Distributed Resource Allocation Over Dynamic Networks With Uncertainty

Thinh T. Doan, Carolyn L. Beck

Research output: Contribution to journalArticlepeer-review


Motivated by broad applications in various fields of engineering, we study a network resource allocation problem where the goal is to optimally allocate a fixed quantity of the resources over a network of nodes. We consider large scale networks with complex interconnection structures, thus any solution must be implemented in parallel and based only on local data resulting in a need for distributed algorithms. In this article, we study a distributed Lagrangian method for such problems. By utilizing the so-called distributed subgradient methods to solve the dual problem, our approach eliminates the need for central coordination in updating the dual variables, which is often required in classic Lagrangian methods. Our focus is to understand the performance of this distributed algorithm when the number of resources is unknown and may be time-varying. In particular, we obtain an upper bound on the convergence rate of the algorithm to the optimal value, in expectation, as a function of network topology. The effectiveness of the proposed method is demonstrated by its application to the economic dispatch problem in power systems, with simulations completed on the benchmark IEEE-14 and IEEE-118 bus test systems.

Original languageEnglish (US)
Article number9
Pages (from-to)4378-4384
Number of pages7
JournalIEEE Transactions on Automatic Control
Issue number9
StatePublished - 2021
Externally publishedYes


  • Distributed algorithms
  • distributed optimization
  • multi-agent systems

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Control and Systems Engineering
  • Computer Science Applications


Dive into the research topics of 'Distributed Resource Allocation Over Dynamic Networks With Uncertainty'. Together they form a unique fingerprint.

Cite this