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A Proximal Diffusion Strategy for Multiagent Optimization With Sparse Affine Constraints
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DOI：10.1109/TAC.2019.2960265.png)
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Abstract
En 中文
This article develops a proximal primal-dual decentralized strategy for multiagent optimization problems that involve multiple coupled affine constraints, where each constraint may involve only a subset of the agents. The constraints are generally sparse, meaning that only a small subset of the agents are involved in them. This scenario arises in many applications, including decentralized control formulations, resource allocation problems, and smart grids. Traditional decentralized solutions tend to ignore the structure of the constraints and lead to degraded performance. We instead develop a decentralized solution that exploits the sparsity structure. Under constant step-size learning, the asymptotic convergence of the proposed algorithm is established in the presence of nonsmooth terms, and it occurs at a linear rate in the smooth case. We also examine how the performance of the algorithm is influenced by the sparsity of the constraints. Simulations illustrate the superior performance of the proposed strategy.
Keywords:
Convergence
Couplings
Resource management
Predictive control
Smart grids
Cost function
Dual diffusion strategy
multiagent optimization
primal– dual methods
sparsely coupled constraints
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