On distributed convex optimization under inequality and equality constraints

Minghui Zhu, Sonia Martinez

Research output: Contribution to journalArticlepeer-review

440 Scopus citations

Abstract

We consider a general multi-agent convex optimization problem where the agents are to collectively minimize a global objective function subject to a global inequality constraint, a global equality constraint, and a global constraint set. The objective function is defined by a sum of local objective functions, while the global constraint set is produced by the intersection of local constraint sets. In particular, we study two cases: one where the equality constraint is absent, and the other where the local constraint sets are identical. We devise two distributed primal-dual subgradient algorithms based on the characterization of the primal-dual optimal solutions as the saddle points of the Lagrangian and penalty functions. These algorithms can be implemented over networks with dynamically changing topologies but satisfying a standard connectivity property, and allow the agents to asymptotically agree on optimal solutions and optimal values of the optimization problem under the Slater's condition.

Original languageEnglish (US)
Article number6018253
Pages (from-to)151-164
Number of pages14
JournalIEEE Transactions on Automatic Control
Volume57
Issue number1
DOIs
StatePublished - Jan 2012

All Science Journal Classification (ASJC) codes

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

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