Incremental Subgradients for Constrained Convex Optimization: A Unified Framework and New Methods
本文为非光滑凸最小化问题提出了一个统一框架，将ε-次梯度算法与凸可行性问题方法结合，用近似投影替代欧几里得投影，并应用于增量次梯度方法，得到适合大规模优化（如断层成像）的新算法。
We present a unifying framework for nonsmooth convex minimization bringing together $\epsilon$-subgradient algorithms and methods for the convex feasibility problem. This development is a natural step for $\epsilon$-subgradient methods in the direction of constrained optimization since the Euclidean projection frequently required in such methods is replaced by an approximate projection, which is often easier to compute. The developments are applied to incremental subgradient methods, resulting in new algorithms suitable to large-scale optimization problems, such as those arising in tomographic imaging.