Distributed Resource Allocation via Accelerated Saddle Point Dynamics
| Autor: | Xinghuo Yu, Lin Wenting, Yan-Wu Wang, Chaojie Li |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Mathematical optimization
Computer science Stability (learning theory) symbols.namesake Rate of convergence Artificial Intelligence Control and Systems Engineering Slow manifold Saddle point Lagrange multiplier Convergence (routing) symbols Resource allocation Dynamical system (definition) Information Systems |
| Zdroj: | IEEE/CAA Journal of Automatica Sinica. 8:1588-1599 |
| ISSN: | 2329-9274 2329-9266 |
| Popis: | In this paper, accelerated saddle point dynamics is proposed for distributed resource allocation over a multi-agent network, which enables a hyper-exponential convergence rate. Specifically, an inertial fast-slow dynamical system with vanishing damping is introduced, based on which the distributed saddle point algorithm is designed. The dual variables are updated in two time scales, i.e., the fast manifold and the slow manifold. In the fast manifold, the consensus of the Lagrangian multipliers and the tracking of the constraints are pursued by the consensus protocol. In the slow manifold, the updating of the Lagrangian multipliers is accelerated by inertial terms. Hyper-exponential stability is defined to characterize a faster convergence of our proposed algorithm in comparison with conventional primal-dual algorithms for distributed resource allocation. The simulation of the application in the energy dispatch problem verifies the result, which demonstrates the fast convergence of the proposed saddle point dynamics. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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