A Refined Inertial DC Algorithm for DC Programming
This work addresses optimization in DC programming, an incremental improvement for researchers in mathematical optimization and applied fields like image processing.
The paper tackles DC programming problems by proposing a refined inertial DC algorithm (RInDCA) with enlarged inertial step-size, which empirically accelerates convergence and is shown to converge to critical points, with numerical simulations on matrix copositivity and image denoising demonstrating benefits.
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-Łojasiewicz (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.