Distributed Adaptive Optimization with Weight-Balancing

Dongdong Yue, S. Baldi, Jinde Cao, Bart De Schutter

Research output: Contribution to journalArticleScientificpeer-review

1 Citation (Scopus)


This paper addresses the continuous-time distributed optimization of a strictly convex summation-separable cost function with possibly non-convex local functions over strongly connected digraphs. Distributed optimization methods in the literature require convexity of local functions, or balanced weights, or vanishing step sizes, or algebraic information (eigenvalues or eigenvectors) of the Laplacian matrix. The solution proposed here covers both weight-balanced and unbalanced digraphs in a unified way, without any of the aforementioned requirements.

Original languageEnglish
Number of pages8
JournalIEEE Transactions on Automatic Control
Publication statusAccepted/In press - 8 Apr 2021


  • Cost function
  • Couplings
  • directed graphs
  • Distributed optimization
  • Eigenvalues and eigenfunctions
  • Laplace equations
  • multi-agent systems
  • Optimization
  • Radio frequency
  • Standards
  • weight balancing


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