Distributed multi-agent optimization with inequality constraints and random projections Academic Article uri icon


  • 2016 Elsevier B.V. In this paper, we consider a multi-agent convex optimization problem whose goal is to minimize a global convex objective function that is the sum of local convex objective functions, subject to global convex inequality constraints and several randomly occurring local convex state constraint sets. A distributed primal-dual random projection subgradient (DPDRPS) algorithm with diminishing stepsize using local communications and computations is proposed to solve such a problem. By employing iterative inequality techniques, the proposed DPDRPS algorithm is proved to be convergent almost surely. Finally, a numerical example is illustrated to show the effectiveness of the theoretical analysis.

published proceedings


author list (cited authors)

  • Zhou, B. o., Liao, X., Huang, T., Wang, H., & Chen, G.

citation count

  • 12

complete list of authors

  • Zhou, Bo||Liao, Xiaofeng||Huang, Tingwen||Wang, Huiwei||Chen, Guo

publication date

  • July 2016