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2010
IEEE

An approximate dual subgradient algorithm for multi-agent non-convex optimization

10 years 6 months ago
An approximate dual subgradient algorithm for multi-agent non-convex optimization
We consider a multi-agent optimization problem where agents aim to cooperatively minimize a sum of local objective functions subject to a global inequality constraint and a global state constraint set. In contrast to existing papers, we do not require that the objective, constraint functions, and state constraint sets are convex. We propose a distributed approximate dual subgradient algorithm to enable agents to asymptotically converge to a pair of approximate primaldual solutions over dynamically changing network topologies. Convergence can be guaranteed provided that the Slater's condition and strong duality property are satisfied.
Minghui Zhu, Sonia Martínez
Added 13 May 2011
Updated 13 May 2011
Type Journal
Year 2010
Where CDC
Authors Minghui Zhu, Sonia Martínez
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