In this paper we consider the difference-of-convex (dc) programming problems, whose objective function is the difference of two convex functions. The classical dcalgorithm (dcA) is well-known for solving this kind of...
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In this paper we consider the difference-of-convex (dc) programming problems, whose objective function is the difference of two convex functions. The classical dcalgorithm (dcA) is well-known for solving this kind of problems, which generally returns a critical point. Recently, an inertialdcalgorithm (IndcA) equipped with heavy-ball inertial-force procedure was proposed in de Oliveira et al. (Set-Valued Variat Anal 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. the Kurdyka-Lojasiewicz (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.
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