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

abstract

• 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.

authors

• Huang, Tingwen

published proceedings

• NEUROCOMPUTING

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

publisher

• Elsevier  Publisher

published in

• Neurocomputing  Journal

keywords

• Distributed Multi-agent Optimization
• Distributed Primal-dual Algorithm
• Inequality Constraint
• Random Projection

Digital Object Identifier (DOI)

• 10.1016/j.neucom.2016.02.064

start page

• 195

end page

• 204

volume

• 197

URL

• http%3A%2F%2Fdx.doi.org%2F10.1016%2Fj.neucom.2016.02.064

user-defined tag

• 10 Reduced Inequalities