论文标题
评论有效的单数值阈值计算
Comments on Efficient Singular Value Thresholding Computation
论文作者
论文摘要
我们讨论如何评估凸的近端运算符和核定常的增加功能，这在几种一阶优化算法（例如（加速）近端梯度下降和ADMM等几种一阶优化算法中构成了关键计算步骤。该问题的各种特殊情况在低级别矩阵完成，深度学习中的辍学培训和高阶低量张量恢复中出现，尽管它们都逐案解决。我们提供了解决此问题的统一且有效的计算程序。
We discuss how to evaluate the proximal operator of a convex and increasing function of a nuclear norm, which forms the key computational step in several first-order optimization algorithms such as (accelerated) proximal gradient descent and ADMM. Various special cases of the problem arise in low-rank matrix completion, dropout training in deep learning and high-order low-rank tensor recovery, although they have all been solved on a case-by-case basis. We provide an unified and efficiently computable procedure for solving this problem.
