Distributed Inequality Constrained Online Optimization for Unbalanced Digraphs using Row Stochastic Property
Distributed Inequality Constrained Online Optimization for Unbalanced Digraphs using Row Stochastic Property
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DOI：
10.1109/cdc51059.2022.9993135
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发表时间：
2022-12
期刊：
影响因子：
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通讯作者：
Keishin Tada;N. Hayashi;S. Takai
中科院分区：
文献类型：
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作者：
Keishin Tada;N. Hayashi;S. Takai
在这项研究中，我们讨论了非平衡有向图上具有时变耦合约束的在线凸优化的原始-对偶分布式算法。一组智能体交换对偶优化器的估计变量和尺度变量，用于补偿信息流的不平衡。然后，每个智能体使用投影的次梯度方法更新原始变量和对偶变量。我们证实了代价函数的后悔和约束违反的累积误差达到了次线性。分布式经济调度问题的数值算例表明，在耦合不等约束条件下，各智能体的估计逼近最优策略。
In this study, we discuss a primal-dual distributed algorithm for online convex optimization with a time-varying coupled constraint on unbalanced directed graphs. A group of agents exchanges the estimation variable for the dual optimizer and the scaling variable, which are used for compensating the unbalanced information flow. Then, each agent updates the primal and dual variables using the projected subgradient methods. We confirm that the regret of the cost function and the cumulative error of the constraint violation achieve sublinearity. A numerical example of a distributed economic dispatch problem demonstrates that the estimation of each agent approaches the optimal strategy under the coupled inequality constraint.