分布式半随机优化与量化细化
Distributed Semi-Stochastic Optimization with Quantization Refinement
AI总结：
本文提出一种分布式优化算法，通过迭代细化量化限制信息量，用于解决通信受限设备的正则化回归问题，并理论分析其线性收敛性。
摘要：
我们考虑在通信受限设备网络中正则化回归问题。每个节点有本地数据和目标，目标是使节点优化全局目标。我们开发了一种基于最近半随机近端梯度方法的分布式优化算法。我们的算法采用迭代细化量化来限制信息量。我们给出了算法实现线性收敛速率的理论分析和条件。最后，我们通过数值模拟展示了算法的性能。
英文摘要：
We consider the problem of regularized regression in a network of communication-constrained devices. Each node has local data and objectives, and the goal is for the nodes to optimize a global objective. We develop a distributed optimization algorithm that is based on recent work on semi-stochastic proximal gradient methods. Our algorithm employs iteratively refined quantization to limit message size. We present theoretical analysis and conditions for the algorithm to achieve a linear convergence rate. Finally, we demonstrate the performance of our algorithm through numerical simulations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
