In this paper, we introduce variational networks (VNs) for image reconstruction. VNs are fully learned models based on the framework of incremental proximal gradient methods. They provide a natural transition between classical variational methods and state-of-the-art residual neural networks. Due to their incremental nature, VNs are very efficient, but only approximately minimize the underlying...

This paper is devoted to the spectral theory (more precisely, tothe variational theory of the spectrum) of guided waves in anelastic half cylinder.  We use variational methods to investigateseveral aspects of propagating waves, including localization (seeFigure 1), existence criteria and the formulas to find them. Weapproach the problem using two complementary methods: Thevariational methods fo...

a numerical method for solving variational problems is presented in this paper. the method is based upon hybrid of hartley functions approximations. the properties of hybrid functions which are the combinations of block-pulse functions and hartley functions are first presented. the operational matrix of integration is then utilized to reduce the variational problems to the solution of algebraic...