技术报告：跟踪时变凸优化问题解的完全异步算法
Technical Report: A Totally Asynchronous Algorithm for Tracking Solutions to Time-Varying Convex Optimization Problems
摘要：
本文提出了一个分散的算法，一组代理跟踪时变不动点，即时变凸优化问题的解决方案。该算法是一阶的，并且它允许所有代理的通信和计算中的总延迟，即，所有这些操作可以以任意定时和任意（有限）延迟发生。收敛速度是根据代理执行的通信和计算来计算的，而不指定何时必须发生。这些速率适用于收敛到每个单独的目标函数的最小值，以及代理的长期行为，因为它们的目标函数的变化。然后，为了提高有限的通信和计算资源的使用，我们优化代理的操作相对于他们的目标函数的变化，以最大限度地减少总不动点跟踪误差随着时间的推移的时间。仿真结果来说明这些发展在实践中，经验评估其鲁棒性代理的更新法律的不确定性。
英文摘要：
This paper presents a decentralized algorithm for a team of agents to track time-varying fixed points that are the solutions to time-varying convex optimization problems. The algorithm is first-order, and it allows for total asynchrony in the communications and computations of all agents, i.e., all such operations can occur with arbitrary timing and arbitrary (finite) delays. Convergence rates are computed in terms of the communications and computations that agents execute, without specifying when they must occur. These rates apply to convergence to the minimum of each individual objective function, as well as agents' long-run behavior as their objective functions change. Then, to improve the usage of limited communication and computation resources, we optimize the timing of agents' operations relative to changes in their objective functions to minimize total fixed point tracking error over time. Simulation results are presented to illustrate these developments in practice and empirically assess their robustness to uncertainties in agents' update laws.
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。
