A Unified Analysis on the Subgradient Upper Bounds for the Subgradient Methods Minimizing Composite Nonconvex, Nonsmooth and Non-Lipschitz Functions
This work provides theoretical foundations for optimization algorithms in machine learning, addressing a broad class of challenging functions, but it is incremental as it builds on existing analysis frameworks.
The paper tackles the problem of analyzing proximal subgradient methods for minimizing composite nonconvex, nonsmooth, and non-Lipschitz functions, establishing unified error-bound and growth conditions that lead to novel iteration complexity results.
This paper presents a unified analysis for the proximal subgradient method (Prox-SubGrad) type approach to minimize an overall objective of $f(x)+r(x)$, subject to convex constraints, where both $f$ and $r$ are weakly convex, nonsmooth, and non-Lipschitz. Leveraging on the properties of the Moreau envelope of weakly convex functions, we are able to relate error-bound conditions, the growth conditions of the subgradients of the objective, and the behavior of the proximal subgradient iterates on some remarkably broad classes of objective functions. Various existing as well as new bounding conditions are studied, leading to novel iteration complexity results. The terrain of our exploration expands to stochastic proximal subgradient algorithms.