  In this paper we consider the difference-of-convex (DC) programming problems,
whose objective function is the difference of two convex functions. The
classical DC Algorithm (DCA) is well-known for solving this kind of problems,
which generally returns a critical point. Recently, an inertial DC algorithm
(InDCA) equipped with heavy-ball inertial-force procedure was proposed in de
Oliveira et al. (Set-Valued and Variational Analysis 27(4):895--919, 2019),
which potentially helps to improve both the convergence speed and the solution
quality. Based on InDCA, we propose a refined inertial DC algorithm (RInDCA)
equipped with enlarged inertial step-size compared with InDCA. Empirically,
larger step-size accelerates the convergence. We demonstrate the subsequential
convergence of our refined version to a critical point. In addition, by
assuming the Kurdyka-{\L}ojasiewicz (KL) property of the objective function, we
establish the sequential convergence of RInDCA. Numerical simulations on
checking copositivity of matrices and image denoising problem show the benefit
of larger step-size.
