Distributed Convex Optimization with State-Dependent (Social) Interactions and Time-Varying Topologies

Seyyed Shaho Alaviani; Nicola Elia

doi:10.1109/tsp.2021.3070223

Distributed Convex Optimization with State-Dependent (Social) Interactions and Time-Varying Topologies

Seyyed Shaho Alaviani, Nicola Elia

Electrical and Computer Engineering

Research output: Contribution to journal › Article › peer-review

4 Scopus citations

Abstract

In this paper, an unconstrained collaborative optimization of a sum of convex functions is considered where agents make decisions using local information from their neighbors. The communication between nodes are described by a time-varying sequence of possibly state-dependent weighted networks. A new framework for modeling multi-Agent optimization problems over networks with state-dependent interactions and time-varying topologies is proposed. A gradient-based discrete-Time algorithm using diminishing step size is proposed for converging to the optimal solution under suitable assumptions. The algorithm is totally asynchronous without requiring B-connectivity assumption for convergence. The algorithm still works even if the weighted matrix of the graph is periodic and irreducible in synchronous protocol. Finally, a case study on a network of robots in an automated warehouse is provided in order to demonstrate the results.

Original language	English (US)
Article number	9392368
Pages (from-to)	2611-2624
Number of pages	14
Journal	IEEE Transactions on Signal Processing
Volume	69
DOIs	https://doi.org/10.1109/tsp.2021.3070223
State	Published - Mar 31 2021

Bibliographical note

Funding Information:
Manuscript received December 21, 2019; revised June 21, 2020, December 8, 2020, and February 28, 2021; accepted March 22, 2021. Date of publication March 31, 2021; date of current version May 14, 2021. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Subhro Das. This work was supported by NSF under Grant CCF-1320643, and AFOSR under Grant FA95501510119. A preliminary version of this paper has appeared without proofs in [11]. This work had been done primarily while Seyyed Shaho Alaviani and Nicola Elia were at Iowa State University, Ames, IA, USA. (Corresponding author: Seyyed Shaho Alaviani.) Seyyed Shaho Alaviani is with the Department of Mechanical Engineering, Clemson University, Clemson, SC 29634 USA (e-mail: salavia@clemson.edu).

Publisher Copyright:
© 1991-2012 IEEE.

Keywords

Distributed optimization
convex optimization
state-dependent networks
time-varying topologies

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10.1109/tsp.2021.3070223

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abstract = "In this paper, an unconstrained collaborative optimization of a sum of convex functions is considered where agents make decisions using local information from their neighbors. The communication between nodes are described by a time-varying sequence of possibly state-dependent weighted networks. A new framework for modeling multi-Agent optimization problems over networks with state-dependent interactions and time-varying topologies is proposed. A gradient-based discrete-Time algorithm using diminishing step size is proposed for converging to the optimal solution under suitable assumptions. The algorithm is totally asynchronous without requiring B-connectivity assumption for convergence. The algorithm still works even if the weighted matrix of the graph is periodic and irreducible in synchronous protocol. Finally, a case study on a network of robots in an automated warehouse is provided in order to demonstrate the results.",

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N2 - In this paper, an unconstrained collaborative optimization of a sum of convex functions is considered where agents make decisions using local information from their neighbors. The communication between nodes are described by a time-varying sequence of possibly state-dependent weighted networks. A new framework for modeling multi-Agent optimization problems over networks with state-dependent interactions and time-varying topologies is proposed. A gradient-based discrete-Time algorithm using diminishing step size is proposed for converging to the optimal solution under suitable assumptions. The algorithm is totally asynchronous without requiring B-connectivity assumption for convergence. The algorithm still works even if the weighted matrix of the graph is periodic and irreducible in synchronous protocol. Finally, a case study on a network of robots in an automated warehouse is provided in order to demonstrate the results.

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Distributed Convex Optimization with State-Dependent (Social) Interactions and Time-Varying Topologies

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