Self-Healing First-Order Distributed Optimization
Self-Healing First-Order Distributed Optimization
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DOI：
10.1109/cdc45484.2021.9683487
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发表时间：
2021-04
期刊：
影响因子：
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通讯作者：
Israel L. Donato Ridgley;R. Freeman;K. Lynch
中科院分区：
文献类型：
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作者：
Israel L. Donato Ridgley;R. Freeman;K. Lynch
我们描述了一个参数化的家庭的一阶分布式优化算法，使网络的代理合作计算的决策变量，最大限度地减少在每个代理的成本函数的总和。这些算法是自我修复的，因为即使它们被随机初始化，代理加入或离开网络，或者本地成本函数改变，也可以保证它们收敛到正确的优化器。我们还提出了模拟证据，我们的算法是自愈的情况下，丢弃的通信数据包。我们的算法是第一个单拉普拉斯方法的分布式凸优化表现出所有这些特点。我们通过牺牲内部稳定性来实现自我修复，这是单拉普拉斯方法的基本权衡。
We describe a parameterized family of first-order distributed optimization algorithms that enable a network of agents to collaboratively calculate a decision variable that minimizes the sum of cost functions at each agent. These algorithms are self-healing in that their convergence to the correct optimizer can be guaranteed even if they are initialized randomly, agents join or leave the network, or local cost functions change. We also present simulation evidence that our algorithms are self-healing in the case of dropped communication packets. Our algorithms are the first single-Laplacian methods for distributed convex optimization to exhibit all of these characteristics. We achieve self-healing by sacrificing internal stability, a fundamental trade-off for single-Laplacian methods.