Composite optimization models via proximal gradient method with increasing adaptive stepsizes

We first consider the convex composite optimization models with locally Lipschitz condition imposed on the gradient of the differentiable term. The classical method which is proximal gradient will be studied with our new strategy of stepsize selection. Our proposed stepsize can be computed conveniently by explicit forms. The sequence of our stepsizes is proved to … 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 limitation. This property plays an important role in improving the speed of the algorithm. … Read more