Newton-Raphson consensus for distributed convex optimization
Newton-Raphson consensus for distributed convex optimization
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DOI：
10.1109/cdc.2011.6160605
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发表时间：
2011-12
期刊：
影响因子：
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通讯作者：
F. Zanella;Damiano Varagnolo;A. Cenedese;G. Pillonetto;L. Schenato
中科院分区：
文献类型：
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作者：
F. Zanella;Damiano Varagnolo;A. Cenedese;G. Pillonetto;L. Schenato
我们研究的多智能体系统的情况下，有限的通信连接的无约束分布式优化问题。特别是，我们专注于凸成本函数之和的最小化，其中全局函数的每个分量仅可用于特定代理，因此可以被视为私人局部成本。代理人需要合作，以计算所有成本之和的最小化。我们提出了一个类似共识的策略，估计在每个代理的全局极小的局部估计的牛顿-拉夫森下降更新。特别是，该算法是基于分离的时间尺度的原则，它被证明是收敛到全局最小，如果一个特定的参数，调整收敛速度被选择足够小。我们还提供了数值模拟，并将它们与其他分布式优化策略进行比较，如交替方向乘法和分布式次梯度法。
We study the problem of unconstrained distributed optimization in the context of multi-agents systems subject to limited communication connectivity. In particular we focus on the minimization of a sum of convex cost functions, where each component of the global function is available only to a specific agent and can thus be seen as a private local cost. The agents need to cooperate to compute the minimizer of the sum of all costs. We propose a consensus-like strategy to estimate a Newton-Raphson descending update for the local estimates of the global minimizer at each agent. In particular, the algorithm is based on the separation of time-scales principle and it is proved to converge to the global minimizer if a specific parameter that tunes the rate of convergence is chosen sufficiently small. We also provide numerical simulations and compare them with alternative distributed optimization strategies like the Alternating Direction Method of Multipliers and the Distributed Subgradient Method.