论文标题
对近端平滑集的随机优化
Stochastic optimization over proximally smooth sets
论文作者
论文摘要
我们引入了一类随机算法，以最大程度地减少近近光滑集的弱凸功能。作为其主要构建块，算法使用目标函数的简化模型和约束集，以及缩回操作以恢复可行性。所有提出的方法都配备了有限的时间效率保证，就自然等式措施而言。我们讨论了对平滑歧管上非平滑优化的后果，并通过弱征不平等削减的过度集合。
We introduce a class of stochastic algorithms for minimizing weakly convex functions over proximally smooth sets. As their main building blocks, the algorithms use simplified models of the objective function and the constraint set, along with a retraction operation to restore feasibility. All the proposed methods come equipped with a finite time efficiency guarantee in terms of a natural stationarity measure. We discuss consequences for nonsmooth optimization over smooth manifolds and over sets cut out by weakly-convex inequalities.
