Distributed Resource Allocation via ADMM over Digraphs

W. Jiang, M. Doostmohammadian, T. Charalambous

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

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In this paper, we solve the resource allocation problem over a network of agents, with edges as communication links that can be unidirectional. The goal is to minimize the sum of allocation cost functions subject to a coupling constraint in a distributed way by using the finite-time consensus-based alternating direction method of multipliers (ADMM) technique. In contrast to the existing gradient descent (GD) based distributed algorithms, our approach can be applied to non-differentiable cost functions. Also, the proposed algorithm is initialization-free and converges at a rate of $\mathcal{O}\left( {1/k} \right)$, where k is the optimization iteration counter. The fast convergence performance related to iteration counter k compared to state-of-the-art GD based algorithms is shown via a simulation example.
Original languageEnglish
Title of host publication2022 IEEE 61st Conference on Decision and Control (CDC)
Number of pages7
ISBN (Electronic)978-1-6654-6761-2
Publication statusPublished - 10 Jan 2023
MoE publication typeA4 Article in a conference publication
EventIEEE Conference on Decision and Control - Cancun, Mexico
Duration: 6 Dec 20229 Dec 2022
Conference number: 61


ConferenceIEEE Conference on Decision and Control
Abbreviated titleCDC


  • Couplings
  • Directed graphs
  • Linear programming
  • Cost function
  • Convex functions
  • Large-scale systems
  • Resource management
  • Distributed optimization
  • ADMM
  • resource allocation
  • finite-time consensus
  • digraphs


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