论文标题
用于分布式特征的复合优化的多代理原始偶对策略
A Multi-Agent Primal-Dual Strategy for Composite Optimization over Distributed Features
论文作者
论文摘要
这项工作研究了多代理共享优化问题，目标函数是平滑局部函数的总和以及偶联所有试剂的凸（可能是非平滑）函数。这种情况在许多机器学习和工程应用中都产生，例如分布式功能和资源分配的回归。我们将这个问题重新制定为同等的鞍点问题，这是分散解决方案的。然后，我们提出了一种近端原始二重式算法，并在局部函数强烈键值时建立其线性收敛到最佳解决方案。据我们所知，这是第一个线性收敛分散算法，用于使用一般凸（可能是非平滑轴）耦合函数的多代理共享问题。
This work studies multi-agent sharing optimization problems with the objective function being the sum of smooth local functions plus a convex (possibly non-smooth) function coupling all agents. This scenario arises in many machine learning and engineering applications, such as regression over distributed features and resource allocation. We reformulate this problem into an equivalent saddle-point problem, which is amenable to decentralized solutions. We then propose a proximal primal-dual algorithm and establish its linear convergence to the optimal solution when the local functions are strongly-convex. To our knowledge, this is the first linearly convergent decentralized algorithm for multi-agent sharing problems with a general convex (possibly non-smooth) coupling function.
