Fast gradient descent method for convex optimization problems with an oracle that generates a $(\delta ,L)$-model of a function in a requested point

In this article we propose a new concept of a $(\delta ,L)$-model of a function which generalizes the concept of the $(\delta ,L)$-oracle (Devolder-Glineur-Nesterov). Using this concept we describe the gradient descent method and the fast gradient descent method and we show that many popular methods (composite optimization methods, level methods, conjugate gradient methods, proximal methods) are special cases of proposed methods in this article.