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Cited by 444Distributed Control Multi-Agent SystemsSparse and Compressive Sensing TechniquesOptimization and Variational Analysis

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Multi-Agent Distributed Optimization via Inexact Consensus ADMM

Open access
Chang, Tsung-Hui, Hong, Mingyi, Wang, Xiangfeng·2015·IEEE Transactions on Signal Processing
DOI 10.1109/tsp.2014.2367458
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Abstract

Multi-agent distributed consensus optimization problems arise in many signal processing applications. Recently, the alternating direction method of multipliers (ADMM) has been used for solving this family of problems. ADMM based distributed optimization method is shown to have faster convergence rate compared with classic methods based on consensus subgradient, but can be computationally expensive, especially for problems with complicated structures or large dimensions. In this paper, we propose low-complexity algorithms that can reduce the overall computational cost of consensus ADMM by an order of magnitude for certain large-scale problems. Central to the proposed algorithms is the use of an inexact step for each ADMM update, which enables the agents to perform cheap computation at each iteration. Our convergence analyses show that the proposed methods converge well under some convexity assumptions. Numerical results show that the proposed algorithms offer considerably lower computational complexity than the standard ADMM based distributed optimization methods.

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  • Augmented Lagrangian based primal-dual methods have linear convergence under strong convexity and Lipschitz gradient assumptions or under the positive-definite bounded Hessian assumption.

    “The augmented Lagrangian based primal-dual methods have linear convergence under strong convexity and Lipschitz gradient assumptions [4,30] or under the positive-definite bounded Hessian assumption [12,13].”

    From EXTRA: An Exact First-Order Algorithm for Decentralized Consensus Optimization (2015) · cites this paper as [4]

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Metadata

Authors
Chang, T.-H., Hong, M., & Wang, X.
Affiliations
  • National Taiwan University of Science and Technology (TW)
  • Iowa State University (US)
  • Shanghai Key Laboratory of Trustworthy Computing (CN)
ORCID
Venue
IEEE Transactions on Signal Processing
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2015-01
Volume
63
Issue
2
Pages
482-497
ISSN
  • 1053-587X (ISSN-L)
  • 1941-0476
DOI
10.1109/tsp.2014.2367458
OpenAlex
W2092620240
Cited by
444
References
2
Citations by year
2026: 25 · 2025: 34 · 2024: 43 · 2023: 41 · 2022: 36 · 2021: 51 · 2020: 54 · 2019: 43 · 2018: 46 · 2017: 27 · 2016: 27 · 2015: 13
FWCI
27.20
Type
journal article
Language
en
Open access
Licence
ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
Locations
  • IEEE Transactions on Signal Processing (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • Iowa State University Digital Repository (Iowa State University) (submittedVersion)
  • arXiv (Cornell University)
Funders
National Science Council (NSC-102-2221-E-011-005-MY3)
Topics
  • Physical Sciences › Engineering › Control and Systems Engineering › Distributed Control Multi-Agent Systems
  • Physical Sciences › Computer Science › Signal Processing › Sparse and Compressive Sensing Techniques
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › Optimization and Variational Analysis
Keywords
consensus optimization, alternating direction method of multipliers, low-complexity algorithms, convergence analysis, convex optimization, large-scale optimization, computational complexity reduction

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