Graphical Convergence of Subgradients in Nonconvex Optimization and Learning

the investigation of research articles in applied linguistics: convergence and divergence in iranian elt context

چکیده ندارد.

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

Global Convergence of ADMM in Nonconvex Nonsmooth Optimization

In this paper, we analyze the convergence of the alternating direction method of multipliers (ADMM) for minimizing a nonconvex and possibly nonsmooth objective function, φ(x0, . . . , xp, y), subject to coupled linear equality constraints. Our ADMM updates each of the primal variables x0, . . . , xp, y, followed by updating the dual variable. We separate the variable y from xi’s as it has a spe...

An Effective Optimization Algorithm for Locally Nonconvex Lipschitz Functions Based on Mollifier Subgradients

We present an effective algorithm for minimization of locally nonconvex Lipschitz functions based on mollifier functions approximating the Clarke generalized gradient. To this aim, first we approximate the Clarke generalized gradient by mollifier subgradients. To construct this approximation, we use a set of averaged functions gradients. Then, we show that the convex hull of this set serves as ...

Global Convergence of Splitting Methods for Nonconvex Composite Optimization

We consider the problem of minimizing the sum of a smooth function h with a bounded Hessian, and a nonsmooth function. We assume that the latter function is a composition of a proper closed function P and a surjective linear map M, with the proximal mappings of τP , τ > 0, simple to compute. This problem is nonconvex in general and encompasses many important applications in engineering and mach...