Decentralized consensus optimization and resource allocation
Decentralized consensus optimization and resource allocation
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DOI：
10.1007/978-3-319-97478-1_10
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
A. Nedić;Alexander Olshevsky;Wei Shi
中科院分区：
文献类型：
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作者：
A. Nedić;Alexander Olshevsky;Wei Shi
我们考虑的共识优化和资源分配的问题，我们讨论分散算法解决这些问题。通过“分散”，我们的意思是算法将在一组网络代理中实现，每个代理都能够与相邻代理进行通信。对于这两个问题，网络中的每个代理都希望协作最小化涉及全局信息的函数，同时只能访问部分信息。具体来说，我们将首先介绍这两个问题的背景下，分布式优化，回顾相关文献，并讨论一个有趣的“镜像关系”的问题。之后，我们将讨论一些最先进的算法来解决分散的共识优化问题，并基于“镜像关系”，我们然后开发一些算法来解决分散的资源分配问题。我们还提供了一些数值实验来证明算法的有效性，并验证了使用“镜像关系”的方法。
We consider the problems of consensus optimization and resource allocation, and we discuss decentralized algorithms for solving such problems. By “decentralized”, we mean the algorithms are to be implemented in a set of networked agents, whereby each agent is able to communicate with its neighboring agents. For both problems, every agent in the network wants to collaboratively minimize a function that involves global information, while having access to only partial information. Specifically, we will first introduce the two problems in the context of distributed optimization, review the related literature, and discuss an interesting “mirror relation” between the problems. Afterwards, we will discuss some of the state-of-the-art algorithms for solving the decentralized consensus optimization problem and, based on the “mirror relationship”, we then develop some algorithms for solving the decentralized resource allocation problem. We also provide some numerical experiments to demonstrate the efficacy of the algorithms and validate the methodology of using the “mirror relation”.