论文标题
通过非平滑方法进行约束分布式优化的指数收敛算法设计
Exponentially Convergent Algorithm Design for Constrained Distributed Optimization via Non-smooth Approach
论文作者
论文摘要
我们考虑以分布式方式最小化具有集合约束的非平滑目标函数的总和。至于这个问题，我们首次提出了一种具有指数收敛速率的分布式算法。通过确切的惩罚方法，我们将问题等效地重新分配为分布，而没有共识限制。然后，我们借助差分包含，设计了分布式的投影亚级别算法。此外，我们表明该算法将其收敛到最佳解决方案，以指数为强凸目标函数。
We consider minimizing a sum of non-smooth objective functions with set constraints in a distributed manner. As to this problem, we propose a distributed algorithm with an exponential convergence rate for the first time. By the exact penalty method, we reformulate the problem equivalently as a standard distributed one without consensus constraints. Then we design a distributed projected subgradient algorithm with the help of differential inclusions. Furthermore, we show that the algorithm converges to the optimal solution exponentially for strongly convex objective functions.
