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A Proximal Diffusion Strategy for Multi-Agent Optimization with Sparse Affine Constraints. (arXiv:1810.02124v1 [math.OC])
来源于:arXiv
This work develops a proximal primal-dual distributed strategy for
multi-agent optimization problems that involve multiple coupled affine
constraints, and 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 distributed control formulations, resource allocation problems, and
smart grids. Traditional distributed solutions tend to ignore the structure of
the constraints and lead to degraded performance. We instead develop a
distributed solution that exploits the sparsity structure. Under constant
step-size learning, we establish the asymptotic convergence of the distributed
algorithm in the presence of non-smooth terms, and further show that
convergence 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 t 查看全文>>