An inexact modified subgradient algorithm for nonconvex optimization

An effective optimization algorithm for locally nonconvex Lipschitz functions based on mollifier subgradients

A Proximal Bundle Method for Nonconvex Functions with Inexact Oracles

For a class of nonconvex nonsmooth functions, we consider the problem of computing an approximate critical point, in the case of inexact oracles. The latter means that only an inexact function value and an inexact subgradient are available, at any given point. We assume that the errors in function and subgradient evaluations are merely bounded, and in principle need not vanish in the limit. We ...

A proximal bundle method for nonsmooth nonconvex functions with inexact information

For a class of nonconvex nonsmooth functions, we consider the problem of computing an approximate critical point, in the case when only inexact information about the function and subgradient values is available. We assume that the errors in function and subgradient evaluations are merely bounded, and in principle need not vanish in the limit. We examine the redistributed proximal bundle approac...

Inexactness in Bundle Methods for Locally Lipschitz Functions

We consider the problem of computing a critical point of a nonconvex locally Lipschitz function over a convex compact constraint set given an inexact oracle that provides an approximate function value and an approximate subgradient. We assume that the errors in function and subgradient evaluations are merely bounded, and in particular need not vanish in the limit. After some discussion on how t...

Proximally Guided Stochastic Subgradient Method for Nonsmooth, Nonconvex Problems

In this paper, we introduce a stochastic projected subgradient method for weakly convex (i.e., uniformly prox-regular) nonsmooth, nonconvex functions—a wide class of functions which includes the additive and convex composite classes. At a high-level, the method is an inexact proximal point iteration in which the strongly convex proximal subproblems are quickly solved with a specialized stochast...