Sonia Martínez
Jacobs Faculty Scholar
Professor of Mechanical and Aerospace Engineering
An approximate dual subgradient algorithm for multi-agent non-convex optimization
Minghui Zhu, and Sonia MartÃnez
Proceedings of the IEEE Int. Conference on Decision and Control, Atlanta, GA, December 2010
Abstract:
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 primal-dual solutions over dynamically changing network topologies. Convergence can be guaranteed provided that the Slater's condition and strong duality property are satisfied.
File: main.pdf
Bib-tex entry:
@InProceedings{MZ-SM:10-cdc,
author = {M. Zhu and S. Mart{\'\i}nez},
booktitle = {IEEE International Conference on Decision and Control},
title = {An approximate dual subgradient algorithm for multi-agent non-convex optimization},
pages = {7487--7492},
year = {2010},
month = {December},
address = {Atlanta, GA, USA}
}