NDLI: An Approximate Dual Subgradient Algorithm for Multi-Agent Non-Convex Optimization

Content Provider	IEEE Xplore Digital Library
Author	Minghui Zhu Martinez, S.
Copyright Year	1963
Abstract	We consider a multi-agent optimization problem where agents subject to local, intermittent interactions aim to minimize a sum of local objective functions subject to a global inequality constraint and a global state constraint set. In contrast to previous work, we do not require that the objective, constraint functions, and state constraint sets are convex. In order to deal with time-varying network topologies satisfying a standard connectivity assumption, we resort to consensus algorithm techniques and the Lagrangian duality method. We slightly relax the requirement of exact consensus, and propose a distributed approximate dual subgradient algorithm to enable agents to asymptotically converge to a pair of primal-dual solutions to an approximate problem. To guarantee convergence, we assume that the Slater's condition is satisfied and the optimal solution set of the dual limit is singleton. We implement our algorithm over a source localization problem and compare the performance with existing algorithms.
Sponsorship	IEEE Control Systems Society
Starting Page	1534
Ending Page	1539
Page Count	6
File Size	1443136
File Format	PDF
ISSN	00189286
Volume Number	58
Issue Number	6
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-06-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Approximation algorithms Optimization Vectors Convergence Linear programming Algorithm design and analysis Approximation methods Lagrangian duality Dual subgradient algorithm
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Electrical and Electronic Engineering Computer Science Applications

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