论文标题
通过仿射约束分散的强键优化：原始方法和双重方法
Decentralized Strongly-Convex Optimization with Affine Constraints: Primal and Dual Approaches
论文作者
论文摘要
分散优化是用于分布式信号处理和传感以及隐私性和大规模机器学习的常见范式。假定多个计算实体本地拥有目标函数，并通过网络连接。代理商的目的通常是通过进行梯度更新并与直接邻居交换信息来最大程度地减少本地目标的总和。在文献中，分散优化的理论已经发达了。特别是，它包括下限和最佳算法。在本文中，我们假设每个节点都具有仿射约束。我们讨论了与仿射约束的分散优化问题的几种原始方法和双重方法。
Decentralized optimization is a common paradigm used in distributed signal processing and sensing as well as privacy-preserving and large-scale machine learning. It is assumed that several computational entities locally hold objective functions and are connected by a network. The agents aim to commonly minimize the sum of the local objectives subject by making gradient updates and exchanging information with their immediate neighbors. Theory of decentralized optimization is pretty well-developed in the literature. In particular, it includes lower bounds and optimal algorithms. In this paper, we assume that along with an objective, each node also holds affine constraints. We discuss several primal and dual approaches to decentralized optimization problem with affine constraints.
