Primal-dual algorithm for distributed optimization with local domains on signed networks

We consider the distributed optimization problem on signed networks. Each agent has a local function which depends on a subset of the components of the variable and is subject to a local constraint set. A primal-dual algorithm with fixed step size is proposed. The algorithm ensures that the agents&#...

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Veröffentlicht in:Chinese Control Conference S. 4930 - 4935
Hauptverfasser: Ren, Xiaoxing, Li, Dewei, Xi, Yugeng, Pan, Lulu, Shao, Haibin
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: Technical Committee on Control Theory, Chinese Association of Automation 01.07.2020
Schlagworte:
Automation
Control systems
Convex functions
convex optimization
distributed optimization
Indexes
Machine learning algorithms
multi-agent systems
Optimization
primal-dual method
signed network
Silicon
ISSN:1934-1768
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Abstract We consider the distributed optimization problem on signed networks. Each agent has a local function which depends on a subset of the components of the variable and is subject to a local constraint set. A primal-dual algorithm with fixed step size is proposed. The algorithm ensures that the agents' estimates converge to a subset of the components of an optimal solution or its opposite. Note that each component of the variable is allowed to be associated with more than one agents, our algorithm guarantees that those coupled agents achieve bipartite consensus on estimates for the intersection components. Numerical results are provided to demonstrate the theoretical analysis.
AbstractList We consider the distributed optimization problem on signed networks. Each agent has a local function which depends on a subset of the components of the variable and is subject to a local constraint set. A primal-dual algorithm with fixed step size is proposed. The algorithm ensures that the agents' estimates converge to a subset of the components of an optimal solution or its opposite. Note that each component of the variable is allowed to be associated with more than one agents, our algorithm guarantees that those coupled agents achieve bipartite consensus on estimates for the intersection components. Numerical results are provided to demonstrate the theoretical analysis.
Author Ren, Xiaoxing
Xi, Yugeng
Pan, Lulu
Li, Dewei
Shao, Haibin
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  givenname: Xiaoxing
  surname: Ren
  fullname: Ren, Xiaoxing
  organization: Shanghai Jiao Tong University,Department of Automation,Shanghai
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  givenname: Dewei
  surname: Li
  fullname: Li, Dewei
  organization: Shanghai Jiao Tong University,Department of Automation,Shanghai
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  givenname: Yugeng
  surname: Xi
  fullname: Xi, Yugeng
  organization: Shanghai Jiao Tong University,Department of Automation,Shanghai
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  givenname: Lulu
  surname: Pan
  fullname: Pan, Lulu
  organization: Shanghai Jiao Tong University,Department of Automation,Shanghai
– sequence: 5
  givenname: Haibin
  surname: Shao
  fullname: Shao, Haibin
  organization: Shanghai Jiao Tong University,Department of Automation,Shanghai
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Snippet We consider the distributed optimization problem on signed networks. Each agent has a local function which depends on a subset of the components of the...
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StartPage 4930
SubjectTerms Automation
Control systems
Convex functions
convex optimization
distributed optimization
Indexes
Machine learning algorithms
multi-agent systems
Optimization
primal-dual method
signed network
Silicon
Title Primal-dual algorithm for distributed optimization with local domains on signed networks
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