Analysis of generalized Bregman surrogate algorithms for nonsmooth nonconvex statistical learning
非光滑非凸统计学习中的广义 Bregman 替代算法分析
Modern statistical applications often involve minimizing an objective function that may be nonsmooth and/or nonconvex. This paper focuses on a broad Bregman-surrogate algorithm framework including the local linear approximation, mirror descent, iterative thresholding, DC programming and many others as particular instances. The recharacterization via generalized Bregman functions enables us to construct suitable error measures and establish global convergence rates for…