论文标题
分布式事件触发的算法，用于与耦合约束的凸优化
Distributed Event-Triggered Algorithm for Convex Optimization with Coupled Constraints
论文作者
论文摘要
本文通过事件触发的机制开发了分布式的原始二重式算法，以解决一类凸出的优化问题，但受本地设置约束，耦合平等和不平等约束的约束。不同于某些现有的分布式算法的步骤尺寸减小，我们的算法使用恒定的步骤尺寸，并显示出具有$ O（1/k）$收敛速率的最佳解决方案的精确收敛，其中$ k> 0 $ k> 0 $是迭代编号。此外，通过应用事件触发的通信机制，提出的算法可以有效地降低通信成本而不牺牲收敛速度。最后，提出了一个数值示例，以验证所提出算法的有效性。
This paper develops a distributed primal-dual algorithm via event-triggered mechanism to solve a class of convex optimization problems subject to local set constraints, coupled equality and inequality constraints. Different from some existing distributed algorithms with the diminishing step-sizes, our algorithm uses the constant step-sizes, and is shown to achieve an exact convergence to an optimal solution with an $O(1/k)$ convergence rate for general convex objective functions, where $k>0$ is the iteration number. Moreover, by applying event-triggered communication mechanism, the proposed algorithm can effectively reduce the communication cost without sacrificing the convergence rate. Finally, a numerical example is presented to verify the effectiveness of the proposed algorithm.
