论文标题
用于约束凸优化问题的原始双重亚级别方法
Primal-dual subgradient method for constrained convex optimization problems
论文作者
论文摘要
本文考虑了一般凸的限制问题设置，其中不假定函数是可区分的，也不是Lipschitz的连续。我们的动机是找到一种简单的一阶方法，用于解决最小要求的广泛凸优化问题。我们在这种情况下研究了加权双平均值的方法（Nesterov，2009年），并证明它是一种最佳方法。
This paper considers a general convex constrained problem setting where functions are not assumed to be differentiable nor Lipschitz continuous. Our motivation is in finding a simple first-order method for solving a wide range of convex optimization problems with minimal requirements. We study the method of weighted dual averages (Nesterov, 2009) in this setting and prove that it is an optimal method.
