problem under the Slater’s condition.
VL  - 5
IS  - 5
ER  -

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Author Information

Li Xiao

Department of Mathematics and Information Engineering, Chongqing University of Education, Chongqing, PR China
Junjie Bao

Department of Mathematics and Information Engineering, Chongqing University of Education, Chongqing, PR China
Xi Shi

Department of Mathematics and Information Engineering, Chongqing University of Education, Chongqing, PR China

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APA Style

Li Xiao, Junjie Bao, Xi Shi. (2016). Distributed Subgradient Algorithm for Multi-Agent Convex Optimization with Global Inequality and Equality Constraints. Applied and Computational Mathematics, 5(5), 213-229. https://doi.org/10.11648/j.acm.20160505.15

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ACS Style

Li Xiao; Junjie Bao; Xi Shi. Distributed Subgradient Algorithm for Multi-Agent Convex Optimization with Global Inequality and Equality Constraints. Appl. Comput. Math. 2016, 5(5), 213-229. doi: 10.11648/j.acm.20160505.15

Copy | Download

AMA Style

Li Xiao, Junjie Bao, Xi Shi. Distributed Subgradient Algorithm for Multi-Agent Convex Optimization with Global Inequality and Equality Constraints. Appl Comput Math. 2016;5(5):213-229. doi: 10.11648/j.acm.20160505.15

Copy | Download

@article{10.11648/j.acm.20160505.15,
  author = {Li Xiao and Junjie Bao and Xi Shi},
  title = {Distributed Subgradient Algorithm for Multi-Agent Convex Optimization with Global Inequality and Equality Constraints},
  journal = {Applied and Computational Mathematics},
  volume = {5},
  number = {5},
  pages = {213-229},
  doi = {10.11648/j.acm.20160505.15},
  url = {https://doi.org/10.11648/j.acm.20160505.15},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.acm.20160505.15},
  abstract = {In this paper, we present an improved subgradient algorithm for solving a general multi-agent convex optimization problem in a distributed way, where the agents are to jointly minimize a global objective function subject to a global inequality constraint, a global equality constraint and a global constraint set. The global objective function is a combination of local agent objective functions and the global constraint set is the intersection of each agent local constraint set. Our motivation comes from networking applications where dual and primal-dual subgradient methods have attracted much attention in the design of decentralized network protocols. Our main focus is on constrained problems where the local constraint sets are identical. Thus, we propose a distributed primal-dual subgradient algorithm, which is based on the description of the primal-dual optimal solutions as the saddle points of the penalty functions. We show that, the algorithm can be implemented over networks with changing topologies but satisfying a standard connectivity property, and allow the agents to asymptotically converge to optimal solution with optimal value of the optimization problem under the Slater’s condition.},
 year = {2016}
}

Copy | Download

TY  - JOUR
T1  - Distributed Subgradient Algorithm for Multi-Agent Convex Optimization with Global Inequality and Equality Constraints
AU  - Li Xiao
AU  - Junjie Bao
AU  - Xi Shi
Y1  - 2016/10/27
PY  - 2016
N1  - https://doi.org/10.11648/j.acm.20160505.15
DO  - 10.11648/j.acm.20160505.15
T2  - Applied and Computational Mathematics
JF  - Applied and Computational Mathematics
JO  - Applied and Computational Mathematics
SP  - 213
EP  - 229
PB  - Science Publishing Group
SN  - 2328-5613
UR  - https://doi.org/10.11648/j.acm.20160505.15
AB  - In this paper, we present an improved subgradient algorithm for solving a general multi-agent convex optimization problem in a distributed way, where the agents are to jointly minimize a global objective function subject to a global inequality constraint, a global equality constraint and a global constraint set. The global objective function is a combination of local agent objective functions and the global constraint set is the intersection of each agent local constraint set. Our motivation comes from networking applications where dual and primal-dual subgradient methods have attracted much attention in the design of decentralized network protocols. Our main focus is on constrained problems where the local constraint sets are identical. Thus, we propose a distributed primal-dual subgradient algorithm, which is based on the description of the primal-dual optimal solutions as the saddle points of the penalty functions. We show that, the algorithm can be implemented over networks with changing topologies but satisfying a standard connectivity property, and allow the agents to asymptotically converge to optimal solution with optimal value of the optimization problem under the Slater’s condition.
VL  - 5
IS  - 5
ER  -

Copy | Download