Composite optimization models via proximal gradient method with a novel enhanced adaptive stepsize

We consider the {\it composite optimization problems} under convex and nonconvex settings. For the convex case, the {\it locally Lipschitz} condition is imposed on the gradient of the differentiable convex term. The classical {\it proximal gradient method} will be studied with our novel {\it enhanced adaptive} stepsize selection. To obtain the convergence of the proposed … Read more

A new proximal gradient algorithm for solving mixed variational inequality problems with a novel explicit stepsize and applications

In this paper, we propose a new algorithm for solving monotone mixed variational inequality problems in real Hilbert spaces based on proximal gradient method. Our new algorithm uses a novel explicit stepsize which is proved to be increasing to a positive value. This property plays an important role in improving the speed of the algorithm. … Read more