Bilevel Distributed Optimization in Directed Networks
Bilevel Distributed Optimization in Directed Networks
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DOI：
10.23919/acc50511.2021.9483429
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发表时间：
2020-06
期刊：
影响因子：
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通讯作者：
Farzad Yousefian
中科院分区：
文献类型：
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作者：
Farzad Yousefian
在无线传感器网络和大规模数据处理的新兴应用的动机，我们认为分布式优化有向网络中的代理本地通信他们的信息，他们的邻居，以合作最小化的全球成本函数。我们引入了一个新的统一的分布式约束优化模型，其特征在于作为一个双层优化问题。该模型涵盖了有向网络上广泛的现有问题，包括：（i）线性约束的分布式优化;（ii）有向网络上的分布式无约束非强凸优化。采用一种新的正则化松弛方法和梯度跟踪计划，我们开发了一个迭代正则化推挽梯度算法。我们建立了共识，并推导出新的收敛速度报表的次优性和不可行的生成的迭代求解双层模型。所提出的算法和在这项工作中获得的复杂性分析似乎是新的解决双层模型，也为两个子类的问题。所提出的算法的数值性能。
Motivated by emerging applications in wireless sensor networks and large-scale data processing, we consider distributed optimization over directed networks where the agents communicate their information locally to their neighbors to cooperatively minimize a global cost function. We introduce a new unifying distributed constrained optimization model that is characterized as a bilevel optimization problem. This model captures a wide range of existing problems over directed networks including: (i) Distributed optimization with linear constraints; (ii) Distributed unconstrained nonstrongly convex optimization over directed networks. Employing a novel regularization-based relaxation approach and gradient-tracking schemes, we develop an iteratively regularized push-pull gradient algorithm. We establish the consensus and derive new convergence rate statements for suboptimality and infeasibility of the generated iterates for solving the bilevel model. The proposed algorithm and the complexity analysis obtained in this work appear to be new for addressing the bilevel model and also for the two sub-classes of problems. The numerical performance of the proposed algorithm is presented.